How many people use social media? Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

What are the most popular social media platforms? Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Target group study with intensive users of social networks. The focus was on the frequency of use, significance and purpose of use of social media. Further questions were: How do social networks affect the information behaviour of their users? Where do social networks come into contact with political content or topics? Other focal points were political information behaviour and the credibility of social media. In addition, the following research questions were examined: Which information offerings of the Federal Government are important and which are actually used? What are the expectations of these information services? What are the respondents´ attitudes towards the relationship between politics and social networks?

Topics: Social media use: frequency of use of social media; platforms used at least once a day; ranking of the four most frequently used social networks; preferred devices for social media use; type of use or reasons for use; attitude towards social media in general with regard to time spent, credibility of information, quality of social contacts, relevant information and life without social networks.

Politics and media: political interest; points of contact with politics in everyday life; frequency of use of various media offers for political information; attitude towards politics and political information in social networks with regard to trustworthiness, credibility and orientation.

Political information behaviour in social networks: frequency of interaction with political activities of others in social networks; frequency of own political activities in social networks; seen political content on the social networks Facebook, Instagram, Twitter, Youtube and WhatsApp; opinion on political posts and contributions in the used social networks; personal consequences of political oversupply; Facebook users: active search for political content or automatic display in newsfeed; comparison of the quality of political discussions within and outside social networks; friends in social networks personally known; similar or rather different political views of these friends; own reaction to political posts of friends; following politicians, parties and political institutions in social networks; following persons and organizations sharing personal political opinions or with different political opinions; reasons why the respondent follows politicians etc. in social networks; perception of hate comments; evaluation of the law against hate comments.

Information behaviour concerning the Federal Government: importance of selected information offerings of the Federal Government; perception and frequency of use of these information offerings; expectations of information offerings of the Federal Government in social networks.

Living conditions: assessment of one´s own economic situation; satisfaction with democracy; democracy as a good form of government; party identification.

Demography: sex; age; household size; education; gainful employment; occupational status; federal state; city size; net household income.

Additionally coded was: weighting factor.

The messaging app LINE was used by 81.6 percent of internet users in Japan during the third quarter of 2024. LINE was the most used social media service, ahead of X, Instagram, and TikTok.

Whereas the amount of social media content posted by U.S. brands has declined in 2019, user engagement has increased. Overall, U.S. brands saw a five percent increase on cross-platform social media actions and an eleven percent increase in actions per post. The automotive industry performed best, with a 22 and 24 percent increase in user actions and actions per post respectively.

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Social Networking Market Size 2025-2029

The social networking market size is forecast to increase by USD 312.3 billion at a CAGR of 21.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing internet penetration worldwide. With more individuals gaining access to the internet, the number of social media users continues to rise, providing a vast audience for businesses to engage with. However, this market growth is not without challenges. Privacy concerns have emerged as a major obstacle, with users becoming increasingly wary of how their data is being collected and used. This trend is particularly prevalent in regions with stringent data protection regulations. Despite these challenges, social networking platforms continue to innovate and adapt to meet user demands and regulatory requirements. For instance, some companies are focusing on improving data security and privacy features to address user concerns. Others are exploring new revenue streams, such as e-commerce and subscription services, to diversify their offerings and mitigate the impact of declining organic reach on advertising revenues. Companies seeking to capitalize on the opportunities presented by the market must stay abreast of these trends and navigate privacy concerns effectively to succeed. Adopting a user-centric approach, investing in data security and privacy, and exploring new revenue streams are key strategies for companies looking to thrive in this dynamic market.

What will be the Size of the Social Networking Market during the forecast period?

Request Free SampleThe market continues to evolve, driven by the increasing number of smartphone users worldwide. This market encompasses various platforms, including messaging sites like Facebook Messenger and iMessage, as well as e-commerce platforms integrated with social media, such as Instagram. The business of apps has shifted towards a bottom-up approach, with independent databases and performance indicators becoming essential for B2C enterprises. In-app purchases and the purchase of apps themselves have become significant revenue streams. The market exhibits an s-curve function, with early adopters leading the way, followed by the mass market. The rise of 5G technology is expected to fuel further growth, enabling more experiences through 3D image context and real-time communication. However, data security concerns persist, necessitating security measures. National lockdowns have accelerated the shift towards online communities for various activities, including theatre, sports, art, music, and games. Overall, the market is a dynamic and evolving landscape, presenting both opportunities and challenges for businesses.

How is this Social Networking Industry segmented?

