social media data thesis

Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

• Published: 02 April 2019
• Volume 51 , pages 1766–1781, ( 2019 )

Cite this article

• Matthew Andreotta 1 , 2 ,
• Robertus Nugroho 2 , 3 ,
• Mark J. Hurlstone 1 ,
• Fabio Boschetti 4 ,
• Simon Farrell 1 ,
• Iain Walker 5 &
• Cecile Paris 2  

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To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

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Introduction

Social scientists use qualitative modes of inquiry to explore the detailed descriptions of the world that people see and experience (Pistrang & Barker, 2012 ). To collect the voices of people, researchers can elicit textual descriptions of the world through interview or survey methodologies. However, with the popularity of the Internet and social media technologies, new avenues for data collection are possible. Social media platforms allow users to create content (e.g., Weinberg & Pehlivan, 2011 ), and interact with other users (e.g., Correa, Hinsley, & de Zùñiga, 2011 ; Kietzmann, Hermkens, McCarthy, & Silvestre, 2010 ), in settings where “Anyone can say Anything about Any topic” ( AAA slogan , Allemang & Hendler, 2011 , pg. 6). Combined with the high rate of content production, social media platforms can offer researchers massive and diverse dynamic data sets (Yin & Kaynak, 2015 ; Gudivada et al., 2015 ). With technologies increasingly capable of harvesting, storing, processing, and analyzing this data, researchers can now explore data sets that would be infeasible to collect through more traditional qualitative methods.

Many social media platforms can be considered as textual corpora, willingly and spontaneously authored by millions of users. Researchers can compile a corpus using automated tools and conduct qualitative inquiries of content or focused analyses on specific users (Marwick, 2014 ). In this paper, we outline some of the opportunities and challenges of applying qualitative textual analyses to the big data of social media. Specifically, we present a conceptual and pragmatic justification for combining qualitative textual analyses with data science text-mining tools. This process allows us to both embrace and cope with the volume and diversity of commentary over social media. We then demonstrate this approach in a case study investigating Australian commentary on climate change, using content from the social media platform: Twitter.

Opportunities and challenges for qualitative researchers using social media data

Through social media, qualitative researchers gain access to a massive and diverse range of individuals, and the content they generate. Researchers can identify voices which may not be otherwise heard through more traditional approaches, such as semi-structured interviews and Internet surveys with open-ended questions. This can be done through diagnostic queries to capture the activity of specific peoples, places, events, times, or topics. Diagnostic queries may specify geotagged content, the time of content creation, textual content of user activity, and the online profile of users. For example, Freelon et al., ( 2018 ) identified the Twitter activity of three separate communities (‘Black Twitter’, ‘Asian-American Twitter’, ‘Feminist Twitter’) through the use of hashtags Footnote 1 in tweets from 2015 to 2016. A similar process can be used to capture specific events or moments (Procter et al., 2013 ; Denef et al., 2013 ), places (Lewis et al., 2013 ), and specific topics (Hoppe, 2009 ; Sharma et al., 2017 ).

Collecting social media data may be more scalable than traditional approaches. Once equipped with the resources to access and process data, researchers can potentially scale data harvesting without expending a great deal of resources. This differs from interviews and surveys, where collecting data can require an effortful and time-consuming contribution from participants and researchers.

Social media analyses may also be more ecologically valid than traditional approaches. Unlike approaches where responses from participants are elicited in artificial social contexts (e.g., Internet surveys, laboratory-based interviews), social media data emerges from real-world social environments encompassing a large and diverse range of people, without any prompting from researchers. Thus, in comparison with traditional methodologies (Onwuegbuzie and Leech, 2007 ; Lietz & Zayas, 2010 ; McKechnie, 2008 ), participant behavior is relatively unconstrained if not entirely unconstrained, by the behaviors of researchers.

These opportunities also come up with challenges, because of the following attributes (Parker et al., 2011 ). Firstly, social media can be interactive : its content involves the interactions of users with other users (e.g., conversations), or even external websites (e.g., links to news websites). The ill-defined boundaries of user interaction have implications for determining the units of analysis of qualitative study. For example, conversations can be lengthy, with multiple users, without a clear structure or end-point. Interactivity thus blurs the boundaries between users, their content, and external content (Herring, 2009 ; Parker et al., 2011 ). Secondly, content can be ephemeral and dynamic . The users and content of their postings are transient (Parker et al., 2011 ; Boyd & Crawford, 2012 ; Weinberg & Pehlivan, 2011 ). This feature arises from the diversity of users, the dynamic socio-cultural context surrounding platform use, and the freedom users have to create, distribute, display, and dispose of their content (Marwick & Boyd, 2011 ). Lastly, social media content is massive in volume . The accumulated postings of users can lead to a large amount of data, and due to the diverse and dynamic content, postings may be largely unrelated and accumulate over a short period of time. Researchers hoping to harness the opportunities of social media data sets must therefore develop strategies for coping with these challenges.

A framework integrating computational and qualitative text analyses

Our framework—a mixed-method approach blending the capabilities of data science techniques with the capacities of qualitative analysis—is shown in Fig.  1 . We overcome the challenges of social media data by automating some aspects of the data collection and consolidation, so that the qualitative researcher is left with a manageable volume of data to synthesize and interpret. Broadly, our framework consists of the following four phases: (1) harvest social media data and compile a corpus, (2) use data science techniques to compress the corpus along a dimension of relevance, (3) extract a subset of data from the most relevant spaces of the corpus, and (4) perform a qualitative analysis on this subset of data.

Schematic overview of the four-phased framework

Phase 1: Harvest social media data and compile a corpus

Researchers can use automated tools to query records of social media data, extract this data, and compile it into a corpus. Researchers may query for content posted in a particular time frame (Procter et al., 2013 ), content containing specified terms (Sharma et al., 2017 ), content posted by users meeting particular characteristics (Denef et al., 2013 ; Lewis et al., 2013 ), and content pertaining to a specified location (Hoppe, 2009 ).

Phase 2: Use data science techniques to compress the corpus along a dimension of relevance

Although researchers may be interested in examining the entire data set, it is often more practical to focus on a subsample of data (McKenna et al., 2017 ). Specifically, we advocate dividing the corpus along a dimension of relevance, and sampling from spaces that are more likely to be useful for addressing the research questions under consideration. By relevance, we refer to an attribute of content that is both useful for addressing the research questions and usable for the planned qualitative analysis.

To organize the corpus along a dimension of relevance , researchers can use automated, computational algorithms. This process provides both formal and informal advantages for the subsequent qualitative analysis. Formally, algorithms can assist researchers in privileging an aspect of the corpus most relevant for the current inquiry. For example, topic modeling clusters massive content into semantic topics—a process that would be infeasible using human coders alone. A plethora of techniques exist for separating social media corpora on the basis of useful aspects, such as sentiment (e.g., Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2010 ; Paris, Christensen, Batterham, & O’Dea, 2015 ; Pak & Paroubek, 2011 ) and influence (Weng et al., 2010 ).

Algorithms also produce an informal advantage for qualitative analysis. As mentioned, it is often infeasible for analysts to explore large data sets using qualitative techniques. Computational models of content can allow researchers to consider meaning at a corpus-level when interpreting individual datum or relationships between a subset of data. For example, in an inspection of 2.6 million tweets, Procter et al., ( 2013 ) used the output of an information flow analysis to derive rudimentary codes for inspecting individual tweets. Thus, algorithmic output can form a meaningful scaffold for qualitative analysis by providing analysts with summaries of potentially disjunct and multifaceted data (due to interactive, ephemeral, dynamic attributes of social media).

Phase 3: Extract a subset of data from the most relevant spaces of the corpus

Once the corpus is organized on the basis of relevance, researchers can extract data most relevant for answering their research questions. Researchers can extract a manageable amount of content to qualitatively analyze. For example, if the most relevant space of the corpus is too large for qualitative analysis, the researcher may choose to randomly sample from that space. If the most relevant space is small, the researcher may revisit Phase 2 and adopt a more lenient criteria of relevance.

Phase 4: Perform a qualitative analysis on this subset of data

The final phase involves performing the qualitative analysis to address the research question. As discussed above, researchers may draw on the computational models as a preliminary guide to the data.

Contextualizing the framework within previous qualitative social media studies

The proposed framework generalizes a number of previous approaches (Collins and Nerlich, 2015 ; McKenna et al., 2017 ) and individual studies (e.g., Lewis et al., 2013 ; Newman, 2016 ), in particular that of Marwick ( 2014 ). In Marwick’s general description of qualitative analysis of social media textual corpora, researchers: (1) harvest and compile a corpus, (2) extract a subset of the corpus, and (3) perform a qualitative analysis on the subset. As shown in Fig.  1 , our framework differs in that we introduce formal considerations of relevance, and the use of quantitative techniques to inform the extraction of a subset of data. Although researchers sometimes identify a subset of data most relevant to answering their research question, they seldom deploy data science techniques to identify it. Instead, researchers typically depend on more crude measures to isolate relevant data. For example, researchers have used the number of repostings of user content to quantify influence and recognition (e.g., Newman, 2016 ).

The steps in the framework may not be obvious without a concrete example. Next, we demonstrate our framework by applying it to Australian commentary regarding climate change on Twitter.

