essay on uses and abuses of facebook

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The uses and abuses of Facebook: A review of Facebook addiction

Affiliations.

• 1 School of Health Sciences, RMIT University, Melbourne, Victoria, Australia.
• 2 School of Design and Social Context, RMIT University, Melbourne, Victoria, Australia.
• PMID: 25317337
• PMCID: PMC4189307
• DOI: 10.1556/JBA.3.2014.016

Background and aims: Recent research suggests that use of social networking sites can be addictive for some individuals. Due to the link between motivations for media use and the development of addiction, this systematic review examines Facebook-related uses and gratifications research and Facebook addiction research.

Method: Searches of three large academic databases revealed 24 studies examining the uses and gratifications of Facebook, and nine studies of Facebook addiction.

Results: Comparison of uses and gratifications research reveals that the most popular mo- tives for Facebook use are relationship maintenance, passing time, entertainment, and companionship. These motivations may be related to Facebook addiction through use that is habitual, excessive, or motivated by a desire for mood alteration. Examination of Facebook addiction research indicates that Facebook use can become habitual or excessive, and some addicts use the site to escape from negative moods. However, examination of Facebook addic- tion measures highlights inconsistency in the field.

Discussion: There is some evidence to support the argument that uses and gratifications of Facebook are linked with Facebook addiction. Furthermore, it appears as if the social skill model of addiction may explain Facebook addiction, but inconsistency in the measurement of this condition limits the ability to provide conclusive arguments.

Conclusions: This paper recommends that further research be performed to establish the links between uses and gratifications and Facebook addiction. Furthermore, in order to enhance the construct validity of Facebook addiction, researchers should take a more systematic approach to assessment.

Keywords: Facebook; addiction; social networking sites; uses and gratifications.

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The Uses and Abuses of Facebook: A Review of Facebook Addiction

• September 2014
• Journal of Behavioral Addictions

• University of Melbourne

• RMIT University

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Uses and Abuses of facebook paragraph 

Facebook is one of the most popular social networking platforms in the world, with over 2.8 billion active users. It has revolutionised the way we communicate, share information, and stay connected with friends and family. However, like any other technology, Facebook has its fair share of uses and abuses.

Facebook makes it easy to stay in touch with friends and family, regardless of geographical boundaries. It allows users to send messages, share photos, videos, and updates, and even make voice and video calls, making long-distance communication more accessible and affordable.

Facebook serves as a platform for sharing news, updates, and information on a wide range of topics. Users can subscribe to pages and groups that interest them to stay informed about the latest developments. Facebook can also be used to raise awareness about important social and political issues.

Facebook can be used as a tool for education and learning. Teachers can create groups and pages to share resources and assignments with students, and students can collaborate on projects and discuss topics with classmates. Facebook can also be used to access educational content from museums, universities, and other organisations.

Facebook offers a variety of entertainment and recreational features, such as games, puzzles, and videos. Users can also connect with friends and family to play games together and share funny or interesting content.

Facebook can be used for business and marketing purposes. Businesses can create pages to promote their products and services, and connect with potential customers. Facebook can also be used to run targeted ads to reach a specific audience.

Facebook can be addictive, especially for young people. Users may spend excessive amounts of time on the platform, which can lead to problems in other areas of their lives, such as school, work, and relationships.

Facebook can be used to bully others, either directly or anonymously. This can have a serious negative impact on the victim’s mental and emotional health.

Facebook has been criticised for its role in the spread of misinformation and disinformation. This can be particularly harmful during elections and other important events.

Facebook has been criticised for its data collection and privacy practices. Users should be aware of the information that they are sharing on Facebook and take steps to protect their privacy.

Facebook can be used to commit crimes, such as fraud, stalking, and identity theft. Users should be careful about the information that they share on Facebook and the people they connect with. Facebook is a powerful tool that can be used for good or evil. It is important to be aware of the potential benefits and risks of using Facebook and to use it responsibly. We should be mindful of the time you spend on Facebook.

We should be careful about the information you share on Facebook. We should be selective about who you connect with on Facebook. We should be aware of the privacy settings on Facebook and adjust them to your comfort level. Report any suspicious or illegal activity on Facebook.

By following these tips, you can minimise the risks of using Facebook and maximise the benefits.

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How Facebook Fails 90 Percent of Its Users

Internal documents show the company routinely placing public-relations, profit, and regulatory concerns over user welfare. And if you think it’s bad here, look beyond the U.S.

Listen to more stories on audm

I n the fall of 2019 , Facebook launched a massive effort to combat the use of its platforms for human trafficking. Working around the clock, its employees searched Facebook and its subsidiary Instagram for keywords and hashtags that promoted domestic servitude in the Middle East and elsewhere. Over the course of a few weeks, the company took down 129,191 pieces of content, disabled more than 1,000 accounts, tightened its policies, and added new ways to detect this kind of behavior. After they were through, employees congratulated one another on a job well done.

It was a job well done. It just came a little late. In fact, a group of Facebook researchers focused on the Middle East and North Africa had found numerous Instagram profiles being used as advertisements for trafficked domestic servants as early as March 2018. “Indonesian brought with Tourist Visa,” one photo caption on a picture of a woman reads, in Arabic. “We have more of them.” But these profiles weren’t “actioned”—disabled or taken down—an internal report would explain, because Facebook’s policies “did not acknowledge the violation.” A year and a half later, an undercover BBC investigation revealed the full scope of the problem: a broad network that illegally trafficked domestic workers, facilitated by internet platforms and aided by algorithmically boosted hashtags. In response, Facebook banned one hashtag and took down some 700 Instagram profiles. But according to another internal report, “domestic servitude content remained on the platform.”

Not until October 23, 2019, did the hammer drop: Apple threatened to pull Facebook and Instagram from its App Store because of the BBC report. Motivated by what employees describe in an internal document as “potentially severe consequences to the business” that would result from an App Store ban, Facebook finally kicked into high gear. The document makes clear that the decision to act was not the result of new information: “Was this issue known to Facebook before BBC enquiry and Apple escalation? Yes.”

The document was part of the disclosure made to the Securities and Exchange Commission and provided to Congress in redacted form by Frances Haugen, the whistleblower and former Facebook data scientist. A consortium of more than a dozen news organizations, including The Atlantic , has reviewed the redacted versions.

Reading these documents is a little like going to the eye doctor and seeing the world suddenly sharpen into focus. In the United States, Facebook has facilitated the spread of misinformation, hate speech, and political polarization. It has algorithmically surfaced false information about conspiracy theories and vaccines, and was instrumental in the ability of an extremist mob to attempt a violent coup at the Capitol. That much is now painfully familiar.

But these documents show that the Facebook we have in the United States is actually the platform at its best. It’s the version made by people who speak our language and understand our customs, who take our civic problems seriously because those problems are theirs too. It’s the version that exists on a free internet, under a relatively stable government, in a wealthy democracy. It’s also the version to which Facebook dedicates the most moderation resources. Elsewhere, the documents show, things are different. In the most vulnerable parts of the world—places with limited internet access, where smaller user numbers mean bad actors have undue influence—the trade-offs and mistakes that Facebook makes can have deadly consequences.

A ccording to the documents, Facebook is aware that its products are being used to facilitate hate speech in the Middle East, violent cartels in Mexico, ethnic cleansing in Ethiopia, extremist anti-Muslim rhetoric in India, and sex trafficking in Dubai. It is also aware that its efforts to combat these things are insufficient. A March 2021 report notes, “We frequently observe highly coordinated, intentional activity … by problematic actors” that is “particularly prevalent—and problematic—in At-Risk Countries and Contexts”; the report later acknowledges, “Current mitigation strategies are not enough.”

Read: What Facebook did to American democracy

In some cases, employees have successfully taken steps to address these problems, but in many others, the company response has been slow and incomplete. As recently as late 2020, an internal Facebook report found that only 6 percent of Arabic-language hate content on Instagram was detected by Facebook’s systems. Another report that circulated last winter found that, of material posted in Afghanistan that was classified as hate speech within a 30-day range, only 0.23 percent was taken down automatically by Facebook’s tools. In both instances, employees blamed company leadership for insufficient investment.

In many of the world’s most fragile nations, a company worth hundreds of billions of dollars hasn’t invested enough in the language- and dialect-specific artificial intelligence and staffing it needs to address these problems. Indeed, last year, according to the documents, only 13 percent of Facebook’s misinformation-moderation staff hours were devoted to the non-U.S. countries in which it operates, whose populations comprise more than 90 percent of Facebook’s users. (Facebook declined to tell me how many countries it has users in.) And although Facebook users post in at least 160 languages, the company has built robust AI detection in only a fraction of those languages, the ones spoken in large, high-profile markets such as the U.S. and Europe—a choice, the documents show, that means problematic content is seldom detected.