The social networking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeAdvertisingIn-app purchasePaid appsDistribution ChannelGoogleAppleServiceCommunicationEntertainmentSocializationMarketingCustomer servicePlatformWebsite-basedMobile appsHybrid platformsGeographyNorth AmericaUSCanadaAPACChinaIndiaJapanEuropeFranceGermanyItalyUKSouth AmericaBrazilMiddle East and Africa

By Type Insights

The advertising segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth, with the advertising segment leading the way in 2024. Social media advertising, which utilizes social media platforms to engage audiences, has gained popularity due to its ability to deliver highly targeted campaigns on social networking sites. Common advertising formats include static images, videos, stories, and messenger ads. The increasing use of social media for brand promotion and product awareness is driving market expansion. Furthermore, the rise of in-app purchases and the monetization of apps have contributed to the market's growth. A bottom-up approach, utilizing independent databases and performance indicators, reveals that smartphone users are the primary consumers, with a growing preference for cloud-based apps on Apple iOS-based devices. Consumer attitudes towards data security and privacy concerns are influencing market trends, with 5G technology and AI-based libraries playing a crucial role in addressing these concerns. National lockdowns have accelerated the shift towards online communities, live streaming videos, and OTT platforms. Influencer marketing and customized photo collages are also emerging trends in the market. The business of apps, including e-commerce platforms like Facebook Shops, and Big Tech companies, continue to dominate the landscape. Exchange rates and

Get a glance at the market report of share of various segments Request Free Sample

The Advertising segment w

People use different social networks for a wide range of purposes. In a February 2019 survey, Instagram ranked first among social media survey for viewing photos but ranked head-to-head with Snapchat in terms of being the preferred social network for video consumption. Also, 47 percent of respondents stated that they used Pinterest to find or shop for products, a rate that no other social platform was able to match in that regard.

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by Twitter and the action taken by it. The data compiled is based on the monthly transparency reports published by Twitter in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Twitter include illegal activities, IP-related infringements and other issues such as Abuse,Harassment, Child Sexual Exploitation, Defamation, Hateful Conduct, Impersonation, Misinformation, etc. The action taken by Twitter on the basis of these reports includes the number of URLs actioned

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users of Facebook and Instagram by Meta and the action taken by it. The data compiled is based on the monthly transparency reports published by Meta in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Meta, for Facebook and Instagram, include content showing sexual content, account hacked, lost of access, harassment, request access to personal data, etc. and the action taken include resolution by tools and special review

"Sent private messages" and "Liked posts by other users or followed people" are the top two answers among U.S. consumers in our survey on the subject of "Most common social media activities".The survey was conducted online among 60,869 respondents in the United States, in 2024.

Datasets of Time Series of Social Media Activity. It includes 16.2 million YouTube videos, 0.8 million Usenet threads, 4.6 million Stack-Overflow questions and 70 thousands PLOS ONE papers. This data is used in JM Miotto, EG Altmann, 'Predictability of extreme events in social media', arXiv:1403.3616.

This statistic shows the results of a survey on the active usage of social media platforms in Germany in 2013. During the survey period it was found that 12 percent of German social media users stated that they actively used Google+ in their leisure time. Facebook was the most frequently used network, followed by YouTube.

High Frequency Indicator: The dataset contains year and month-wise data from the year 2021 to till date on the different types of actions taken by by significant social media intermediaries (SSMIs) such as Sharechat, Snapchat, Twitter, Koo, Facebook, Instagram, Google and WhatsApp. The data compiled is based on the monthly transparency reports published by SSMIs in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021)

The different types of action taken include Content actioning (removal), Account Removal Actions as a result of automated detection, Reporting spam, Blocking, Proactive ban, etc. on the different reasons such as harrassment, Child Endangerment with Nudity, Sexual Exploitation and Physical Abuse, Dangerous Organizations and Individuals with Organized Hate and Terrorism and its Propaganda, Hate Speech, Drugs and Firearms, etc.

Notes:

1. Twitter: “Proactive Monitoring” refers to content proactively identified by employing internal proprietary tools and industry hash sharing initiatives.
   1. Google: For data related to automated detection processes, Google includes data where the sender or creator of the content is located in India. In order to attribute a location to an individual sender or creator, Google use data signals such as location of account creation, IP address at the time of video upload and user phone number, as available.
   2. Meta: Proactive Rates- This metric shows the percentage of all content or accounts acted on that Meta found and flagged before users reported them to Meta. These metrics are the best estimates of content Meta act on and of proactive rates based on the creator of the content and predicted country locations for those users.
2. ShareChat: The accounts are proactively banned on the basis of Copyright violations, Sexually explicit, UGC- violation of community standards, Chatrooms and Comments.

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by Sharechat and the action taken by it. The data compiled is based on the monthly transparency reports published by Sharechat in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Sharechat include Impersonation, Terrorism, Suicide, Hate speech, Misinformation, Inappropriate username, picture, handle,status, illegal activities, violence, adult content, etc. and the action taken includes number of Content and accounts reported

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

 • Socio-economic data.


 • Data on habitual residence.


 • Information on intercity journeys.


 • Identity and sense of community.


 • Personal network indicators.


 • Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: https://www.flickr.com/photos/25906481@N07/albums/72157667029974755.

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.

REFERENCES

Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.

Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be

Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft here.

Instructions:

1. Unzip the files.
2. Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.
3. Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.
4. Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file
5. Launch Stata and open each flat csv files with it, then save it in Stata format.
6. Use the provided Stata .do scripts to replicate results.

Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.