Application Example: Australian Commentary regarding Climate Change on Twitter

Social media platform of interest.

We chose to explore user commentary of climate change over Twitter. Twitter activity contains information about: the textual content generated by users (i.e., content of tweets), interactions between users, and the time of content creation (Veltri and Atanasova, 2017 ). This allows us to examine the content of user communication, taking into account the temporal and social contexts of their behavior. Twitter data is relatively easy for researchers to access. Many tweets reside within a public domain, and are accessible through free and accessible APIs.

The characteristics of Twitter’s platform are also favorable for data analysis. An established literature describes computational techniques and considerations for interpreting Twitter data. We used the approaches and findings from other empirical investigations to inform our approach. For example, we drew on past literature to inform the process of identifying which tweets were related to climate change.

Public discussion on climate change

Climate change is one of the greatest challenges facing humanity (Schneider, 2011 ). Steps to prevent and mitigate the damaging consequences of climate change require changes on different political, societal, and individual levels (Lorenzoni & Pidgeon, 2006 ). Insights into public commentary can inform decision making and communication of climate policy and science.

Traditionally, public perceptions are investigated through survey designs and qualitative work (Lorenzoni & Pidgeon, 2006 ). Inquiries into social media allow researchers to explore a large and diverse range of climate change-related dialogue (Auer et al., 2014 ). Yet, existing inquiries of Twitter activity are few in number and typically constrained to specific events related to climate change, such as the release of the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (Newman et al., 2010 ; O’Neill et al., 2015 ; Pearce, 2014 ) and the 2015 United Nations Climate Change Conference, held in Paris (Pathak et al., 2017 ).

When longer time scales are explored, most researchers rely heavily upon computational methods to derive topics of commentary. For example, Kirilenko and Stepchenkova ( 2014 ) examined the topics of climate change tweets posted in 2012, as indicated by the most prevalent hashtags. Although hashtags can mark the topics of tweets, it is a crude measure as tweets with no hashtags are omitted from analysis, and not all topics are indicated via hashtags (e.g., Nugroho, Yang, Zhao, Paris, & Nepal, 2017 ). In a more sophisticated approach, Veltri and Atanasova ( 2017 ) examined the co-occurrence of terms using hierarchical clustering techniques to map the semantic space of climate change tweet content from the year 2013. They identified four themes: (1) “calls for action and increasing awareness”, (2) “discussions about the consequences of climate change”, (3) “policy debate about climate change and energy”, and (4) “local events associated with climate change” (p. 729).

Our research builds on the existing literature in two ways. Firstly, we explore a new data set—Australian tweets over the year 2016. Secondly, in comparison to existing research of Twitter data spanning long time periods, we use qualitative techniques to provide a more nuanced understanding of the topics of climate change. By applying our mixed-methods framework, we address our research question: what are the common topics of Australian’s tweets about climate change?

Outline of approach

We employed our four-phased framework as shown in Fig.  2 . Firstly, we harvested climate change tweets posted in Australia in 2016 and compiled a corpus (phase 1). We then utilized a topic modeling technique (Nugroho et al., 2017 ) to organize the diverse content of the corpus into a number of topics. We were interested in topics which commonly appeared throughout the time period of data collection, and less interested in more transitory topics. To identify enduring topics, we used a topic alignment algorithm (Chuang et al., 2015 ) to group similar topics occurring repeatedly throughout 2016 (phase 2). This process allowed us to identify the topics most relevant to our research question. From each of these, we extracted a manageable subset of data (phase 3). We then performed a qualitative thematic analysis (see Braun & Clarke, 2006 ) on this subset of data to inductively derive themes and answer our research question (phase 4). Footnote 2

Flowchart of application of a four-phased framework for conducting qualitative analyses using data science techniques. We were most interested in topics that frequently occurred throughout the period of data collection. To identify these, we organized the corpus chronologically, and divided the corpus into batches of content. Using computational techniques (shown in blue ), we uncovered topics in each batch and identified similar topics which repeatedly occurred across batches. When identifying topics in each batch, we generated three alternative representations of topics (5, 10, and 20 topics in each batch, shown in yellow ). In stages highlighted in green , we determined the quality of these representations, ultimately selecting the five topics per batch solution

Phase 1: Compiling a corpus

To search Australian’s Twitter data, we used CSIRO’s Emergency Situation Awareness (ESA) platform (CSIRO, 2018 ). The platform was originally built to detect, track, and report on unexpected incidences related to crisis situations (e.g., fires, floods; see Cameron, Power, Robinson, & Yin 2012 ). To do so, the ESA platform harvests tweets based on a location search that covers most of Australia and New Zealand.

The ESA platform archives the harvested tweets, which may be used for other CSIRO research projects. From this archive, we retrieved tweets satisfying three criteria: (1) tweets must be associated with an Australian location, (2) tweets must be harvested from the year 2016, and (3) the content of tweets must be related to climate change. We tested the viability of different markers of climate change tweets used in previous empirical work (Jang & Hart, 2015 ; Newman, 2016 ; Holmberg & Hellsten, 2016 ; O’Neill et al., 2015 ; Pearce et al., 2014 ; Sisco et al., 2017 ; Swain, 2017 ; Williams et al., 2015 ) by informally inspecting the content of tweets matching each criteria. Ultimately, we employed five terms (or combinations of terms) reliably associated with climate change: (1) “climate” AND “change”; (2) “#climatechange”; (3) “#climate”; (4) “global” AND “warming”; and (5) “#globalwarming”. This yielded a corpus of 201,506 tweets.

Phase 2: Using data science techniques to compress the corpus along a dimension of relevance

The next step was to organize the collection of tweets into distinct topics. A topic is an abstract representation of semantically related words and concepts. Each tweet belongs to a topic, and each topic may be represented as a list of keywords (i.e., prominent words of tweets belonging to the topic).

A vast literature surrounds the computational derivation of topics within textual corpora, and specifically within Twitter corpora (Ramage et al., 2010 ; Nugroho et al., 2017 ; Fang et al., 2016a ; Chuang et al., 2014 ). Popular methods for deriving topics include: probabilistic latent semantic analysis (Hofmann, 1999 ), non-negative matrix factorization (Lee & Seung, 2000 ), and latent Dirichlet allocation (Blei et al., 2003 ). These approaches use patterns of co-occurrence of terms within documents to derive topics. They work best on long documents. Tweets, however, are short, and thus only a few unique terms may co-occur between tweets. Consequently, approaches which rely upon patterns of term co-occurrence suffer within the Twitter environment. Moreover, these approaches ignore valuable social and temporal information (Nugroho et al., 2017 ). For example, consider a tweet t 1 and its reply t 2 . The reply feature of Twitter allows users to react to tweets and enter conversations. Therefore, it is likely t 1 and t 2 are related in topic, by virtue of the reply interaction.

To address sparsity concerns, we adopt the non-negative matrix inter-joint factorization (NMijF) of Nugroho et al., ( 2017 ). This process uses both tweet content (i.e., the patterns of co-occurrence of terms amongst tweets) and socio-temporal relationship between tweets (i.e., similarities in the users mentioned in tweets, whether the tweet is a reply to another tweet, whether tweets are posted at a similar time) to derive topics (see Supplementary Material ). The NMijF method has been demonstrated to outperform other topic modeling techniques on Twitter data (Nugroho et al., 2017 ).

Dividing the corpus into batches

Deriving many topics across a data set of thousands of tweets is prohibitively expensive in computational terms. Therefore, we divided the corpus into smaller batches and derived the topics of each batch. To keep the temporal relationships amongst tweets (e.g., timestamps of the tweets) the batches were organized chronologically. The data was partitioned into 41 disjoint batches (40 batches of 5000 tweets; one batch of 1506 tweets).

Generating topical representations for each batch

Following standard topic modeling practice, we removed features from each tweet which may compromise the quality of the topic derivation process. These features include: emoticons, punctuation, terms with fewer than three characters, stop-words (for list of stop-words, see MySQL, 2018 ), and phrases used to harvest the data (e.g., “#climatechange”). Footnote 3 Following this, the terms remaining in tweets were stemmed using the Natural Language Toolkit for Python (Bird et al., 2009 ). All stemmed terms were then tokenized for processing.

The NMijF topic derivation process requires three parameters (see Supplementary Material for more details). We set two of these parameters to the recommendations of Nugroho et al., ( 2017 ), based on empirical analysis. The final parameter—the number of topics derived from each batch—is difficult to estimate a priori , and must be made with some care. If k is too small, keywords and tweets belonging to a topic may be difficult to conceptualize as a singular, coherent, and meaningful topic. If k is too large, keywords and tweets belonging to a topic may be too specific and obscure. To determine a reasonable value of k , we ran the NMijF process on each batch with three different levels of the parameter—5, 10, and 20 topics per batch. This process generated three different representations of the corpus: 205, 410, and 820 topics. For each of these representations, each tweet was classified into one (and only one) topic. We represented each topic as a list of ten keywords most prevalent within the tweets of that topic.