The granular, procedural, sometimes banal back-and-forth exchanges recorded in the documents reveal, in unprecedented detail, how the most powerful company on Earth makes its decisions. And they suggest that, all over the world, Facebook’s choices are consistently driven by public perception, business risk, the threat of regulation, and the specter of “PR fires,” a phrase that appears over and over in the documents. In many cases, Facebook has been slow to respond to developing crises outside the United States and Europe until its hand is forced. “It’s an open secret … that Facebook’s short-term decisions are largely motivated by PR and the potential for negative attention,” an employee named Sophie Zhang wrote in a September 2020 internal memo about Facebook’s failure to act on global misinformation threats. (Most employee names have been redacted for privacy reasons in these documents, but Zhang left the company and came forward as a whistleblower after she wrote this memo.)

Sometimes, even negative attention isn’t enough. In 2019, the human-rights group Avaaz found that Bengali Muslims in India’s Assam state were “facing an extraordinary chorus of abuse and hate” on Facebook: Posts calling Muslims “pigs,” rapists,” and “terrorists” were shared tens of thousands of times and left on the platform because Facebook’s artificial-intelligence systems weren’t built to automatically detect hate speech in Assamese, which is spoken by 23 million people. Facebook removed 96 of the 213 “clearest examples” of hate speech Avaaz flagged for the company before publishing its report. Facebook still does not have technology in place to automatically detect Assamese hate speech.

In a memo dated December 2020 and posted to Workplace, Facebook’s very Facebooklike internal message board, an employee argued that “Facebook’s decision-making on content policy is routinely influenced by political considerations.” To hear this employee tell it, the problem was structural: Employees who are primarily tasked with negotiating with governments over regulation and national security, and with the press over stories, were empowered to weigh in on conversations about building and enforcing Facebook’s rules regarding questionable content around the world. “Time and again,” the memo quotes a Facebook researcher saying, “I’ve seen promising interventions … be prematurely stifled or severely constrained by key decisionmakers—often based on fears of public and policy stakeholder responses.”

Among the consequences of that pattern, according to the memo: The Hindu-nationalist politician T. Raja Singh, who posted to hundreds of thousands of followers on Facebook calling for India’s Rohingya Muslims to be shot—in direct violation of Facebook’s hate-speech guidelines—was allowed to remain on the platform despite repeated requests to ban him, including from the very Facebook employees tasked with monitoring hate speech. A 2020 Wall Street Journal article reported that Facebook’s top public-policy executive in India had raised concerns about backlash if the company were to do so, saying that cracking down on leaders from the ruling party might make running the business more difficult. The company eventually did ban Singh, but not before his posts ping-ponged through the Hindi-speaking world.

In a Workplace thread apparently intended to address employee frustration after the Journal article was published, a leader explained that Facebook’s public-policy teams “are important to the escalations process in that they provide input on a range of issues, including translation, socio-political context, and regulatory risks of different enforcement options.”

Adrienne LaFrance: The largest autocracy on Earth

Employees weren’t placated. In dozens and dozens of comments, they questioned the decisions Facebook had made regarding which parts of the company to involve in content moderation, and raised doubts about its ability to moderate hate speech in India. They called the situation “sad” and Facebook’s response “inadequate,” and wondered about the “propriety of considering regulatory risk” when it comes to violent speech.

“I have a very basic question,” wrote one worker. “Despite having such strong processes around hate speech, how come there are so many instances that we have failed? It does speak on the efficacy of the process.”

Two other employees said that they had personally reported certain Indian accounts for posting hate speech. Even so, one of the employees wrote, “they still continue to thrive on our platform spewing hateful content.”

We “cannot be proud as a company,” yet another wrote, “if we continue to let such barbarism flourish on our network.”

T aken together, Frances Haugen’s leaked documents show Facebook for what it is: a platform racked by misinformation, disinformation, conspiracy thinking, extremism, hate speech, bullying, abuse, human trafficking, revenge porn, and incitements to violence. It is a company that has pursued worldwide growth since its inception—and then, when called upon by regulators, the press, and the public to quell the problems its sheer size has created, it has claimed that its scale makes completely addressing those problems impossible. Instead, Facebook’s 60,000-person global workforce is engaged in a borderless, endless, ever-bigger game of whack-a-mole, one with no winners and a lot of sore arms.

Sophie Zhang was one of the people playing that game. Despite being a junior-level data scientist, she had a knack for identifying “coordinated inauthentic behavior,” Facebook’s term for the fake accounts that have exploited its platforms to undermine global democracy, defraud users, and spread false information. In her memo, which is included in the Facebook Papers but was previously leaked to BuzzFeed News , Zhang details what she found in her nearly three years at Facebook: coordinated disinformation campaigns in dozens of countries, including India, Brazil, Mexico, Afghanistan, South Korea, Bolivia, Spain, and Ukraine. In some cases, such as in Honduras and Azerbaijan, Zhang was able to tie accounts involved in these campaigns directly to ruling political parties. In the memo, posted to Workplace the day Zhang was fired from Facebook for what the company alleged was poor performance, she says that she made decisions about these accounts with minimal oversight or support, despite repeated entreaties to senior leadership. On multiple occasions, she said, she was told to prioritize other work.

Facebook has not disputed Zhang’s factual assertions about her time at the company, though it maintains that controlling abuse of its platform is a top priority. A Facebook spokesperson said that the company tries “to keep people safe even if it impacts our bottom line,” adding that the company has spent $13 billion on safety since 2016. “​​Our track record shows that we crack down on abuse abroad with the same intensity that we apply in the U.S.”

Zhang's memo, though, paints a different picture. “We focus upon harm and priority regions like the United States and Western Europe,” she wrote. But eventually, “it became impossible to read the news and monitor world events without feeling the weight of my own responsibility.” Indeed, Facebook explicitly prioritizes certain countries for intervention by sorting them into tiers, the documents show. Zhang “chose not to prioritize” Bolivia, despite credible evidence of inauthentic activity in the run-up to the country’s 2019 election. That election was marred by claims of fraud, which fueled widespread protests; more than 30 people were killed and more than 800 were injured.

Read: Facebook’s id is showing

“I have blood on my hands,” Zhang wrote in the memo. By the time she left Facebook, she was having trouble sleeping at night. “I consider myself to have been put in an impossible spot—caught between my loyalties to the company and my loyalties to the world as a whole.”

I n February, just over a year after Facebook’s high-profile sweep for Middle Eastern and North African domestic-servant trafficking, an internal report identified a web of similar activity, in which women were being trafficked from the Philippines to the Persian Gulf, where they were locked in their homes, denied pay, starved, and abused. This report found that content “should have been detected” for violating Facebook’s policies but had not been, because the mechanism that would have detected much of it had recently been made inactive. The title of the memo is “Domestic Servitude: This Shouldn’t Happen on FB and How We Can Fix It.”

What happened in the Philippines—and in Honduras, and Azerbaijan, and India, and Bolivia—wasn’t just that a very large company lacked a handle on the content posted to its platform. It was that, in many cases, a very large company knew what was happening and failed to meaningfully intervene.

That Facebook has repeatedly prioritized solving problems for Facebook over solving problems for users should not be surprising. The company is under the constant threat of regulation and bad press. Facebook is doing what companies do, triaging and acting in its own self-interest.

But Facebook is not like other companies. It is bigger, and the stakes of its decisions are higher. In North America, we have recently become acutely aware of the risks and harms of social media. But the Facebook we see is the platform at its best. Any solutions will need to apply not only to the problems we still encounter here, but also to those with which the other 90 percent of Facebook’s users struggle every day.

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Essay on Social Media for School Students and Children

500+ words essay on social media.

Social media is a tool that is becoming quite popular these days because of its user-friendly features. Social media platforms like Facebook, Instagram, Twitter and more are giving people a chance to connect with each other across distances. In other words, the whole world is at our fingertips all thanks to social media. The youth is especially one of the most dominant users of social media. All this makes you wonder that something so powerful and with such a massive reach cannot be all good. Like how there are always two sides to a coin, the same goes for social media. Subsequently, different people have different opinions on this debatable topic. So, in this essay on Social Media, we will see the advantages and disadvantages of social media.

Advantages of Social Media

When we look at the positive aspect of social media, we find numerous advantages. The most important being a great device for education . All the information one requires is just a click away. Students can educate themselves on various topics using social media.

Moreover, live lectures are now possible because of social media. You can attend a lecture happening in America while sitting in India.

Furthermore, as more and more people are distancing themselves from newspapers, they are depending on social media for news. You are always updated on the latest happenings of the world through it. A person becomes more socially aware of the issues of the world.

In addition, it strengthens bonds with your loved ones. Distance is not a barrier anymore because of social media. For instance, you can easily communicate with your friends and relatives overseas.

Most importantly, it also provides a great platform for young budding artists to showcase their talent for free. You can get great opportunities for employment through social media too.

Another advantage definitely benefits companies who wish to promote their brands. Social media has become a hub for advertising and offers you great opportunities for connecting with the customer.

Get the huge list of more than 500 Essay Topics and Ideas

Disadvantages of Social Media

Despite having such unique advantages, social media is considered to be one of the most harmful elements of society. If the use of social media is not monitored, it can lead to grave consequences.