Assessing the quality of topical representations

To select a topical representation for further analysis, we inspected the quality of each. Initially, we considered the use of a completely automatic process to assess or produce high quality topic derivations. However, our attempts to use completely automated techniques on tweets with a known topic structure failed to produce correct or reasonable solutions. Thus, we assessed quality using human assessment (see Table  1 ). The first stage involved inspecting each topical representation of the corpus (205, 410, and 820 topics), and manually flagging any topics that were clearly problematic. Specifically, we examined each topical representation to determine whether topics represented as separate were in fact distinguishable from one another. We discovered that the 820 topic representation (20 topics per batch) contained many closely related topics.

To quantify the distinctiveness between topics, we compared each topic to each other topic in the same batch in an automated process. If two topics shared three or more (of ten) keywords, these topics were deemed similar. We adopted this threshold from existing topic modeling work (Fang et al., 2016a , b ), and verified it through an informal inspection. We found that pairs of topics below this threshold were less similar than those equal to or above it. Using this threshold, the 820 topic representation was identified as less distinctive than other representations. Of the 41 batches, nine contained at least two similar topics for the 820 topic representation (cf., 0 batches for the 205 topic representation, two batches for the 410 topic representation). As a result, we chose to exclude the representation from further analysis.

The second stage of quality assessment involved inspecting the quality of individual topics. To achieve this, we adopted the pairwise topic preference task outlined by Fang et al. ( 2016a , b ). In this task, raters were shown pairs of two similar topics (represented as ten keywords), one from the 205 topic representation and the other from the 410 topic representation. To assist in their interpretation of topics, raters could also view three tweets belonging to each topic. For each pair of topics, raters indicated which topic they believed was superior, on the basis of coherency, meaning, interpretability, and the related tweets (see Table  1 ). Through aggregating responses, a relative measure of quality could be derived.

Initially, members of the research team assessed 24 pairs of topics. Results from the task did not indicate a marked preference for either topical representation. To confirm this impression more objectively, we recruited participants from the Australian community as raters. We used Qualtrics—an online survey platform and recruitment service—to recruit 154 Australian participants, matched with the general Australian population on age and gender. Each participant completed judgments on 12 pairs of similar topics (see Supplementary Material for further information).

Participants generally preferred the 410 topic representation over the 205 topic representation ( M = 6.45 of 12 judgments, S D = 1.87). Of 154 participants, 35 were classified as indifferent (selected both topic representations an equal number of times), 74 preferred the 410 topic representation (i.e., selected the 410 topic representation more often than the 205 topic representation), and 45 preferred the 205 topic representation (i.e., selected the 205 topic representation more often that the 410 topic representation). We conducted binomial tests to determine whether the proportion of participants of the three just described types differed reliably from chance levels (0.33). The proportion of indifferent participants (0.23) was reliably lower than chance ( p = 0.005), whereas the proportion of participants preferring the 205 topic solution (0.29) did not differ reliably from chance levels ( p = 0.305). Critically, the proportion of participants preferring the 410 topic solution (0.48) was reliably higher than expected by chance ( p < 0.001). Overall, this pattern indicates a participant preference for the 410 topic representation over the 205 topic representation.

In summary, no topical representation was unequivocally superior. On a batch level, the 410 topic representation contained more batches of non-distinct topic solutions than the 205 topic representation, indicating that the 205 topic representation contained topics which were more distinct. In contrast, on the level of individual topics, the 410 topic representation was preferred by human raters. We use this information, in conjunction with the utility of corresponding aligned topics (see below), to decide which representation is most suitable for our research purposes.

Grouping similar topics repeated in different batches

We were most interested in topics which occurred throughout the year (i.e., in multiple batches) to identify the most stable components of climate change commentary (phase 3). We grouped similar topics from different batches using a topical alignment algorithm (see Chuang et al. 2015 ). This process requires a similarity metric and a similarity threshold. The similarity metric represents the similarity between two topics, which we specified as the proportion of shared keywords (from 0, no keywords shared, to 1, all ten keywords shared). The similarity threshold is a value below which two topics were deemed dissimilar. As above, we set the threshold to 0.3 (three of ten keywords shared)—if two topics shared two or fewer keywords, the topics could not be justifiably classified as similar. To delineate important topics, groups of topics, and other concepts we have provided a glossary of terms in Table  2 .

The topic alignment algorithm is initialized by assigning each topic to its own group. The alignment algorithm iteratively merges the two most similar groups, where the similarity between groups is the maximum similarity between a topic belonging to one group and another topic belonging to the other. Only topics from different groups (by definition, topics from the same group are already grouped as similar) and different batches (by definition, topics from the same batch cannot be similar) can be grouped. This process continues, merging similar groups until no compatible groups remain. We found our initial implementation generated groups of largely dissimilar topics. To address this, we introduced an additional constraint—groups could only be merged if the mean similarity between pairs of topics (each belonging to the two groups in question) was greater than the similarity threshold. This process produced groups of similar topics. Functionally, this allowed us to detect topics repeated throughout the year.

We ran the topical alignment algorithm across both the 205 and 410 topic representations. For the 205 and 410 topic representation respectively, 22.47 and 31.60% of tweets were not associated with topics that aligned with others. This exemplifies the ephemeral and dynamic attributes of Twitter activity: over time, the content of tweets shifts, with some topics appearing only once throughout the year (i.e., in only one batch). In contrast, we identified 42 groups (69.77% of topics) and 101 groups (62.93% of topics) of related topics for the 205 and 410 topic representations respectively, occurring across different time periods (i.e., in more than one batch). Thus, both representations contained transient topics (isolated to one batch) and recurrent topics (present in more than one batch, belonging to a group of two or more topics).

Identifying topics most relevant for answering our research question

For the subsequent qualitative analyses, we were primarily interested in topics prevalent throughout the corpus. We operationalized prevalent topic groupings as any grouping of topics that spanned three or more batches. On this basis, 22 (57.50% of tweets) and 36 (35.14% of tweets) groupings of topics were identified as prevalent for the 205 and 410 topic representations, respectively (see Table  3 ). As an example, consider the prevalent topic groupings from the 205 topic representation, shown in Table  3 . Ten topics are united by commentary on the Great Barrier Reef (Group 2)—indicating this facet of climate change commentary was prevalent throughout the year. In contrast, some topics rarely occurred, such as a topic concerning a climate change comic (indicated by the keywords “xkcd” and “comic”) occurring once and twice in the 205 and 410 topic representation, respectively. Although such topics are meaningful and interesting, they are transient aspects of climate change commen tary and less relevant to our research question. In sum, topic modeling and grouping algorithms have allowed us to collate massive amounts of information, and identify components of the corpus most relevant to our qualitative inquiry.

Selecting the most favorable topical representation

At this stage, we have two complete and coherent representations of the corpus topics, and indications of which topics are most relevant to our research question. Although some evidence indicated that the 410 topic representation contains topics of higher quality, the 205 topic representation was more parsimonious on both the level of topics and groups of topics. Thus, we selected the 205 topic representation for further analysis.

Phase 3. Extract a subset of data

Extracting a subset of data from the selected topical representation.

Before qualitative analysis, researchers must extract a subset of data manageable in size. For this process, we concerned ourselves with only the content of prevalent topic groupings, seen in Table  3 . From each of the 22 prevalent topic groupings, we randomly sampled ten tweets. We selected ten tweets as a trade-off between comprehensiveness and feasibility. This thus reduced our data space for qualitative analysis from 201,423 tweets to 220.

Phase 4: Perform qualitative analysis

Perform thematic analysis.

In the final phase of our analysis, we performed a qualitative thematic analysis (TA; Braun & Clarke, 2006 ) on the subset of tweets sampled in phase 3. This analysis generated distinct themes, each of which answers our research question: what are the common topics of Australian’s tweets about climate change? As such, the themes generated through TA are topics. However, unlike the topics derived from the preceding computational approaches, these themes are informed by the human coder’s interpretation of content and are oriented towards our specific research question. This allows the incorporation of important diagnostic information, including the broader socio-political context of discussed events or terms, and an understanding (albeit, sometimes ambiguous) of the underlying latent meaning of tweets.

We selected TA as the approach allows for flexibility in assumptions and philosophical approaches to qualitative inquiries. Moreover, the approach is used to emphasize similarities and differences between units of analysis (i.e., between tweets) and is therefore useful for generating topics. However, TA is typically applied to lengthy interview transcripts or responses to open survey questions, rather than small units of analysis produced through Twitter activity. To ease the application of TA to small units of analysis, we modified the typical TA process (shown in Table  4 ) as follows.

Firstly, when performing phases 1 and 2 of TA, we initially read through each prevalent topic grouping’s tweets sequentially. By doing this, we took advantage of the relative homogeneity of content within topics. That is, tweets sharing the same topic will be more similar in content than tweets belonging to separate topics. When reading ambiguous tweets, we could use the tweet’s topic (and other related topics from the same group) to aid comprehension. Through the scaffold of topic representations, we facilitated the process of interpreting the data, generating initial codes, and deriving themes.

Secondly, the prevalent topic groupings were used to create initial codes and search for themes (TA phase 2 and 3). For example, the groups of topics indicate content of climate change action (group 1), the Great Barrier Reef (group 2), climate change deniers (group 3), and extreme weather (group 5). The keywords characterizing these topics were used as initial codes (e.g., “action”, “Great Barrier Reef”, “Paris Agreement”, “denial”). In sum, the algorithmic output provided us with an initial set of codes and an understanding of the topic structure that can indicate important features of the corpus.

A member of the research team performed this augmented TA to generate themes. A second rater outside of the research team applied the generated themes to the data, and inter-rater agreement was assessed. Following this, the two raters reached a consensus on the theme of each tweet.

Through TA, we inductively generated five distinct themes. We assigned each tweet to one (and only one) theme. A degree of ambiguity is involved in designating themes for tweets, and seven tweets were too ambiguous to subsume into our thematic framework. The remaining 213 tweets were assigned to one of five themes shown in Table  5 .

In an initial application of the coding scheme, the two raters agreed upon 161 (73.181%) of 220 tweets. Inter-rater reliability was satisfactory, Cohen’s κ = 0.648, p < 0.05. An assessment of agreement for each theme is presented in Table  5 . The proportion of agreement is the total proportion of observations where the two coders both agreed: (1) a tweet belonged to the theme, or (2) a tweet did not belong to the theme. The proportion of specific agreement is the conditional probability that a randomly selected rater will assign the theme to a tweet, given that the other rater did (see Supplementary Material for more information). Theme 3, theme 5, and the N/A categorization had lower levels of agreement than the remaining themes, possibly as tweets belonging to themes 3 and 5 often make references to content relevant to other themes.

Theme 1. Climate change action

The theme occurring most often was climate change action, whereby tweets were related to coping with, preparing for, or preventing climate change. Tweets comment on the action (and inaction) of politicians, political parties, and international cooperation between government, and to a lesser degree, industry, media, and the public. The theme encapsulated commentary on: prioritizing climate change action (“ Let’s start working together for real solutions on climate change ”); Footnote 4 relevant strategies and policies to provide such action (“ #OurOcean is absorbing the majority of #climatechange heat. We need #marinereserves to help build resilience. ”); and the undertaking (“ Labor will take action on climate change, cut pollution, secure investment & jobs in a growing renewables industry ”) or disregarding (“ act on Paris not just sign ”) of action.

Often, users were critical of current or anticipated action (or inaction) towards climate change, criticizing approaches by politicians and governments as ineffective (“ Malcolm Turnbull will never have a credible climate change policy ”), Footnote 5 and undesirable (“ Govt: how can we solve this vexed problem of climate change? Helpful bystander: u could not allow a gigantic coal mine. Govt: but srsly how? ”). Predominately, users characterized the government as unjustifiably paralyzed (“ If a foreign country did half the damage to our country as #climatechange we would declare war. ”), without a leadership focused on addressing climate change (“ an election that leaves Australia with no leadership on #climatechange - the issue of our time! ”).

Theme 2. Consequences of climate change

Users commented on the consequences and risks attributed to climate change. This theme may be further categorized into commentary of: physical systems, such as changes in climate, weather, sea ice, and ocean currents (“ Australia experiencing more extreme fire weather, hotter days as climate changes ”); biological systems, such as marine life (particularly, the Great Barrier Reef) and biodiversity (“ Reefs of the future could look like this if we continue to ignore #climatechange ”); human systems (“ You and your friends will die of old age & I’m going to die from climate change ”); and other miscellaneous consequences (“ The reality is, no matter who you supported, or who wins, climate change is going to destroy everything you love ”). Users specified a wide range of risks and impacts on human systems, such as health, cultural diversity, and insurance. Generally, the consequences of climate change were perceived as negative.

Theme 3. Conversations on climate change

Some commentary centered around discussions of climate change communication, debates, art, media, and podcasts. Frequently, these pertained to debates between politicians (“ not so gripping from No Principles Malcolm. Not one mention of climate change in his pitch. ”) and television panel discussions (“ Yes let’s all debate whether climate change is happening... #qanda ”). Footnote 6 Users condemned the climate change discussions of federal government (“ Turnbull gov echoes Stalinist Russia? Australia scrubbed from UN climate change report after government intervention ”), those skeptical of climate change (“ Trouble is climate change deniers use weather info to muddy debate. Careful???????????????? ”), and media (“ Will politicians & MSM hacks ever work out that they cannot spin our way out of the #climatechange crisis? ”). The term “climate change” was critiqued, both by users skeptical of the legitimacy of climate change (“ Weren’t we supposed to call it ‘climate change’ now? Are we back to ‘global warming’ again? What happened? Apart from summer? ”) and by users seeking action (“ Maybe governments will actually listen if we stop saying “extreme weather” & “climate change” & just say the atmosphere is being radicalized ”).

Theme 4. Climate change deniers

The fourth theme involved commentary on individuals or groups who were perceived to deny climate change. Generally, these were politicians and associated political parties, such as: Malcolm Roberts (a climate change skeptic, elected as an Australian Senator in 2016), Malcolm Turnbull, and Donald Trump. Commentary focused on the beliefs and legitimacy of those who deny the science of climate change (“ One Nation’s Malcolm Roberts is in denial about the facts of climate change ”) or support the denial of climate change science (“ Meanwhile in Australia... Malcolm Roberts, funded by climate change skeptic global groups loses the plot when nobody believes his findings ”). Some users advocated attempts to change the beliefs of those who deny climate change science (“ We have a president-elect who doesn’t believe in climate change. Millions of people are going to have to say: Mr. Trump, you are dead wrong ”), whereas others advocated disengaging from conversation entirely (“ You know I just don’t see any point engaging with climate change deniers like Roberts. Ignore him ”). In comparison to other themes, commentary revolved around individuals and their beliefs, rather than the phenomenon of climate change itself.

Theme 5. The legitimacy of climate change and climate science

Using our four-phased framework, we aimed to identify and qualitatively inspect the most enduring aspects of climate change commentary from Australian posts on Twitter in 2016. We achieved this by using computational techniques to model 205 topics of the corpus, and identify and group similar topics that repeatedly occurred throughout the year. From the most relevant topic groupings, we extracted a subsample of tweets and identified five themes with a thematic analysis: climate change action, consequences of climate change, conversations on climate change, climate change deniers, and the legitimacy of climate change and climate science. Overall, we demonstrated the process of using a mixed-methodology that blends qualitative analyses with data science methods to explore social media data.

Our workflow draws on the advantages of both quantitative and qualitative techniques. Without quantitative techniques, it would be impossible to derive topics that apply to the entire corpus. The derived topics are a preliminary map for understanding the corpus, serving as a scaffold upon which we could derive meaningful themes contextualized within the wider socio-political context of Australia in 2016. By incorporating quantitatively-derived topics into the qualitative process, we attempted to construct themes that would generalize to a larger, relevant component of the corpus. The robustness of these themes is corroborated by their association with computationally-derived topics, which repeatedly occurred throughout the year (i.e., prevalent topic groupings). Moreover, four of the five themes have been observed in existing data science analyses of Twitter climate change commentary. Within the literature, the themes of climate change action and consequences of climate change are common (Newman, 2016 ; O’Neill et al., 2015 ; Pathak et al., 2017 ; Pearce, 2014 ; Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ). The themes of the legitimacy of climate change and climate science (Jang & Hart, 2015 ; Newman, 2016 ; O’Neill et al., 2015 ; Pearce, 2014 ) and climate change deniers (Pathak et al., 2017 ) have also been observed. The replication of these themes demonstrates the validity of our findings.

One of the five themes—conversations on climate change—has not been explicitly identified in existing data science analyses of tweets on climate change. Although not explicitly identifying the theme, Kirilenko and Stepchenkova ( 2014 ) found hashtags related to public conversations (e.g., “#qanda”, “#Debates”) were used frequently throughout the year 2012. Similar to the literature, few (if any) topics in our 205 topic solution could be construed as solely relating to the theme of “conversation”. However, as we progressed through the different phases of the framework, the theme became increasingly apparent. By the grouping stage, we identified a collection of topics unified by a keyword relating to debate. The subsequent thematic analysis clearly discerned this theme. The derivation of a theme previously undetected by other data science studies lends credence to the conclusions of Guetterman et al., ( 2018 ), who deduced that supplementing a quantitative approach with a qualitative technique can lead to the generation of more themes than a quantitative approach alone.

The uniqueness of a conversational theme can be accounted for by three potentially contributing factors. Firstly, tweets related to conversations on climate change often contained material pertinent to other themes. The overlap between this theme and others may hinder the capabilities of computational techniques to uniquely cluster these tweets, and undermine the ability of humans to reach agreement when coding content for this theme (indicated by the relatively low proportion of specific agreement in our thematic analysis). Secondly, a conversational theme may only be relevant in election years. Unlike other studies spanning long time periods (Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ), Kirilenko and Stepchenkova ( 2014 ) and our study harvested data from US presidential election years (2012 and 2016, respectively). Moreover, an Australian federal election occurred in our year of observation. The occurrence of national elections and associated political debates may generate more discussion and criticisms of conversations on climate change. Alternatively, the emergence of a conversational theme may be attributable to the Australian panel discussion television program Q & A. The program regularly hosts politicians and other public figures to discuss political issues. Viewers are encouraged to participate by publishing tweets using the hashtag “#qanda”, perhaps prompting viewers to generate uniquely tagged content not otherwise observed in other countries. Importantly, in 2016, Q & A featured a debate on climate change between science communicator Professor Brian Cox and Senator Malcolm Roberts, a prominent climate science skeptic.