Thus, the sharing on social media especially by children must be monitored at all times. Next up is the addition of social media which is quite common amongst the youth.

This addiction hampers with the academic performance of a student as they waste their time on social media instead of studying. Social media also creates communal rifts. Fake news is spread with the use of it, which poisons the mind of peace-loving citizens.

In short, surely social media has both advantages and disadvantages. But, it all depends on the user at the end. The youth must particularly create a balance between their academic performances, physical activities, and social media. Excess use of anything is harmful and the same thing applies to social media. Therefore, we must strive to live a satisfying life with the right balance.

FAQs on Social Media

Q.1 Is social media beneficial? If yes, then how?

A.1 Social media is quite beneficial. Social Media offers information, news, educational material, a platform for talented youth and brands.

Q.2 What is a disadvantage of Social Media?

A.2 Social media invades your privacy. It makes you addicted and causes health problems. It also results in cyberbullying and scams as well as communal hatred.

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• v.17; 2023 Jun

Social media use and abuse: Different profiles of users and their associations with addictive behaviours

Deon tullett-prado.

a Victoria University, Australia

Vasileios Stavropoulos

b University of Athens, Greece

Rapson Gomez

c Federation University, Australia

Associated Data

The data is made available via a link document.

Introduction

Social media use has become increasingly prevalent worldwide. Simultaneously, concerns surrounding social media abuse/problematic use, which resembles behavioural and substance addictions, have proliferated. This has prompted the introduction of ‘Social Media Addiction’ [SMA], as a condition requiring clarifications regarding its definition, assessment and associations with other addictions. Thus, this study aimed to: (a) advance knowledge on the typology/structure of SMA symptoms experienced and: (b) explore the association of these typologies with addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

A sample of 968 [Mage = 29.5, SDage = 9.36, nmales = 622 (64.3 %), nfemales = 315, (32.5 %)] adults was surveyed regarding their SMA experiences, using the Bergen Social Media Addiction Scale (BSMAS). Their experiences of Gaming, Internet, Gambling, Alcohol, Cigarette, Drug, Sex, Shopping and Exercise addictions were additionally assessed, and latent profile analysis (LPA) was implemented.

Three distinct profiles were revealed, based on the severity of one’s SMA symptoms: ‘low’, ‘moderate’ and ‘high’ risk. Subsequent ANOVA analyses suggested that participants classified as ‘high’ risk indicated significantly higher behaviours related to internet, gambling, gaming, sex and in particular shopping addictions.

Conclusions

Results support SMA as a unitary construct, while they potentially challenge the distinction between technological and behavioural addictions. Findings also imply that the assessment of those presenting with SMA behaviours, as well as prevention and intervention targeting SMA at risk groups, should consider other comorbid addictions.

1. Introduction

Social media – a form of online communication in which users create profiles, generate and share content, while forming online social networks/communities ( Obar & Wildman, 2015 ), is quickly growing to become almost all consuming in the media landscape. Currently the number of daily social media users exceeds 53 % (∼4.5 billion users) of the global population, approaching 80 % among more developed nations ( Countrymeters, 2021 , DataReportal, 2021 ). Due to technological advancements, the rise of ‘digital natives’ (i.e. children and adolescents raised with and familiarised with digital technology) and coronavirus pandemic triggered lockdowns, the frequency and duration of social media usage has been steadily increasing as people compensate for a lack of face to face interaction or grow with Social Media as a normal part of their lives (i.e. ∼ 2 h and 27 min average daily; DataReportal, 2021 , Heffer et al., 2019 , Zhong et al., 2020 , Nguyen, 2021 ). Furthermore, social media is increasingly involved in various domains of life including education, economics and even politics, to the point where engagement with the economy and wider society almost necessitates its use, driving the continued proliferation of social media use ( Calderaro, 2018 , Nguyen, 2021 , Mabić et al., 2020 , Mourão and Kilgo, 2021 ). This societal shift towards increased social media use has had some positive benefits, serving to facilitate the creation and maintenance of social groups, increase access to opportunities for career advancement and created wide ranging and accessible education options for many users ( Calderaro, 2018 , Prinstein et al., 2020 , Bouchillon, 2020 , Nguyen, 2021 ). However, for a minority of users - roughly 5–10 % ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 ) – social media use has become excessive, to the point where it dominates one’s life, similarly to an addictive behaviour - a state known as 'problematic social media use' ( Sun & Zhang, 2020 ). For these users, social media is experienced as the single most important activity in one’s life, while compromising their other roles and obligations (e.g. family, romance, employment; Sun and Zhang, 2020 , Griffiths and Kuss, 2017 ). This is a situation associated with low mood/depression, the compromise of one’s identity, social comparison leading to anxiety and self-esteem issues, work, academic/career difficulties, compromised sleep schedules and physical health, and even social impairment leading to isolation ( Anderson et al., 2017 , Sun and Zhang, 2020 , Gorwa and Guilbeault, 2020 ).

1.1. Problematic social media engagement in the context of addictions

Problematic social media use is markedly similar to the experience of substance addiction, thus leading to problematic social media use being modelled by some as a behavioural addiction - social media addiction (SMA; Sun and Zhang, 2020 ). In brief, an addiction loosely refers to a state where an individual experiences a powerful craving to engage with a behaviour, and inability to control their related actions, such that it begins to negatively impact their life ( Starcevic, 2016 ). Although initially the term referred to substance addictions induced by psychotropic drugs (e.g., amphetamines), it later expanded to include behavioural addictions ( Chamberlain et al., 2016 ). These reflect a fixation and lack of control, similar to those experienced in the abuse of substances, related to one’s excessive/problematic behaviours ( Starcevic, 2016 ).

Indeed, behavioural addictions, such as gaming, gambling and (arguably) social media addiction (SMA) share many common features with substance related addictions ( Zarate et al., 2022 ). Their similarities extend beyond the core addiction manifestations of fixation, loss of control and negative life consequences ( Grant et al., 2010 , Bodor et al., 2016 , Martinac et al., 2019 , Zarate et al., 2022 ). For instance, it has been evidenced that common risk factors/mechanisms (e.g., low impulse control), behavioural patterns (e.g., chronic relapse; sudden “spontaneous” quitting), ages of onset (e.g., adolescence and young adulthood) and negative life consequences (e.g., financial and legal difficulties) are similar between the so-called behavioural addictions and formally diagnosed substance addictions ( Grant et al., 2010 ). Moreover, such commonalities often accommodate the concurrent experience of addictive presentations, and/or even the substitution/flow from one addiction to the next (e.g., gambling and alcoholism; Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ).

With these features in mind, SMA has been depicted as characterized by the following six symptoms; A deep preoccupation with social media use (salience), use to either increase their positive feelings and/or buffer their negative feelings (mood modification), the requirement for progressively increasing time-engagement to get the same effect (i.e., tolerance), withdrawal symptoms such as irritability and frustration when access is reduced (withdrawal), the development of tensions with other people due to under-performance across several life domains (conflict) and reduced self-regulation resulting in an inability to reduce use (relapse; Andreassen et al., 2012 , Brown, 1993 , Griffiths and Kuss, 2017 , Sun and Zhang, 2020 ).

This developing model of SMA has been gaining popularity as the most widely used conceptualisation of problematic social media use, and guiding the development of relevant measurement tools ( Andreassen et al., 2012 , Haand and Shuwang, 2020 , Prinstein et al., 2020 ; Van den Eijnden et al., 2016) ). However, SMA is not currently uniformly accepted as an understanding of problematic social media use. Some critics have labelled the SMA model a premature pathologisation of ordinary social media use behaviours with low construct validity and little evidence for its existence, often inviting alternative proposed classifications derived by cognitive-behavioural or contextual models ( Sun & Zhang, 2020 ; Panova & Carbonell, 2018 7; Moretta, Buodo, Demetrovics & Potenza, 2022 ). Furthermore, the causes, risk factors and consequences of SMA, as well as the measures employed in its assessment have yet to be elucidated in depth, with research in the area being largely exploratory in nature ( Prinstein et al., 2020 , Sun and Zhang, 2020 ). In this context, what functional, regular and excessive social media use behaviours may involve has also been debated ( Wegmann et al., 2022 ). Thus, there is a need for further research clarifying the nature of SMA, identifying risk factors and related negative outcomes, as well as potential methods of treatment ( Prinstein et al., 2020 , Sun and Zhang, 2020 , Moretta et al., 2022 ).