Although our four-phased framework capitalizes on both quantitative and qualitative techniques, it still has limitations. Namely, the sparse content relationships between data points (in our case, tweets) can jeopardize the quality and reproducibility of algorithmic results (e.g., Chuang et al., 2015 ). Moreover, computational techniques can require large computing resources. To a degree, our application mitigated these limitations. We adopted a topic modeling algorithm which uses additional dimensions of tweets (social and temporal) to address the influence of term-to-term sparsity (Nugroho et al., 2017 ). To circumvent concerns of computing resources, we partitioned the corpus into batches, modeled the topics in each batch, and grouped similar topics together using another computational technique (Chuang et al., 2015 ).

As a demonstration of our four-phased framework, our application is limited to a single example. For data collection, we were able to draw from the procedures of existing studies which had successfully used keywords to identify climate change tweets. Without an existing literature, identifying diagnostic terms can be difficult. Nevertheless, this demonstration of our four-phased framework exemplifies some of the critical decisions analysts must make when utilizing a mixed-method approach to social media data.

Both qualitative and quantitative researchers can benefit from our four-phased framework. For qualitative researchers, we provide a novel vehicle for addressing their research questions. The diversity and volume of content of social media data may be overwhelming for both the researcher and their method. Through computational techniques, the diversity and scale of data can be managed, allowing researchers to obtain a large volume of data and extract from it a relevant sample to conduct qualitative analyses. Additionally, computational techniques can help researchers explore and comprehend the nature of their data. For the quantitative researcher, our four-phased framework provides a strategy for formally documenting the qualitative interpretations. When applying algorithms, analysts must ultimately make qualitative assessments of the quality and meaning of output. In comparison to the mathematical machinery underpinning these techniques, the qualitative interpretations of algorithmic output are not well-documented. As these qualitative judgments are inseparable from data science, researchers should strive to formalize and document their decisions—our framework provides one means of achieving this goal.

Through the application of our four-phased framework, we contribute to an emerging literature on public perceptions of climate change by providing an in-depth examination of the structure of Australian social media discourse. This insight is useful for communicators and policy makers hoping to understand and engage the Australian online public. Our findings indicate that, within Australian commentary on climate change, a wide variety of messages and sentiment are present. A positive aspect of the commentary is that many users want action on climate change. The time is ripe it would seem for communicators to discuss Australia’s policy response to climate change—the public are listening and they want to be involved in the discussion. Consistent with this, we find some users discussing conversations about climate change as a topic. Yet, in some quarters there is still skepticism about the legitimacy of climate change and climate science, and so there remains a pressing need to implement strategies to persuade members of the Australian public of the reality and urgency of the climate change problem. At the same time, our analyses suggest that climate communicators must counter the sometimes held belief, expressed in our second theme on climate change consequences, that it is already too late to solve the climate problem. Members of the public need to be aware of the gravity of the climate change problem, but they also need powerful self efficacy promoting messages that convince them that we still have time to solve the problem, and that their individual actions matter.

On Twitter, users may precede a phrase with a hashtag (#). This allows users to signify and search for tweets related to a specific theme.

The analysis of this study was preregistered on the Open Science Framework: https://osf.io/mb8kh/ . See the Supplementary Material for a discussion of discrepancies. Analysis scripts and interim results from computational techniques can be found at: https://github.com/AndreottaM/TopicAlignment .

83 tweets were rendered empty and discarded from the corpus.

The content of tweet are reported verbatim. Sensitive information is redacted.

Malcolm Turnbull was the Prime Minister of Australia during the year 2016.

“ #qanda ” is a hashtag used to refer to Q & A, an Australian panel discussion television program.

Commonwealth Scientific and Industrial Research Organisation (CSIRO) is the national scientific research agency of Australia.

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Andreotta, M., Nugroho, R., Hurlstone, M.J. et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behav Res 51 , 1766–1781 (2019). https://doi.org/10.3758/s13428-019-01202-8

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THE IMPACT OF SOCIAL MEDIA ADVERTISMENT ON CONSUMER BUYING BEHAVIOUR - Bajon Daa Useni

Bajon Daa Useni

This study investigates the impact of social media advertising on consumer buying behavior. With the pervasive influence of social media platforms in contemporary society, businesses increasingly rely on social media advertising to reach and engage with consumers. The study utilizes secondary data spanning from 2013 to 2023 to analyze the relationship between social media advertising exposure and consumer buying behavior. Drawing on theories from economics and marketing, as well as empirical research on social media advertising effectiveness, the study employs regression analysis, mediation analysis, and moderation analysis to examine the direct and indirect effects of social media advertising on consumer purchase intentions and satisfaction. The findings reveal that social media exposure positively influences consumer purchase intentions, with brand awareness mediating this relationship. Additionally, demographic variables such as gender, age group, and occupation moderate the effectiveness of social media advertising on consumer buying behavior. Based on these findings, recommendations are provided for businesses to optimize their social media advertising strategies and enhance consumer engagement and satisfaction. The study contributes to the understanding of the role of social media advertising in shaping consumer behavior and provides valuable insights for practitioners and researchers in the fields of marketing and economics.

SDIWC Organization

The flow of information in the age of social media is bidirectional and interactive, hence creating wealth of information. This information is leveraged by the consumer when conducting external search for his consumption decisions. However, only few studies have explored the influence of social media content on decision making act during the consumption process, it hence needs further investigation. In this study, we try to fill the gap by using in depth interviews with 21 participants, to investigate how product information on social media influences consumers’ propensity to consume. The findings of the research propose the “IDEA” model which explains that Social Media Content can influence the decision making process of other consumers (1) by providing Information about products, (2) by instilling Desire among consumers, (3) by sharing of Experience knowledge and (4) by extinguishing Anxiety around a product purchase decision. Our results give the community managers some elements to understand and manage their brand’s appearance in different social media.

husnain mustafa

UNICAF UNIVERSITY

Geofrey Lusaggi

For any business to thrive in this era, it has to embrace social media in its marketing strategy combination as a critical element of business operations. The strength of the customer relationships is through uniting communications across the entire business, from 6 marketing and sales to customer service and operations. Cheri Husney in Guth, D. J .(2021) is asserts that even if one uses the traditional marketing platforms like seminar, the exercise becomes more fulfilling and effective when followed by social media platforms like tweeter, SMS, and others. A business must engage hybrid marketing approach of including online and traditional marketing. This helps the business to optimize the scarce recourse for best results. Integration of social media marketing to other core business process enables greater yields (Bae, Il-Hyun, Zamrudi, M.F., 2018). This approach strikes a balance between the positives impacts of conventional marketing like: ease, faster and cheap way of reaching mass customers, instant feedback, market intelligence and proactive enablement among others, yet traditional marketing approaches bring in positives impacts like: the ability to reach prospects that are off online or not any social media, control of people’s feedback, focused customer reach and others. Furthermore, elements in a marketing mix complement and generate information of one another. Data security and information control must be handled with lots vigilance and with caution.

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Home — Essay Samples — Sociology — Social Media — Social Media: Thesis Statement

Social Media: Thesis Statement

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Published: Mar 16, 2024

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Positive effects, negative effects, positive social change.

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6 Example Essays on Social Media | Advantages, Effects, and Outlines

Got an essay assignment about the effects of social media we got you covered check out our examples and outlines below.

Social media has become one of our society's most prominent ways of communication and information sharing in a very short time. It has changed how we communicate and has given us a platform to express our views and opinions and connect with others. It keeps us informed about the world around us. Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn have brought individuals from all over the world together, breaking down geographical borders and fostering a genuinely global community.

However, social media comes with its difficulties. With the rise of misinformation, cyberbullying, and privacy problems, it's critical to utilize these platforms properly and be aware of the risks. Students in the academic world are frequently assigned essays about the impact of social media on numerous elements of our lives, such as relationships, politics, and culture. These essays necessitate a thorough comprehension of the subject matter, critical thinking, and the ability to synthesize and convey information clearly and succinctly.

But where do you begin? It can be challenging to know where to start with so much information available. Jenni.ai comes in handy here. Jenni.ai is an AI application built exclusively for students to help them write essays more quickly and easily. Jenni.ai provides students with inspiration and assistance on how to approach their essays with its enormous database of sample essays on a variety of themes, including social media. Jenni.ai is the solution you've been looking for if you're experiencing writer's block or need assistance getting started.

So, whether you're a student looking to better your essay writing skills or want to remain up to date on the latest social media advancements, Jenni.ai is here to help. Jenni.ai is the ideal tool for helping you write your finest essay ever, thanks to its simple design, an extensive database of example essays, and cutting-edge AI technology. So, why delay? Sign up for a free trial of Jenni.ai today and begin exploring the worlds of social networking and essay writing!

Want to learn how to write an argumentative essay? Check out these inspiring examples!

We will provide various examples of social media essays so you may get a feel for the genre.

6 Examples of Social Media Essays

Here are 6 examples of Social Media Essays:

The Impact of Social Media on Relationships and Communication

Introduction:.