Two avenues important for realizing these goals (and the focus of this study) involve: a) profiling SMA behaviours in the broader community, and b) decoding their associations with other addictions. Profiling these behaviours would involve identifying groups of people with particular patterns of use rather than simply examining trends in behaviour across the greater population. This would allow for clearer understandings of the ways in which different groups experience SMA and a more person-centred analysis (i.e., focused on finer understandings of personal experiences, Bányai et al., 2017 ). Moreover, when combined with analyses of association, it can allow for assertions not only about whether SMA associates with a variable, but about which components of the experience of SMA associate with a variable, allowing for more nuanced understandings. One such association with much potential for exploration, is that of SMA with other addictions (i.e., how does a certain SMA type differentially relate with other addictive behaviors, such as gambling and/or substance abuse?). Such knowledge would be useful, due to the shared common features and risk factors between addictions. It would allow for a greater understanding of the likelihood of comorbid addictions, or of flow from one addiction to the next ( Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ). However, the various links between different addictions are not identical, with alcoholism (for example) associating less strongly with excessive/problematic internet use than with problematic/excessive (so called “addictive) sex behaviours ( Grant et al., 2010 ). In that line, some studies have suggested the consideration of different addiction subgroups (e.g., substance, behavioural and technology addictions Marmet et al., 2019 ), and/or different profiles of individuals being prone to manifest some addictive behaviours more than others ( Zilberman et al., 2018 ). Accordingly, one may assume that distinct profiles of those suffering from SMA behaviours may be more at risk for certain addictions over others, rather than with addictions in general ( Zarate et al., 2022 ).

Understanding these varying connections could be vital for SMA treatment. Co-occurring addictions often reinforce each-other through their behavioural effects. Furthermore, by targeting only a single addiction type in a treatment, other addictions an individual is vulnerable to can come to the fore ( Grant et al., 2010 , Miller et al., 2019 ). Thus, a holistic view of addictive vulnerability may require consideration ( Grant et al., 2010 , Miller et al., 2019 ). This makes the identification of individual SMA profiles, as well as any potential co-occurring addictions, pivotal for more efficient assessment, prevention and intervention of SMA behaviours.

To the best of the authors’ knowledge, four studies to date have attempted to explore SMA profiles. Three of those have been conducted predominantly with European adolescent samples, and varied in terms of the type and number of profiles detected ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ). The fourth was conducted with English speaking adults from the United Kingdom and the United States ( Cheng et al., 2022 ). Of extant studies, Bányai et al. (2017) identified three profiles varying quantitively (i.e., in terms of their SMA symptoms’ severity) across a low, moderate and high range. In contrast, Brailovskaia et al., 2021 , Luo et al., 2021 identified four and five profiles that varied both quantitatively and qualitatively in terms of the type of SMA symptoms reported. Brailovskaia et al., (2021) proposed the ‘low symptom’, ‘low withdrawal’ (i.e., lower overall SMA symptoms with distinctively lower withdrawal), ‘high withdrawal’ (i.e., higher overall SMA symptoms with distinctively higher withdrawal) and ‘high symptom’ profiles. Luo et al. (2021) supported the ‘casual’, ‘regular’, ‘low risk high engagement’, ‘at risk high engagement’ and ‘addicted’ user profiles, which demonstrated progressively higher SMA symptoms severity alongside significant differences regarding mood modification, relapse, withdrawal and conflict symptoms, that distinguished the low and high risk ‘high engagement’ profiles. Finally, considering the occurrence of different SMA profiles in adults, Cheng and colleagues, (2022), supported the occurrence of ‘no-risk’, ‘at risk’ and ‘high risk’ social media users applying in both US and UK populations, with the UK sample showing a lower proportion of the ‘no-risk’ profile (i.e. UK = 55 % vs US = 62.2) and a higher percentage of the high risk profile (i.e. UK = 11.9 % vs US = 9.1 %). Thus, considering the number of identified profiles best describing the population of social media users, Cheng and colleagues’ findings (2022) were similar to Bányai and colleagues’ (2017) suggestions for SMA behaviour profiles of adolescents. At this point it should be noted, that none of the four studies exploring SMA behaviours profiles to date has taken into consideration different profile parameterizations, meaning that potential differences in the heterogeneity/ variability of those classified within the same profile were not considered (e.g. some profiles maybe more loose/ inclusive than others; Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ).

The lack of convergence regarding the optimum number and the description of SMA profiles occurring, as well as age, cultural and parameterization limitations of the four available SMA profiling studies, invites further investigation. This is especially evident in light of preliminary evidence confirming one’s SMA profile may link more to certain addictions over others ( Zarate et al., 2022 ). Indeed, those suffering from SMA behaviours have been shown to display heightened degrees of alcohol and drug use, a vulnerability to internet addiction in general, while presenting lower proneness towards exercise addiction and tobacco use ( Grant et al., 2010 , Anderson et al., 2017 , Duradoni et al., 2020 , Spilkova et al., 2017 ). In terms of gambling addiction, social media addicts display similar results on tests of value-based decision making as gambling addicts ( Meshi et al., 2019 ). Finally, regarding shopping addiction, the proliferation of advertisements for products online, and the ease of access via social media to online stores could be assumed to have an intensifying SMA effect ( Rose & Dhandayudham, 2014 ). Aside from these promising, yet relatively limited findings, the assessed connections between SMA and other addictions tend to be either addressed in isolation (e.g., SMA with gambling only and not multiple other addiction forms; Gainsbury et al., 2016a , Gainsbury et al., 2016b ) and in a variable (and not person) focused manner (e.g., higher levels of SMA relate with higher levels of drug addiction; Spilkova et al., 2017 ), which overlooks an individual’s profile. These profiles are vitally needed, as knowing the type of individual who may experience a series of disparate addictions is paramount for identifying at risk social media users and populations in need of more focused prevention/intervention programs ( Grant et al., 2010 ). Hence, using person focused methods such as latent profile(s) analysis (LPA) that address the ways in which distinct variations/profiles in SMA behaviours may occur, and how these relate with other addictions is imperative ( Lanza & Cooper, 2016 ).

1.2. Present study

To address this research priority, while considering SMA behaviours as being normally distributed (i.e., a minimum–maximum continuum) across the different profiles of users in the general population, the present Australian study uses a large community sample, solid psychometric measures and a sequence of differing in parameterizations LCA models aiming to: (a) advance past knowledge on the typology/structure of SMA symptom one experiences and: (b) innovatively explore the association of these typologies with a comprehensive list of addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

Based on Cheng and colleagues (2022) and Bányai and colleagues (2017), it was envisaged that three profiles arrayed in terms of ascending SMA symptoms’ severity would be likely identified. Furthermore, guided by past literature supporting closer associations between technological and behavioural addictions than with substance related addictions, it was hypothesized that those classified at higher SMA risk profiles would report higher symptoms of other technological and behavioural addictions, such as those related to excessive gaming and gambling, than with drug addiction ( Chamberlain and Grant, 2019 , Zarate et al., 2022 ).

2.1. Participants

The current study was conducted in Australia. Responses initially retrieved included 1097 participants. Of those, 129 were not considered for the current analyses. In particular, 84 respondents were classified as preview-only registrations and did not address any items, 5 presented with systematic response inconsistencies, and thus were considered invalid, 11 were excluded as potential bots, 11 had not provided their informed consent (i.e., did not tick the digital consent box, although they later addressed the survey), and 18 were taken out for not fulfilling age conditions (i.e., being adults), in line with the ethics approval received. Therefore, responses from 968 English-speaking adults from the general community were examined. An online sample of adult, English speaking participants aged 18 to 64 who were familiar with gaming [ N  = 968, M age  = 29.5, SD age  = 9.36, n males  = 622 (64.3 %), n females  = 315, (32.5 %), n trans/non-binary  = 26 (2.7 %), n queer  =  1 (0.1 %), n other  =  1 (0.1 %), n missing  =  3 (0.3 %)] was analysed. According to Hill (1998) random sampling error is required to lie below 4 %, that is satisfied by the current sample’s 3 % (SPH analytics, 2021). See Table 1 for participants’ sociodemographic information.

Socio-demographic and online use characteristics of participants.

Note: Percentages represent the percentage of that sex which is represented by any one grouping, rather than percentages of the overall population.

2.2. Measures

Psychometric instruments targeting sociodemographics, SMA and a semi-comprehensive range of behavioral, digital and substance addictions were employed. These instruments involved the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2012 ), the Internet Gaming Disorder 9 items Short Form (IGDS-SF9; Pontes & Griffiths, 2015 ), The Internet Disorder Scale (IDS9-SF; ( Pontes & Griffiths, 2016 ), the Online Gambling Disorder Questionnaire (IGD-Q; González-Cabrera et al., 2020 ), the 10-Item Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993 , the Five Item Cigarette Dependance Scale (CDS-5; Etter et al., 2003 ), the 10- item Drug Abuse Screening Test (DAST-10; Skinner, 1982 ), the Bergen-Yale Sex Addiction Scale (BYSAS; Andreassen et al., 2018), the Bergen Shopping Addiction Scale (BSAS; Andreassen et al., 2015) and the 6-item Revised Exercise Addiction Inventory (EAI-R; Szabo et al., 2019 ). Precise details of these measures, including values related to assumptions can be found in Table 2 .

Measure descriptions and internal consistency.

Note Table 2 : Streiner’s (2003) guidelines are used when measuring internal reliability, with Cronbachs Alpha scores in the range of 0.60–0.69 labelled ‘acceptable’, ranges between 0.70 and 0.89 labelled ‘good’ and ranges between 0.90 and 1.00 labelled ‘excellent’. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 ( Brown, 2006 ). OGD-G kurtosis (13.90) and skewness (3.45) exceeded the recommended limits ( Brown, 2006 ). However, LPA does not assume data distribution linearity, normality and or homogeneity ( Rosenberg et al., 2019 ). Considering aim B, related to detecting significant reported differences on measures for gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively, anova results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong, Saminathan, & Chang, 2016 ).

2.3. Procedure

Approval was received from the Victoria University Human Research Ethics Committee (HRE20-169). Data was collected in August 2019 to August 2020 via an online survey link distributed via social media (i.e., Facebook; Instagram; Twitter), digital forums (i.e. reddit) and the Victoria University learning management system. Familiarity with gaming was preferred, so that associations with one’s online gaming patterns were studied. The link first took potential participants to the Plain Language Information Statement (PLIS) which informed on the study requirements and participants’ anonymity and free of penalty withdrawal rights. Digital provision of informed consent (i.e., ticking a box) was required by the participants before proceeding to the survey.

2.4. Statistical analyses

Statistical analyses were conducted via: a) R-studio for the latent profile(s) analyses (LPA) and; b) Jamovi for descriptive statistics and profiles’ comparisons. Regarding aim A, LPA identified naturally homogenous subgroups within a population ( Rosenberg et al., 2019 ). Through the TIDYLPA CRAN R package, a number of models varying in terms of their structure/parameterization and the number of ‘profiles’ were tested using the six BSMAS criteria/items as indicators ( Rosenberg et al., 2019 ; see Table 3 ).

LCA model parameterization characteristics.

Subsequently, the constructed models were compared regarding selected fit indices (i.e., Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), bootstrapped Lo-Mendel Rubin test (B-LMR or LRT), entropy and the N_Min; Rosenberg et al., 2019 ) 1 . This involved 1: Dismissing any models with N -Min’s equalling 0, as each profile requires at least one participant, 2: Dismissing models with entropy scores below 0.64 ( Tein et al., 2013 ), 3: Dismissing models with nonsignificant BLMR value, and 4: assessing the remaining models on their AIC/BIC looking for an elbow point in the decline or the lowest values.

Regarding aim B of the study, ANOVA with bootstrapping (1000x) was employed to detect significant profile differences regarding one’s gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively.

All analyses’ assumptions were met with one exception 2 . The measure of Online Gambling disorder experience violated guidelines for the acceptable departure from normality and homogeneity ( Kim, 2013 ). Given this violation, results regarding gambling addiction should be considered with some caution.

3.1. Aim A: LPA of BSMAS symptoms

The converged models’ fit, varying by number of profiles and parametrization is displayed in Table 4 , with the CIP parameterisation presenting as the optimum (i.e. lower AIC and BIC, and 1–8 profiles converging; all CVDP, CIUP, CVUP models did not converge except the CVUP one profile). Subsequently, the CIP models were further examined via the TIDYLPA Mclust function (see Table 5 ). AIC and BIC decreased as the number of profiles increased. This flattened past 3 profiles (i.e., elbow point; Rosenberg et al., 2019 ). Furthermore, past 3 profiles, N -min reached zero, indicating profiles with zero participants in them – thus reducing interpretability. Lastly, the BLRT test reached non significance once the model had 4 profiles, again indicating the 3-profile model as best fitting. Therefore, alternative CIP -models were rejected in favour of the 3-profile one. This displayed a level of classification accuracy well above the suggested cut off point of 0.76 (entropy = 0.90; Larose et al., 2016 ), suggesting over 90 % correct classification ( Larose et al., 2016 ). Regarding the profiles’ proportions, counts revealed 33.6 % as profile 1, 52.4 % as profile 2, 14 % as profile 3.

Initial model testing.

Fit indices of cip models with 1–8 classes.

Table 6 and Fig. 1 present the profiles’ raw mean scores across the 6 BSMAS items whilst Table 7 and Fig. 2 present the standardised mean scores.

Raw Mean Scores and Standard Error of the 6 BSMAS Criteria Across the Three Classes/Profiles.

Raw symptom experience of the three classes.

Standardised mean scores of the 6 bsmas criteria Across the Three Classes/Profiles.

Note: For standard errors, see Table 6 .

Standardized symptom experience of the three classes.

Profile 1 scores varied from 1.74 to 2.98 raw and between 0.08 and 0.58 standard deviations above the sample mean symptom experience. In terms of plateaus and steeps, profile 1 displayed a raw score plateaus across symptoms 1–3 (salience, tolerance, mood modification), a decline in symptom 4 (relapse), and another plateau across symptoms 5–6 (withdrawal and conflict). It further displayed a standardized score plateau around the level of 0.5 standard deviations across symptoms 1–3 and a decline across symptoms 4–6. Profile 2 varied consistently between raw mean scores of 1 and 1.36 across the 6 SMA symptoms, and between −0.74 and −0.53 standard deviations from the sample mean with general plateaus in standardized score across symptoms 1–3 and 4–6. Finally, profile 3 mean scores varied between 3.02 and 3.95 raw and 1.26 to 1.88 standardized. Plateaus were witnessed in the raw scores across symptoms 1–3 (salience, tolerance, mood modification), a decline at symptom 4 (relapse), a relative peak at symptom 5 (withdrawal), and a further decline across symptom 6 (conflict). However, the standardized scores for profile 3 were relatively constant across the first four symptoms, before sharply reaching a peak at symptom 5 and then declining once more. Accordingly, the three profiles were identified as severity profiles ‘Low’ (profile 2), ‘Moderate’ (profile 1) and ‘High’ (profile 3) risk. Table 8 , Table 9 provide the profile means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed.

Post Hoc Descriptives across a semi-comprehensive list of addictions.

Post Hoc Comparisons of the SMA profiles revealed across the addictive behaviors measured.

3.2. Aim 2: BSMAS profiles and addiction risk/personal factors.

Table 8 , Table 9 display the Jamovi outputs for the BSMAS profiles and their means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed using ANOVA. Cohen’s (1988) benchmarks were used for eta squared values, with > 0.01 indicating small, >0.059 medium and > 0.138 large effects. ANOVA results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong et al., 2016 ). SMA profiles significantly differed across the range of behavioral addiction forms examined with more severe SMA profiles presenting consistently higher scores with a medium effect size regarding gaming ( F  = 57.5, p  <.001, η 2  = 0.108), sex ( F  = 39.53, p  <.001, η 2  = 0.076) and gambling ( F  = 40.332, p  <.001, η 2  = 0.078), and large effect sizes regarding shopping ( F  = 90.06, p  <.001, η 2  = 0.159) and general internet addiction symptoms ( F  = 137.17, p  <.001, η2 = 0.223). Only relationships of ‘medium’ size or greater were considered further in this analysis, though small effects were found with alcoholism ( F  = 11.34, p  <.001, η 2  = 0.023), substance abuse ( F  = 4.83, p  =.008, η 2  = 0.01) and exercise addiction ( F  = 5.415, p  =.005, η2 = 0.011). Pairwise comparisons consistently confirmed that the ‘low’ SMA profile scored significantly lower than the ‘moderate’ and the ‘high’ SMA profile’, and the ‘moderate’ SMA profile being significantly lower than the ‘high’ SMA profile across all addiction forms assessed (see Table 8 , Table 9 ).

4. Discussion

The present study examined the occurrence of distinct SMA profiles and their associations with a range of other addictive behaviors. It did so via uniquely combining a large community sample, measures of established psychometric properties addressing both SMA and an extensive range of other proposed substance and behavioral addictions, to calculate the best fitting model in terms of parameterization and profile number. A model of the CIP parameterization with three profiles was supported by the data. The three identified SMA profiles ranged in terms of severity and were labeled as ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk. Membership of the ‘high’ SMA risk profile was shown to link with significantly higher reported experiences of Internet and shopping addictive behaviours, and moderately with higher levels of addictive symptoms related to gaming, sex and gambling.