The way we share information and build relationships has evolved as a direct result of the prevalence of social media in our daily lives. The influence of social media on interpersonal connections and conversation is a hot topic. Although social media has many positive effects, such as bringing people together regardless of physical proximity and making communication quicker and more accessible, it also has a dark side that can affect interpersonal connections and dialogue.

Positive Effects:

Connecting People Across Distances

One of social media's most significant benefits is its ability to connect individuals across long distances. People can use social media platforms to interact and stay in touch with friends and family far away. People can now maintain intimate relationships with those they care about, even when physically separated.

Improved Communication Speed and Efficiency

Additionally, the proliferation of social media sites has accelerated and simplified communication. Thanks to instant messaging, users can have short, timely conversations rather than lengthy ones via email. Furthermore, social media facilitates group communication, such as with classmates or employees, by providing a unified forum for such activities.

Negative Effects:

Decreased Face-to-Face Communication

The decline in in-person interaction is one of social media's most pernicious consequences on interpersonal connections and dialogue. People's reliance on digital communication over in-person contact has increased along with the popularity of social media. Face-to-face interaction has suffered as a result, which has adverse effects on interpersonal relationships and the development of social skills.

Decreased Emotional Intimacy

Another adverse effect of social media on relationships and communication is decreased emotional intimacy. Digital communication lacks the nonverbal cues and facial expressions critical in building emotional connections with others. This can make it more difficult for people to develop close and meaningful relationships, leading to increased loneliness and isolation.

Increased Conflict and Miscommunication

Finally, social media can also lead to increased conflict and miscommunication. The anonymity and distance provided by digital communication can lead to misunderstandings and hurtful comments that might not have been made face-to-face. Additionally, social media can provide a platform for cyberbullying , which can have severe consequences for the victim's mental health and well-being.

Conclusion:

In conclusion, the impact of social media on relationships and communication is a complex issue with both positive and negative effects. While social media platforms offer many benefits, such as connecting people across distances and enabling faster and more accessible communication, they also have a dark side that can negatively affect relationships and communication. It is up to individuals to use social media responsibly and to prioritize in-person communication in their relationships and interactions with others.

The Role of Social Media in the Spread of Misinformation and Fake News

Social media has revolutionized the way information is shared and disseminated. However, the ease and speed at which data can be spread on social media also make it a powerful tool for spreading misinformation and fake news. Misinformation and fake news can seriously affect public opinion, influence political decisions, and even cause harm to individuals and communities.

The Pervasiveness of Misinformation and Fake News on Social Media

Misinformation and fake news are prevalent on social media platforms, where they can spread quickly and reach a large audience. This is partly due to the way social media algorithms work, which prioritizes content likely to generate engagement, such as sensational or controversial stories. As a result, false information can spread rapidly and be widely shared before it is fact-checked or debunked.

The Influence of Social Media on Public Opinion

Social media can significantly impact public opinion, as people are likelier to believe the information they see shared by their friends and followers. This can lead to a self-reinforcing cycle, where misinformation and fake news are spread and reinforced, even in the face of evidence to the contrary.

The Challenge of Correcting Misinformation and Fake News

Correcting misinformation and fake news on social media can be a challenging task. This is partly due to the speed at which false information can spread and the difficulty of reaching the same audience exposed to the wrong information in the first place. Additionally, some individuals may be resistant to accepting correction, primarily if the incorrect information supports their beliefs or biases.

In conclusion, the function of social media in disseminating misinformation and fake news is complex and urgent. While social media has revolutionized the sharing of information, it has also made it simpler for false information to propagate and be widely believed. Individuals must be accountable for the information they share and consume, and social media firms must take measures to prevent the spread of disinformation and fake news on their platforms.

The Effects of Social Media on Mental Health and Well-Being

Social media has become an integral part of modern life, with billions of people around the world using platforms like Facebook, Instagram, and Twitter to stay connected with others and access information. However, while social media has many benefits, it can also negatively affect mental health and well-being.

Comparison and Low Self-Esteem

One of the key ways that social media can affect mental health is by promoting feelings of comparison and low self-esteem. People often present a curated version of their lives on social media, highlighting their successes and hiding their struggles. This can lead others to compare themselves unfavorably, leading to feelings of inadequacy and low self-esteem.

Cyberbullying and Online Harassment

Another way that social media can negatively impact mental health is through cyberbullying and online harassment. Social media provides a platform for anonymous individuals to harass and abuse others, leading to feelings of anxiety, fear, and depression.

Social Isolation

Despite its name, social media can also contribute to feelings of isolation. At the same time, people may have many online friends but need more meaningful in-person connections and support. This can lead to feelings of loneliness and depression.

Addiction and Overuse

Finally, social media can be addictive, leading to overuse and negatively impacting mental health and well-being. People may spend hours each day scrolling through their feeds, neglecting other important areas of their lives, such as work, family, and self-care.

In sum, social media has positive and negative consequences on one's psychological and emotional well-being. Realizing this, and taking measures like reducing one's social media use, reaching out to loved ones for help, and prioritizing one's well-being, are crucial. In addition, it's vital that social media giants take ownership of their platforms and actively encourage excellent mental health and well-being.

The Use of Social Media in Political Activism and Social Movements

Social media has recently become increasingly crucial in political action and social movements. Platforms such as Twitter, Facebook, and Instagram have given people new ways to express themselves, organize protests, and raise awareness about social and political issues.

Raising Awareness and Mobilizing Action

One of the most important uses of social media in political activity and social movements has been to raise awareness about important issues and mobilize action. Hashtags such as #MeToo and #BlackLivesMatter, for example, have brought attention to sexual harassment and racial injustice, respectively. Similarly, social media has been used to organize protests and other political actions, allowing people to band together and express themselves on a bigger scale.

Connecting with like-minded individuals

A second method in that social media has been utilized in political activity and social movements is to unite like-minded individuals. Through social media, individuals can join online groups, share knowledge and resources, and work with others to accomplish shared objectives. This has been especially significant for geographically scattered individuals or those without access to traditional means of political organizing.

Challenges and Limitations

As a vehicle for political action and social movements, social media has faced many obstacles and restrictions despite its many advantages. For instance, the propagation of misinformation and fake news on social media can impede attempts to disseminate accurate and reliable information. In addition, social media corporations have been condemned for censorship and insufficient protection of user rights.

In conclusion, social media has emerged as a potent instrument for political activism and social movements, giving voice to previously unheard communities and galvanizing support for change. Social media presents many opportunities for communication and collaboration. Still, users and institutions must be conscious of the risks and limitations of these tools to promote their responsible and productive usage.

The Potential Privacy Concerns Raised by Social Media Use and Data Collection Practices

With billions of users each day on sites like Facebook, Twitter, and Instagram, social media has ingrained itself into every aspect of our lives. While these platforms offer a straightforward method to communicate with others and exchange information, they also raise significant concerns over data collecting and privacy. This article will examine the possible privacy issues posed by social media use and data-gathering techniques.

Data Collection and Sharing

The gathering and sharing of personal data are significant privacy issues brought up by social media use. Social networking sites gather user data, including details about their relationships, hobbies, and routines. This information is made available to third-party businesses for various uses, such as marketing and advertising. This can lead to serious concerns about who has access to and uses our personal information.

Lack of Control Over Personal Information

The absence of user control over personal information is a significant privacy issue brought up by social media usage. Social media makes it challenging to limit who has access to and how data is utilized once it has been posted. Sensitive information may end up being extensively disseminated and may be used maliciously as a result.

Personalized Marketing

Social media companies utilize the information they gather about users to target them with adverts relevant to their interests and usage patterns. Although this could be useful, it might also cause consumers to worry about their privacy since they might feel that their personal information is being used without their permission. Furthermore, there are issues with the integrity of the data being used to target users and the possibility of prejudice based on individual traits.

Government Surveillance

Using social media might spark worries about government surveillance. There are significant concerns regarding privacy and free expression when governments in some nations utilize social media platforms to follow and monitor residents.

In conclusion, social media use raises significant concerns regarding data collecting and privacy. While these platforms make it easy to interact with people and exchange information, they also gather a lot of personal information, which raises questions about who may access it and how it will be used. Users should be aware of these privacy issues and take precautions to safeguard their personal information, such as exercising caution when choosing what details to disclose on social media and keeping their information sharing with other firms to a minimum.

The Ethical and Privacy Concerns Surrounding Social Media Use And Data Collection

Our use of social media to communicate with loved ones, acquire information, and even conduct business has become a crucial part of our everyday lives. The extensive use of social media does, however, raise some ethical and privacy issues that must be resolved. The influence of social media use and data collecting on user rights, the accountability of social media businesses, and the need for improved regulation are all topics that will be covered in this article.

Effect on Individual Privacy:

Social networking sites gather tons of personal data from their users, including delicate information like search history, location data, and even health data. Each user's detailed profile may be created with this data and sold to advertising or used for other reasons. Concerns regarding the privacy of personal information might arise because social media businesses can use this data to target users with customized adverts.

Additionally, individuals might need to know how much their personal information is being gathered and exploited. Data breaches or the unauthorized sharing of personal information with other parties may result in instances where sensitive information is exposed. Users should be aware of the privacy rules of social media firms and take precautions to secure their data.

Responsibility of Social Media Companies:

Social media firms should ensure that they responsibly and ethically gather and use user information. This entails establishing strong security measures to safeguard sensitive information and ensuring users are informed of what information is being collected and how it is used.