4.1. Number and variations of SMA profiles

Three SMA profiles, entailing ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’(14 %) SMA risk were supported, with symptom 5 – withdrawal – displaying the highest inter-profile disparities. These results help clarify the number of SMA profiles in the population, as past findings were inconsistent supporting either 3, or 4 or 5 SMA profiles ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 ), as well as the nature of the differences between these profiles (i.e. quantitative: “how much/high one experiences SMA symptoms” or qualitative: “the type of SMA symptoms one experiences”). Our findings are consistent with the findings of Bányai and colleagues (2017) and Cheng and colleagues (2022) indicating a unidimensional experience of SMA (i.e., that the intensity/severity an individual reports best defines their profile membership, rather than the type of SMA symptoms) with three profiles ranging in severity from ‘low’ to ‘moderate’ to ‘high’ and those belonging at the higher risk profiles being the minority. Conversely, these results stand in opposition with two past studies identifying profiles that varied qualitatively (i.e., specific SMA symptoms experienced more by certain profiles) and suggesting the occurrence of 4 and 5 profiles respectively ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Such differences might be explained by variations in the targeted populations of these studies. Characteristics such as gender, nationality and age all have significant effects on how and why social media is employed ( Andreassen et al., 2016 ; Hsu et al., 2015 ; Park et al., 2015 ). Given that the two studies in question utilized European, adolescent samples, the difference in the culture and age of our samples may have produced our varying results, ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Comparability issues may also explain these results, given the profiling analyses implemented in the studies of Brailovskaia and colleagues, (2021), as well as Luo and colleagues (2021) did not extensively consider different profiles parameterizations, as the present study and Cheng et al. (2022) did. Furthermore, the results of this study closely replicated those of the Cheng et al., (2022) study, with both studies identifying a near identical pattern of symptom experience across three advancing levels of severity. This replication of results may indicate their accuracy, strengthening the validity of SMA experience models involving 3 differentiated profiles of staggered severity. Both our findings and Cheng et al.’s findings indicate profiles characterized by higher levels of cognitive symptoms (salience, withdrawal and mood modification) for each class when compared to their experience of behavioral symptoms (Relapse, withdrawal, conflict; Cheng et al., 2022 ). Further research may focus on any potentially mediating/moderating factors that may be interfering, and potentially further replicate such results, proving their reliability. Furthermore, given that past studies (with different results) utilized European, adolescent samples, cultural and age comparability limitations need to be considered and accounted for in future research ( Bányai et al., 2017 , Brailovskaia et al., 2021 ; Cheng et al., 2022 ).

Regarding withdrawal being the symptom of highest discrepancy between profiles, findings suggest that it may be more SMA predictive, and thus merit specific assessment or diagnostic attention, aligning with past literature ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 , Smith and Short, 2022 ). Indeed, the experience of irritability and frustration when abstaining from usage has been shown to possess higher differentiation power regarding diagnosing and measuring other technological addictions such as gaming, indicating the possibility of a broader centrality to withdrawal across the constellation of digital addictions ( Gomez et al., 2019 ; Schivinski et al., 2018 ).

Finally, the higher SMA risk profile percentage in the current study compared with previous research [e.g., 14 % in contrast to the 4.5 % ( Bányai et al., 2017 ), 4.2 % ( Luo et al., 2021 ) and 7.2 % ( Brailovskaia et al., 2021 )] also invites significant plausible interpretations. The data collection for the present Australian study occurred between August 2019 to August 2020, while Bányai and their colleagues (2017) collected their data in Hungary in March 2015, and Brailovskaia and their colleagues (2021) in Lithuania and Germany between October 2019 and December 2019. The first cases of the COVID-19 pandemic outside China were reported in January 2020, and the pandemic isolation measures prompted more intense social media usage, to compensate for their lack of in person interactions started unfolding later in 2020 ( Ryan, 2021 , Saud et al., 2020 ). Thus, it is likely that the higher SMA symptom scores reported in the present study are inflated by the social isolation conditions imposed during the time the data was collected. Furthermore, the present study involves an adult English-speaking population rather than European adolescents, as the studies of Bányai and their colleagues (2017) and Brailovskaia and their colleagues (2021). Thus, age and/or cultural differences may explain the higher proportion of the high SMA risk profile found. For instance, it is possible that there may be greater SMA vulnerability among older demographics and/or across countries. The explanation of differences across counties is reinforced by the findings of Cheng and colleagues (2022) who assessed and compared UK and US adult populations, the first is less likely, as younger age has been shown to relate to higher SMA behaviors ( Lyvers et al., 2019 ). Overall, the present results closely align with that of Cheng and colleagues (2022), who also collected their data during a similar period (between May 18, 2020 and May 24, 2020) from English speaking countries (as the present study did). They, in line with our findings, also supported the occurrence of three SMA behavior profiles, with the low risk profile exceeding 50 % of the general population and those at higher risk ranging above 9 %.

4.2. Concurrent addiction risk

Considering the second study aim, ascending risk profile membership was strongly related to increased experiences of internet and shopping addiction, while it moderately connected with gaming, gambling and sex addictions. Finally, it weakly associated with alcohol, exercise and drug addictions. These findings constitute the first semi-comprehensive cross-addiction risk ranking of SMA high-risk profiled individuals, allowing the following implications.

Firstly, no distinction was found between the so called “technological” and other behavioral addictions, potentially contradicting prior theory on the topic ( Gomez et al., 2022 ). Typically, the abuse of internet gaming/pornography/social media, has been classified as behavioral addiction ( Enrique, 2010 , Savci and Aysan, 2017 ). However, their shared active substance – the internet – has prompted some scholars to suggest that these should be classified as a distinct subtype of behavioral addictions named “technological/ Internet Use addictions/disorders” ( Savci & Aysan, 2017 ). Nevertheless, the stronger association revealed between the “high” SMA risk profile and shopping addictions (not always necessitating the internet), compared to other technology related addictions, challenges this conceptual distinction ( Savci & Aysan, 2017 ). This finding may point to an expanding intersection between shopping and SMA, as an increasing number of social media platforms host easily accessible product and services advertising channels (e.g., Facebook property and car selling/marketing groups, Instagram shopping; Rose & Dhandayudham, 2014 ). In turn, the desire to shop may prompt a desire to find these services online, share shopping endeavors with others or find deals one can only access through social media creating a reciprocal effect ( Rose & Dhandayudham, 2014 ). This possibility aligns with previous studies assuming reciprocal addictive co-occurrences ( Tullett-Prado et al., 2021 ). This relationship might also be exacerbated by shared causal factors underpinning addictions in general, such as one’s drive for immediate gratification and/or impulsive tendencies ( Andreassen et al., 2016 ; Niedermoser et al., 2021 ). Although such interpretations remain to be tested, the strong SMA and shopping addiction link evidenced suggests that clinicians should closely examine the shopping behaviors of those suffering from SMA behaviours, and if comorbidity is detected – address both addictions concurrently ( Grant et al., 2010 , Miller et al., 2019 ). Conclusively, despite some studies suggesting the distinction between technological, and especially internet related (e.g., SMA, internet gaming), addictions and other behavioral addictions ( Gomez et al., 2022 , Zarate et al., 2022 ), the current study’s high risk SMA profile associations appear not to differentiate based on the technological/internet nature that other addictions may involve.

Secondly, results suggest a novel hierarchical list of the types of addictions related to the higher SMA risk profile. While previous research has established links between various addictive behaviors and SMA (i.e., gaming and SMA; Wang et al., 2015 ), these have never before - to the best of the authors’ knowledge – been examined simultaneously allowing their comparison/ranking. Therefore, our findings may allow for more accurate predictions about the addictive comorbidities of SMA, aiding in SMA’s assessment and treatment. For example, Internet, shopping, gambling, gaming and sex addictions were all shown to more significantly associate with the high risk SMA profile than exercise and substance related addictive behaviors ( King et al., 2014 ; Gainsbury et al., 2016a ; Gainsbury et al., 2016b ; Rose and Dhandayudham, 2014 , Kamaruddin et al., 2018 , Leung, 2014 ). Thus, clinicians working with those with SMA may wish to screen for gaming and sex addictions. Regardless of the underlying causes, this hierarchy provides the likelihood of one addiction precipitating and perpetuating another in a cyclical manner, guiding assessment, prevention, and intervention priorities of concurrent addictions.

Lastly, these results indicate a lower relevance of the high risk SMA profile with exercise/substance addictive behaviors. Considering excessive exercise, our study reinforces literature indicating decreased physical activity among SMA and problematic internet users in general ( Anderson et al., 2017 , Duradoni et al., 2020 ). Naturally, those suffering from SMA behaviours spend large amounts of time sedentary in front of a screen, precluding excessive physical activities. Similarly, the lack of a significant relationship between tobacco abuse and SMA has also been identified priori, perhaps due to the cultural divide between social media and smoking in terms of their acceptance by wider society and of the difference in their users ( Spilkova et al., 2017 ). Contrary to expectations, there were weak/negligible associations between the high SMA risk profile with substance and alcohol abuse behaviours. This finding contradicts current knowledge supporting their frequent comorbidity ( Grant et al., 2010 , Spilkova et al., 2017 ; Winpenny et al., 2014 ). This finding may potentially be explained by individual differences between these users, as while one can assume many traits are shared between those vulnerable to substances and SMA, these may be expressed differently. For example, despite narcissism being a common addiction risk factor, its predictive power is mediated by reward sensitivity in SMA, where in alcoholism and substances, no such relationship exists ( Lyvers et al., 2019 ). Perhaps the constant dopamine rewards and the addictive reward schedule of social media targets this vulnerability in a way that alcoholism does not. Overall, one could assume that the associations between SMA and less “traditionally” (i.e., substance related; Gomez et al., 2022 ) viewed addictions deserves more attention. Thus, future research is recommended.

4.3. Limitations and future direction

The current findings need to be considered in the light of various limitations. Firstly, limitations related to the cross-sectional, age specific and self-report surveyed data are present. These methodological restrictions do not allow for conclusions regarding the longitudinal and/or causal associations between different addictions, nor for generalization of the findings to different age groups, such as adolescents. Furthermore, the self-report questionnaires employed may accommodate subjectivity biases (e.g., subjective and/or false memory recollections; Hoerger & Currell, 2012 ; Sun & Zhang, 2020 The latter risk is reinforced by the non-inclusion of social desirability subscales in the current study, posing obstacles in ensuring participant responses are accurate.

Additionally, there is a conceptual overlap between SMA and Internet Addiction (IA), which operates as an umbrella construct inclusive of all online addictions (i.e., irrespective of the aspect of the Internet being abused; Anderson et al., 2017 , Savci and Aysan, 2017 ). Thus, caution is warranted considering the interpretation of the SMA profiles and IA association, as SMA may constitute a specific subtype included under the IA umbrella ( Savci & Aysan, 2017 ). However, one should also consider that: (a) SMA, as a particular IA subtype is not identical to IA ( Pontes, & Griffiths, 2014 ); and (b) recent findings show that IA and addictive behaviours related to specific internet applications, such as SMA, could correlate with different types of electroencephalogram [EEG] activity, suggesting their neurophysiological distinction (e.g. gaming disorder patients experience raised delta and theta activity and reduced beta activity, while Internet addiction patients experience raised gamma and reduced beta and delta activity; Burleigh et al., 2020 ). Overall, these advocate in favour of a careful consideration of the SMA profiles and IA associations.

Finally, the role of demographic differences, related to one’s gender and age, which have been shown to mediate the relationship between social media engagement and symptoms of other psychiatric disorders ( Andreassen et al., 2016 ) have not been attended here.

Thus, regarding the present findings and their limitations, future studies should focus on a number of key avenues; (1) achieving a more granular understanding of SMA’s associations with comorbid addictions via case study or longitudinal research (e.g., cross lag designs), (2) further clarifying the nature of the experience of SMA symptoms, (3) investigating the link between shopping addiction and SMA, as well as potential interventions that target both of these addictions simultaneously and, (4) attending to gender and age differences related to the different SMA risk profiles, as well as how these may associate with other addictions.

5. Conclusion

The present study bears significant implications for the way that SMA behaviours are assessed among adults in the community and subsequently addressed in adult clinical populations. By profiling the ways in which SMA symptoms are experienced, three groups of adult social media users, differing regarding the reported intensity of their SMA symptoms were revealed. These included the ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk profiles. The high SMA risk profile membership was strongly related to increased rates of reported internet and shopping related addictive behaviours, moderately associated with gaming, gambling and sex related addictive behaviours and weakly associated with alcohol, exercise and drug related addictive behaviours, to the point that such associations were negligible at most. These results enable a better understanding of those experiencing higher SMA behaviours, and the introduction of a risk hierarchy of SMA-addiction comorbidities that needs to be taken into consideration when assessing and/or treating those suffering from SMA symptoms. Specifically, SMA and its potential addictive behaviour comorbidities may be addressed with psychoeducation and risk management techniques in the context of SMA relapse prevention and intervention plans, with a greater emphasis on shopping and general internet addictive behaviours. Regarding epidemiological implications, the inclusion of 14 % of the sample in the high SMA risk profile implies that while social media use can be a risky experience, it should not be over-pathologized. More importantly, and provided that the present findings are reinforced by other studies, SMA awareness campaigns might need to be introduced, while regulating policies should concurrently address the risk for multiple addictions among those suffering from SMA behaviours.

Note 1: Firstly, results were compared across all converged models. In brief, the AIC and BIC are measures of the prediction error which penalize goodness of fit by the number of parameters to prevent overfit, models with lower scores are deemed better fitting ( Tein et al., 2013 ). Of the 16 possible models, the parameterization with the most consistently low AIC’s and BIC’s across models with 1–8 profiles were chosen, eliminating 8 of the possible models. Subsequently, the remaining models were more closely examined through TIDYLPA using the compare solutions command, with the. BLMR operating as a direct comparison between 2 models (i.e. the model tested and a similar model with one profile less) on their relative fit using likelihood ratios. A BLMR based output p value will be obtained for each comparison pair with lower p-values corresponding to the greater fit among the models tested (i.e. if BLMR p >.05, the model with the higher number of profiles needs to be rejected; Tein et al., 2013). Entropy is an estimate of the probability that any one individual is correctly allocated in their profile/profile. Entropy ranges from 0 to 1 with higher scores corresponding with a better model ( Tein et al., 2013 ; Larose et al., 2016 ). Finally, the N_min represents the minimum proportion of sample participants in any one presentation profile and aids in determining the interpretability/parsimony of a model. If N_min is 0, then there is a profile or profilees in the model empty of members. Thus, the interpretability and parsimony of the model is reduced ( CRAN, 2021 ). These differing fit indices were weighed up against eachother in order to identify the best fitting model (Akogul & Erisoglu, 2017). This best fitting model was subsequently applied to the datasheet, and then the individual profilees examined through the use of descriptive statistics in order to identify their characteristics.

Note 2: With regards to the assumptions of the LPA Model, as a non-parametric test, no assumptions were made regarding the distribution of data. With regards to the subsequent ANOVA analyses, 2 assumptions were made as to the nature of the distribution. Homogeneity of variances and Normality. Thus, the distribution of the data was assessed via Jamovi. Skewness and Kurtosis for all measures employed in the ANOVA analyses. Skewness ranged from 0.673 to 2.49 for all variables bar the OGD-Q which had a skewness of 3.45. Kurtosis ranged from 0.11 to 6 for variables bar the OGD-Q which had a kurtosis of 13.9. Thus, all measures excepting the OGD-Q sat within the respective acceptable ranges of + 3 to −3 and + 10 to −10 recommended by Brown and Moore (2012).

Dr Vasileios Stavropoulos received funding by:

The Victoria University, Early Career Researcher Fund ECR 2020, number 68761601.

The Australian Research Council, Discovery Early Career Researcher Award, 2021, number DE210101107.

Ethical Standards – Animal Rights

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Thus, the present study was approved by the Human Ethics Research Committee of Victoria University (Australia).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Confirmation statement

Authors confirm that this paper has not been either previously published or submitted simultaneously for publication elsewhere.

Publication

Authors confirm that this paper is not under consideration for publication elsewhere. However, the authors do disclose that the paper has been considered elsewhere, advanced to the pre-print stage and then withdrawn.

Authors assign copyright or license the publication rights in the present article.

Availability of data and materials

Data is deposited as a supplementary file with the current document.

CRediT authorship contribution statement

Deon Tullett-Prado: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation. Vasileios Stavropoulos: Supervision, Resources, Funding acquisition, Project administration. Rapson Gomez: Supervision, Resources. Jo Doley: Supervision, Resources.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Biographies

Deon Tullett-Prado: Deon Tullett-Prado is a PhD candidate and emerging researcher in the area of behavioral addictions and in particular Internet Gaming Disorder. His expertise involves advanced statistical analysis skills and innovative techniques regarding population profiling.

Dr Vasileios Stavropoulos: Dr Vasileios Stavropoulos is a member of the Australian Psychological Society (APS) and a registered psychologist endorsed in Clinical Psychology with the Australian Health Practitioner Regulation Authority (AHPRA). Vasileios' research interests include the areas of Behavioral Addictions and Developmental Psychopathology. In that context, Vasileios is a member of the European Association of Developmental Psychology (EADP) and the EADP Early Researchers Union. Considering his academic collaborations, Vasileios maintains his research ties with the Athena Studies for Resilient Adaptation Research Team of the University of Athens, the International Gaming Centre of Nottingham Trent University, Palo Alto University and the Korean Advanced Institute of Science and Technology. Vasileios has received the ARC DECRA award 2021.

Dr Rapson Gomez: Rapson Gomez is professor in clinical psychology who once directed clinical training at the School of Psychology, University of Tasmania (Hobart, Australia). Now he focuses on research using innovative statistical techniques with a particular focus on ADHD, biological methods of personality, psychometrics and Cyberpsychology.

Dr Jo Doley: A lecturer at Victoria University, Dr Doley has a keen interest in the social aspects of body image and eating disorders. With expertise in a variety of quantitative methodologies, including experimental studies, delphi studies, and systematic reviews, Dr Doley has been conducting research into the ways that personal characteristics like sexual orientation and gender may impact on body image. Furthermore, in conjunction with the cyberpsychology group at VU they have been building a new expertise on digital media and it’s potential addictive effects.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.abrep.2023.100479 .

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Data availability

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Background and aims

Recent research suggests that use of social networking sites can be addictive for some individuals. Due to the link between motivations for media use and the development of addiction, this systematic review examines Facebook-related uses and gratifications research and Facebook addiction research.