Many social media businesses, nevertheless, have come under fire for not upholding these obligations. For instance, the Cambridge Analytica incident highlighted how Facebook users' personal information was exploited for political objectives without their knowledge. This demonstrates the necessity of social media corporations being held responsible for their deeds and ensuring that they are safeguarding the security and privacy of their users.

Better Regulation Is Needed

There is a need for tighter regulation in this field, given the effect, social media has on individual privacy as well as the obligations of social media firms. The creation of laws and regulations that ensure social media companies are gathering and using user information ethically and responsibly, as well as making sure users are aware of their rights and have the ability to control the information that is being collected about them, are all part of this.

Additionally, legislation should ensure that social media businesses are held responsible for their behavior, for example, by levying fines for data breaches or the unauthorized use of personal data. This will provide social media businesses with a significant incentive to prioritize their users' privacy and security and ensure they are upholding their obligations.

In conclusion, social media has fundamentally changed how we engage and communicate with one another, but this increased convenience also raises several ethical and privacy issues. Essential concerns that need to be addressed include the effect of social media on individual privacy, the accountability of social media businesses, and the requirement for greater regulation to safeguard user rights. We can make everyone's online experience safer and more secure by looking more closely at these issues.

In conclusion, social media is a complex and multifaceted topic that has recently captured the world's attention. With its ever-growing influence on our lives, it's no surprise that it has become a popular subject for students to explore in their writing. Whether you are writing an argumentative essay on the impact of social media on privacy, a persuasive essay on the role of social media in politics, or a descriptive essay on the changes social media has brought to the way we communicate, there are countless angles to approach this subject.

However, writing a comprehensive and well-researched essay on social media can be daunting. It requires a thorough understanding of the topic and the ability to articulate your ideas clearly and concisely. This is where Jenni.ai comes in. Our AI-powered tool is designed to help students like you save time and energy and focus on what truly matters - your education. With Jenni.ai , you'll have access to a wealth of examples and receive personalized writing suggestions and feedback.

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Social media: a golden goose for scammers

Scammers are hiding in plain sight on social media platforms and reports to the FTC’s Consumer Sentinel Network point to huge profits. One in four people who reported losing money to fraud since 2021 said it started on social media. [1] Reported losses to scams on social media during the same period hit a staggering $2.7 billion, far higher than any other method of contact. And because the vast majority of frauds are not reported, this figure reflects just a small fraction of the public harm. [2]

Social media gives scammers an edge in several ways. They can easily manufacture a fake persona, or hack into your profile, pretend to be you, and con your friends. They can learn to tailor their approach from what you share on social media. And scammers who place ads can even use tools available to advertisers to methodically target you based on personal details, such as your age, interests, or past purchases. All of this costs them next to nothing to reach billions of people from anywhere in the world.

Reports show that scams on social media are a problem for people of all ages, but the numbers are most striking for younger people. In the first six months of 2023, in reports of money lost to fraud by people 20-29, social media was the contact method more than 38% of the time.  For people 18-19, that figure was 47%. [3] The numbers decrease with age, consistent with generational differences in social media use. [4]

The most frequently reported fraud loss in the first half of 2023 was from people who tried to buy something marketed on social media, coming in at a whopping 44% of all social media fraud loss reports. Most of these reports are about undelivered goods, with no-show clothing and electronics topping the list. [5] According to reports, these scams most often start with an ad on Facebook or Instagram. [6]  

While online shopping scams have the highest number of reports, the largest share of dollar losses are to scams that use social media to promote fake investment opportunities. [7] In the first six months of 2023, more than half the money reported lost to fraud on social media went to investment scammers. To draw people in, these scammers promote their own supposed investment success, often trying  to lure people to investment websites and apps that turn out to be bogus. They make promises of huge returns, and even make it look like an “investment” is growing. But if people invest, and reports say it’s usually in cryptocurrency, [8] they end up empty handed.

After investment scams, reports point to romance scams as having the second highest losses on social media. In the first six months of 2023, half of people who said they lost money to an online romance scam said it began on Facebook, Instagram, or Snapchat. [9] These scams often start with a seemingly innocent friend request from a stranger followed by love bombing and the inevitable request for money. 

Here are some ways to steer clear of scams on social media:

• Limit who can see your posts and information on social media. All platforms collect information about you from your activities on social media, but visit  your privacy settings to set some restrictions.
• If you get a message from a friend about an opportunity or an urgent need for money, call them. Their account may have been hacked—especially if they ask you to pay by cryptocurrency, gift card, or wire transfer. That’s how scammers ask you to pay.
• If someone appears on your social media and rushes you to start a friendship or romance, slow down. Read about  romance scams . And never send money to someone you haven’t met in person.
• Before you buy,  check out the company . Search online for its name plus “scam” or “complaint.”

[1] This figure excludes reports that did not specify a contact method. Including reports directly to the FTC and reports provided by Sentinel data contributors, 257,945 reports about money lost to fraud originating on social media were filed from January 2021 through June 2023.

[2] See Anderson, K. B.,  To Whom Do Victims of Mass-Market Consumer Fraud Complain?  at 1 (May 2021),  available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3852323  (study showed only 4.8% of people who experienced mass-market consumer fraud complained to a Better Business Bureau or a government entity).

[3] These figures exclude reports that did not specify a contact method and reports that did not include age information.

[4] See Pew Research Center, Social Media Use in 2021 (April 2021), available at  https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/ (study showed people ages 18-29 reported the highest social media use at 84%, followed by ages 30-49 at 81%, ages 50-64 at 73% and 65 and over at 45%). In the first 6 months of 2023, the share of loss reports indicating social media as the contact method by age was as follows: 47% (18-19), 38% (20-29), 32% (30-39), 28% (40-49), 26% (50-59), 21% (60-69), 15% (70-79), 9% (80 and over). Social media was the top contact method ranked by fraud loss reports for all age groups under age 70, while phone call was the top contact method for the 70-79 and 80 and over age groups.

[5] The top undelivered items were identified by hand-coding a random sample of 400 reports that contained a narrative description identifying the items ordered.

[6] In the first 6 months of 2023, people reported undelivered merchandise in 61% of loss reports about online shopping fraud originating on social media. Facebook was identified as the social media platform in 60% of these reports, and Instagram was identified in 24%. This excludes reports that did not identify a platform.

[7] The top platforms identified in these reports were Instagram (30%), Facebook (26%), WhatsApp (13%), and Telegram (9%). Reports that did not indicate a platform are excluded from these calculations.

[8] In the first 6 months of 2023, cryptocurrency was identified as the payment method in 53% of investment-related fraud reports that indicated social media as the method of contact. This excludes reports that did not specify a payment method.

[9] Facebook and Instagram were each identified in 21% of these reports, followed by Snapchat at 8%. This excludes reports that did not specify the platform, website, or app.

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Bitcoin atms: a payment portal for scammers, who’s who in scams: a spring roundup, impersonation scams: not what they used to be, iykyk: the top text scams of 2022.

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The 2024 Social Media Content Strategy Report: A Playbook for Every Network

Social media users have an insatiable appetite for content—even as the social ecosystem becomes more complex. Despite AI-generated content inundating feeds, emerging networks shaking up the landscape and algorithms transforming on a dime, consumers have never been more plugged in and eager to engage with their favorite brands on every channel.

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Key findings from the research, what you’ll find in this toolkit, lean into "edutainment".

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Politics with Michelle Grattan: Robert French on the realities of a social media age ban

Professorial Fellow, University of Canberra

Disclosure statement

Michelle Grattan does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

University of Canberra provides funding as a member of The Conversation AU.

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The Albanese government has announced it will ban children from accessing social media. This follows work by the South Australian government, which commissioned a report on implementing a ban.

That report was done by Robert French, the former High Court Chief Justice and current chancellor of the University of Western Australia. Though it was prepared as a basis for SA legislation, French has provided a model that could be used in drafting a federal law.

French joined the podcast to discuss his model.

On the ban itself, French outlines some of the reasons for the policy:

There’s no doubt a very strong argument being advanced by the proponents of the ban that the harms that come to the child come from both the information or harmful information to which a child might be exposed, but also from the very nature of the medium itself. So if you ban somebody from a particular medium because it’s addictive or has other harmful side effects, collateral damage as it were, it’s not the information so much that is being restricted.

While framing a model for SA, French says he kept the door open for federal legislation:

In devising a model for state legislation, I was very conscious of the importance of compatibility so far as possible with the existing Commonwealth legislation. And to use language very similar to that used in the Commonwealth legislation so that if there were a move in the direction of a national scheme, the proposals in the legislative model in the report could be applied or modified, as the case may be, to the national legislation, which is the National Online Safety Act.

While French acknowledges possible privacy concerns, he explains why the long-time storage of personal data would be unnecessary:

I think the question of privacy depends upon what information is handed over to whom in order to verify or assure that they are of the appropriate age range and for how long the recipient of that information is entitled to keep it. So that sort of stuff can be covered under the Privacy Act. And of course, once you’ve got a person registered as of a certain age and if, provided your subject satisfies as the same user, then the need for repeated use of the personal data should be unnecessary.

Now that the federal government has committed to legislating a ban, French says a state law wouldn’t be necessary:

The federal legislation can pretty well cover the territory. […] The constitutional powers of the Commonwealth in this area are pretty broad, and I suspect that there won’t be any need for top-up or supplementary legislation from within the states. I should make the point that [SA] does not have its legislation ready to go at this point. What it has from the report is a legislative model. And, this is an approach to how you could frame a law that would have the effect that you’re looking for.

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• Politics with Michelle Grattan
• Social media ban

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How To Recover Your Hacked Email or Social Media Account

Signs That Someone Hacked Your Account

How to get back into your hacked account, what to do after you take back control of your hacked account, why it’s so important to protect your email account.

Hackers try to take over your accounts. Some might want to steal your personal information — like your usernames and passwords, bank account numbers, or Social Security number — to commit identity theft. Others might want to spread malware or scam others. So, what types of things might tip you off that someone hacked your account and changed your password?

• You can’t log in to your account.
• You get a notification about a change to your username or password— but you didn’t make that change.
• You get a notification that someone logged into your account from a device you don’t recognize or a location you’re not at.
• Your friends or family report getting emails or messages you didn’t send, sometimes with random links or fake pleas for help or money .

First, make sure your computer security software is up to date, then run a scan. If the scan identifies suspicious software, delete it, and restart your computer. Then, follow the provider’s account recovery instructions.

After you get back into your hacked account

• take steps to secure your account
• check for signs that someone had access to your account
• notify your contacts about the hack

Take steps to secure your account

• Change your account password. Create a strong password that is hard to guess .
• Sign out of all devices. That way anyone who’s logged in to your account on another device will get kicked out.
• Turn on two-factor authentication (2FA), also known as two-step verification, if it’s available on your accounts. With 2FA, you’ll have to enter your password and something else to log in. That could be a PIN or a one-time verification code you get by text or email or from an authenticator app.
• Check your account recovery information. Make sure the recovery email addresses and phone numbers listed are ones you entered and have access to.

Check for signs that someone had access to your account

• Check your email settings to see if there are rules set up to forward emails. Delete any rules you didn’t set up, so your messages aren’t forwarded to someone else’s address.
• Check your sent folder for emails the hacker sent from your account. Look in your deleted folder for emails the hacker may have read then deleted.
• Check your social media accounts for messages the hacker posted or sent from your account, or for new friends you don’t recognize.

Tell your contacts

Send your friends a quick email or text, or post something, to let them know about the hack. Tell them not to click on links in emails from you and to ignore pleas for help or money .

Your email account is an important part of protecting your personal information online. Why? Say you forget your account password and use the password reset feature to get a new one. You get an email with a password reset link, click on it, and change your account password. All in a matter of minutes.

Now, imagine if someone hacked your email account. They could request a password reset link for any of your other accounts, get the password reset link from your inbox, change your password, and lock you out of the account.

That’s why it’s critical to protect your email account by using a strong password and turning on two-factor authentication .

Learn more about protecting yourself from hackers and other threats .

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The Albanese government’s teen social media ban is technology denialism that delays real action

When it comes to the well-being of children, the Albanese government is ignoring its commitment to 'science' and 'evidence-based solutions' in favour of cowing to loud voices and political expediency.

Sep 11, 2024

When it comes to some topics, the prime minister is happy to be guided by experts.

“I’m happy to engage in a debate about facts … because the facts and the science tells us that it simply doesn’t stack up,” said Anthony Albanese at a press conference announcing Matt Kean as the new chair of the Climate Change authority.

On climate change, the Albanese government says it will “ follow the science ”. (Whether that’s true is another matter ). Or that it will pursue “ evidence-based ” solutions, which it has also promised for other issues like gender-based violence or closing the gap. The government has leaned into this point of difference with the previous government, painting its predecessor as climate denialists and dinosaurs.

So why does this commitment to expertise and knowledge end when it comes to children and social media?

This week, Albanese said that his government will legislate a minimum age on social media, although he has not committed to an age nor a method . Not to be outdone, Opposition Leader Peter Dutton pledged to do the same thing within the first 100 days of his government. This comes following a campaign by News Corp and Nova’s Michael “Wippa” Wipfli to restrict children under 16 from using social media, as well several state premiers promising to do the same. 

This is all in the name of protecting children from nasty things online. The prime minister — sounding a lot like his predecessor, it must be said — is talking about about how kids should be on the “footy fields or netball courts” and not online, as if to suggest that talking to someone over social media isn’t a “real” experience.

You’d think, given the bipartisan and mainstream media support for it, that this policy would be effective. That it would have support from experts. And from the people it impacts and those who represent them. That it was “evidence-based” and “follows the science”.

You might assume that, but it’s not the case.

As previously covered, the vast majority of academics who research teen use of social media and the internet do not support a full ban for children. While they acknowledge there are real dangers, they also point to potential benefits. Groups representing young Australians, too, say a ban could make it harder for young people to access mental health support or to express themselves. Anti-youth bullying organisation PROJECT ROCKIT co-founder Lucy Thomas called the ban a “distraction” from real issues affecting children’s safety. 

Earlier this year, the government’s own eSafety commissioner not-so-subtly made the case against the ban — with a rather pointed plea to make sure policy was “being informed by robust and rigorous research”. A group of teens brought together for the eSafety commissioner’s youth council also made a case against the ban, saying that “social media is a tool to exercise these rights and it is important to remember that social media is neither good nor bad — but rather the intention and execution in using this tool defines its impact.” Now, as happened to the members of the government’s climate change youth advisory group , their opinions have been disregarded.

There is clearly a desire to do something about teens’ well-being. Parents are concerned about their children, and there are things we can do to improve the internet for our children (and maybe even for the rest of us). Coming up with sophisticated solutions is possible, even if it isn’t as easy as a ban. 

Instead, we have a government that is cowing to vibes and vested interests (News Corp and other Australian media companies have campaigned heavily on the social irresponsibility of tech companies during the fight over the news media bargaining code). 

We’ve got a prime minister throwing under the bus the LGBTQIA+ teens, the people with disabilities, the Aboriginal and Torres Strait Islanders, the young men, who all see real benefits from using social media according to his own government . He is playing into parents’ worst fears rather than trying to have a nuanced discussion about this topic. What’s worse is that this discussion distracts from real efforts to help children. 

In that way, it’s not that different to the fight over climate change. Replace “social media ban” with “gas-led recovery”. Instead of telling kids not to worry about climate change, it’s telling them to log off as if that will solve all their problems. It’s ignoring experts in favour of a politically expedient approach.

Simply put, the Albanese government is embracing technology denialism in order to appear like it’s helping our children, while stalling on reforms that might actually make a difference.

Do you back a ban on social media for young people? Or are there better solutions? Let us know your thoughts by writing to [email protected] . Please include your full name to be considered for publication. We reserve the right to edit for length and clarity.

About the Author

Associate Editor @cameronwilson

Cam Wilson is Crikey ’s associate editor. He previously worked as a reporter at the ABC, BuzzFeed , Business Insider and Gizmodo . He primarily covers internet culture and tech in Australia.

• Anthony Albanese
• social media
• social media ban
• young people

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Meta’s Section 230 Claim Fails in Bid to Escape Kids Harm Case

By Tonya Riley

A lawsuit accusing Meta Platforms Inc. of knowingly designing its social media platforms to addict and harm children’s mental and physical health survived the company’s motion to dismiss.

Judge Neal Kravitz on Monday shot down Meta’s argument that Section 230 of the Communications Decency Act shields it from liability for the claims, instead siding with other recent court decisions where judges have found that statute prevents liability only for third-party content, not design features.

WATCH: Will the legal cases against social media companies have the same success as tobacco litigation?

“Section 230 provides no refuge to Meta because none of the omissions-based deceptive trade practice claims seeks to treat Meta as a publisher of any particular third-party content,” Kravitz wrote for the Superior Court of the District of Columbia.

“Meta puts profits over kids’ health & safety,” Washington, D.C., Attorney General Brian Schwalb said in a post on X . “I look forward to holding them accountable in court.”

Meta didn’t immediately respond to a request for comment.

Meta’s practices violated D.C. consumer protection laws, including by using deceptive trade practices to misrepresent the platforms safety, according to the complaint filed Oct. 24, 2023. Addictive features like personalized algorithms and “infinite scroll” addict young users in order for Meta to profit at the expense of their health. The complaint cited in-house Meta studies leaked by a whistleblower that showed Meta was aware of the mental health impact it had on teenagers, including promoting eating disorders.

The decision is the latest front in the tech industry’s battle to use Section 230 as a shield for its design features. Kravitz cited a July opinion in NetChoice v. Reyes refusing the tech industry group’s claims that Section 230 made Utah’s social media verification law unlawful, writing that it didn’t implicate user-generated speech, just design features. Meta also lost bid to dismiss New Mexico’s complaint accusing the company of facilitating child exploitation.

The case is District of Columbia vs. Meta Platforms, Inc. et al., , D.C. Super. Ct., No. 2023-CAB-006550, motion to dismiss denied 9/9/24

To contact the reporter on this story: Tonya Riley in Washington at [email protected]

To contact the editor responsible for this story: Adam M. Taylor at [email protected]

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