Searches of three large academic databases revealed 24 studies examining the uses and gratifications of Facebook, and nine studies of Facebook addiction.

Comparison of uses and gratifications research reveals that the most popular motives for Facebook use are relationship maintenance, passing time, entertainment, and companionship. These motivations may be related to Facebook addiction through use that is habitual, excessive, or motivated by a desire for mood alteration. Examination of Facebook addiction research indicates that Facebook use can become habitual or excessive, and some addicts use the site to escape from negative moods. However, examination of Facebook addiction measures highlights inconsistency in the field.

There is some evidence to support the argument that uses and gratifications of Facebook are linked with Facebook addiction. Furthermore, it appears as if the social skill model of addiction may explain Facebook addiction, but inconsistency in the measurement of this condition limits the ability to provide conclusive arguments.

Conclusions

This paper recommends that further research be performed to establish the links between uses and gratifications and Facebook addiction. Furthermore, in order to enhance the construct validity of Facebook addiction, researchers should take a more systematic approach to assessment.

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Paragraph on Facebook for HSC and SSC

Here are some short paragraphs on Facebook describing the advantages and disadvantages of Facebook.

Here we shared some example paragraphs on Facebook for classes 6 to 10, SSC and HSC students. The paragraphs describe the advantages and disadvantages of Facebook . You know Facebook has become a part and parcel of our life.

So paragraph on Facebook is very important for SSC/HSC and Honours level students. You can write this paragraph for the following headings:

Paragraph on Facebook for Class 6, 7, 8 (150 Words)

Facebook is a social networking website. It helps us to be connected with our family members, cousins, and friends . Facebook has become the most popular networking website. It was founded in 2004 by Mark Zuckerberg, a student at Harvard University . The mother company of Facebook is Meta Platforms Inc which is an Americal Tech Company.

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He can share his thoughts, photos, videos, and activities with connected friends. A person can send text messages, and voice messages, and make voice or video calls to his friends. Facebook also helps business owners sell and customers find their products.

But we see some disadvantages of Facebook too. Criminals are using Facebook to commit crimes.

Above all, Facebook has become a part and parcel of communication.

You may read: Essay on uses and abuses of Facebook

Paragraph on Advantages and Disadvantages of Facebook

Facebook is a social networking website intended to connect friends , family, and business associates. There are many social networking sites in the world Twitter, Google Plus, My space, etc. But it is the largest of the other networking sites in the world.

Facebook was founded in 2004 by Harvard student Mark Zuckerberg and was originally called Facebook. The mother company of Facebook is Meta Platforms Inc which is an Americal Tech Company. Facebook began as a college networking website and was quickly successful on campus and expanded beyond Harvard into other schools.

At that time, it was only available to schools, universities, organizations, and companies in English-speaking countries.

Later it was extended to include anyone and everyone. Facebook users create a profile page that includes the following: Information, Status, Friends, Friends in Other Networks, Photos, Notes, Groups, and The Wall.

As a result, everyone within that network can view the profile. Users are able to search for friends and acquaintances by e-mail address, school, or university, or just by typing in a name or location in the search box.

When people become friends, they are able to see all of each other’s profiles including contact information. A popular feature on Facebook is to share photographs uploaded from a phone, camera, or computer .

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Above all, Facebook has become a part and parcel of the communication system.

Read: Essay on the uses and abuses of Facebook

Paragraph on Uses and Abuses of Facebook (250 Words)

Facebook is a very popular social networking site currently in the world. The mother company of Facebook is Meta Platforms Inc which is an Americal Tech Company. Facebook has both advantages and disadvantages. It is a great way to keep in touch with our family and friends with instant messaging, audio, and video calling facilities.

Facebook is the perfect environment to stay connected. With status updates, photos, and profile information, we can know updated information about our connected friends or relatives.

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Facebook helps us to know and meet new people easily. We can search for our friends with their names or mobile numbers. Using Facebook, one can express one’s opinion easily and others can comment on that.

It has created business opportunities for us. Now, a businessman can easily promote his products or services to their customers through Facebook. Customers also find it easy to search for necessary products and know about them.

Students can communicate with their mates, other students, and teachers and find answers to their questions. They can meet great teachers and educators and learn great things from them. You may read:  5 Reasons why students should use Facebook

On the other hand, there are some disadvantages of Facebook too. Teenagers who spend a lot of time using it will suffer in the long run. It kills their valuable time for the study. Sometimes people share wrong and hateful news and information that cause harm to others. Cybercriminals are always active.

They can steal your information from your profile and harass you. Facebook, people know about different cultures worldwide. Sometimes, they start practising other cultures forgetting their own cultures. Thus, It causes a cultural assault breaking our own cultures.

There is no denying fact that Facebook has a lot of advantages with some disadvantages too. So, we must be careful about using this social media.

Join our Facebook page and group for the latest tips and tutorials.

You may also learn about:

• Online Learning Platforms for Students
• Best Online Teaching Platforms
• Online Master Degree
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31 Comments

osssam nice wonderful excilent good. also thanks

nic bur so short

http://StartParttimeJob.com/?userid=27199

Can you please share patriots composition??please please I need it badly

Ok, I will share it soon. Thanks for visiting BDclass.

Welcome to bdclass.com

not that good

Very easily writing. That's a very good for us…

Thanks for helping

'in the word' or 'in the world' ?in the 2nd line

Thank you so much

Thanks…

Now a days.(Nowadays)

There’s only one way to spell nowadays—as one word. Even though this adverb evolved from the Middle English now adays, spelling it as more than one word today is a mistake. You shouldn’t write it as now a days, nowdays, nowaday, or any other spelling:

Good but short

tnx sir it was very helpfull for.

very very nice

This is very helpful…Thank you sir

Thank you sir

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• DOI: 10.1556/JBA.3.2014.016
• Corpus ID: 17301851

The uses and abuses of Facebook: A review of Facebook addiction

• Tracii Ryan , A. Chester , +1 author Sophia Xenos
• Published in Journal of Behavioral… 26 August 2014

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Published in Journal of Behavioral Addictions 2014

Tracii Ryan A. Chester J. Reece Sophia Xenos

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Uses and Abuses of Facebook Essay

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Uses and Abuses of Facebook

Facebook, founded by Mark Zuckerberg in 2004, has become one of the most influential social media platforms worldwide. With over two billion monthly active users, it has transformed how we connect, share, and communicate. While Facebook offers numerous benefits, it has a fair share of drawbacks. This essay will explore eight key points highlighting the uses and abuses of Facebook.

Uses of Facebook: Facebook is primarily a platform for connecting with friends and family. It allows people to maintain relationships with loved ones, whether they are near or far. Users can share updates, photos, and messages, which foster a sense of connection and belonging.

Facebook is a valuable source of information and news. Users can follow news outlets, organizations, and individuals to stay updated on current events, trends, and topics of interest. It serves as a convenient way to access a wide range of information.

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ইউটিউবে ভিডিও লেকচার দেখুনঃ

Businesses and individuals use Facebook for promotional purposes. The platform offers tools for advertising, creating pages, and reaching a vast audience. It has become a powerful marketing tool, enabling companies to effectively connect with potential customers and promote their products or services.

Facebook facilitates creating and managing various groups and communities based on shared interests, hobbies, or causes. These groups unite like-minded people, fostering discussions, support, and collaboration.

Facebook serves as a platform for educational purposes. Schools, universities, and educational organizations use it to share resources, assignments, and updates with students. It can also be a valuable medium for online courses and discussions.

Abuses of Facebook: Facebook has faced numerous privacy scandals, raising concerns about handling user data. Instances like the Cambridge Analytica controversy exposed how user information can be misused, leading to a loss of trust in the platform.

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The anonymity offered by the internet can lead to cyberbullying on Facebook. Users may face harassment, threats, or offensive content, which can have severe emotional and psychological consequences.

The addictive nature of Facebook can lead to excessive use, resulting in wasted time and productivity loss. Scrolling through the news feed for hours or playing games can harm one’s daily responsibilities and well-being.

Facebook is a breeding ground for the spread of fake news and misinformation. Due to the ease of sharing content, false information can quickly gain traction, causing confusion and harm.

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While Facebook connects people virtually, it can also lead to isolation in the real world. Excessive time spent on the platform can detract from face-to-face interactions, leading to loneliness and social disconnection.

In conclusion, Facebook is a powerful tool with numerous uses, from connecting with loved ones to marketing and education. However, it also presents several challenges and abuses, including privacy concerns, cyberbullying, addiction, the spread of fake news, and social isolation. Users must be aware of these issues and use the platform responsibly while holding companies like Facebook accountable for addressing these challenges and protecting users’ interests. Balancing the benefits and drawbacks of Facebook is essential in our increasingly connected digital age.

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© Copyright - Literature Xpres

• আমাদের সম্পর্কে জানুন

Uses and Abuses of Facebook

• Organizational Leadership, Policy and Development

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T1 - Uses and Abuses of Facebook

AU - Fry, Gerald W.

M3 - Article

JO - The Nation

JF - The Nation