The size of the AI in Social Media Market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 28.04% during the forecast period.Social media and AI means the application of artificial intelligence technologies for enhancing social media platforms and user experience. Artificial intelligence technologies enable social media to analyze big volumes of user data, make recommendations about the content they are supposed to view, detect harmful content, and enhance engagement.Social media has really become dominated by AI, using it for personal news feeds, targeted advertisements, and content moderation. AI could therefore be used by social media to understand its users, their preferences, or even interactions to make for tailored recommendations and content. There are also AI-driven tools that detect and flag as harmful hateful speech or the spread of misinformation and other things like that, thus helping online environments be safer.Artificial Intelligence in social media changes how humans connect and consume information in cyberspace. It means everything the AI algorithms comprehend when understanding what the users want, in terms of how they respond in order to enhance the experience and also augment engagement and business. When technology moves forward, social media platforms will be more intelligent, and their personalization, based on how the digital communications platform is formed, defines it further. Recent developments include: October 2022: Meta announced a collaboration with Microsoft to provide consumers with unique experiences in various sectors, including gaming and the future of work. Microsoft will introduce Microsoft 365 apps to Meta Quest devices as part of this collaboration, allowing individuals to interact with content from productivity programs such as Excel, Word, Outlook, PowerPoint, and SharePoint within virtual reality (VR). It also wants to bring Windows 365 to devices so that users can stream their whole Windows experience, including their own apps, content, and preferences, through a Windows Cloud PC., October 2022: Adobe announced new AI features that maximize creativity and accuracy across Creative Cloud products, and Adobe Express, the industry's leading all-in-one tool, allows anyone to make professional-quality, unique content. In addition, Adobe stated its intention to assist creators by leveraging its Content Authenticity Initiative (CAI) to maintain transparency when using generative AI. New AI features in Adobe Express allow Quick Actions for users to immediately compress images and videos for quick social media sharing, discover appropriate color palettes for the maximum visual aspect, and instantly canvas over 22,000 Adobe Fonts for the ideal typeface.. Key drivers for this market are: Integration of Artificial Intelligence Technology with Social Media for Effective Advertising, Increase in User Engagement on Social Media by Using Smartphones; Rise in Use of AI in Understanding Market Trends and Gaining Competitive Edge. Potential restraints include: Limited Number of Artificial Intelligence Technology Experts, Low Adoption of AI in Developing Economies. Notable trends are: Retail Industry to Witness a Significant Growth.

While work on the relationship between social media use and adolescent mental health has allowed for some progress, research in this area is still relatively new and shows mixed evidence. This is partly the consequence of a rapidly changing field, resulting in conceptualisation and measurement issues that hinder progress. Given the need for robust conceptualisation, the present study included five focus groups with a total of 26 adolescents aged 11-15 in Northwest England, to understand their experiences, motivations, and perceptions of social media use, relating to mental health and wellbeing. Reflexive thematic analysis was used to analyse the transcripts. We developed three themes and 14 sub-themes. Young people discussed key motivations for using social media (theme 1) relating to social connections, keeping up-to-date, mood management, the ‘default’ activity, freedom to express and develop myself, and fitting in. They shared some of the benefits and positive experiences of social media use (theme 2) such as feeling connected, validation and reassurance, and enjoyment and supporting a sense of self, and finally, they talked about negative experiences of social media use (theme 3), including platform risks, loss of control, social conflict, social comparison, and self-presentation management. Our findings have contributed to our understanding of the salient dimensions and language to inform the development of an adolescent social media experience measure related to mental health.

The increased use of social media among young people has attracted the attention of the public, the media and the government, and has led to growing concerns about its impact on young people's mental health, wellbeing and levels of loneliness. This concern stems from reported increase in mental health difficulties and increased social media use among this population. Research on this area is however relatively new and with mixed evidence. While some of the experiences with social media can be challenging, there is not sufficient evidence to support that social media is fundamentally bad. Indeed, recent evidence challenges this and suggests that the connection between social media and mental health might be a weak one, and its benefits, that have been largely overlooked, should also be considered.

This area of research has suffered the consequences of a rapidly changing field, resulting in quick, but methodologically flawed self-report measures of social media experience, that hinders progress. We have identified three potential problems in the self-report measurement of social media engagement and experience:

1) Most measures were developed without asking young people's experiences. This means that social media measures are being developed for young people without young people having any input. How can we be sure we are asking the right things if we do not consider their views?; 2) Many measures focus on "addictive social media", however this term is based largely on anecdotal evidence. In fact, the questions they use in these measures are based on nicotine dependence and gambling addiction criteria. Assuming these are the same can lead to misleading conclusions; 3) Many of the existing measures were not developed using rigorous and robust theoretical and statistical (psychometric) methods. Their validity is therefore questionable; 4) Even though the engagement with social media includes objective digital behaviours, this kind of information and data have not been considered during the development of measures. We cannot capture however the full picture of social media experience without assessing both, because they each offer unique information.

To address these challenges and limitations reported in the current literature we propose a 3-year project to co-develop, with young people, a comprehensive and freely available self-report social media experience measure that will be appropriate for young people. This will take into account existing research, objective social media data, and the views of social media experts, clinicians, parents/carers, teachers, and policy makers. Importantly, the development of the measure will be guided by the views and experiences of young people.

The proposed project will follow a novel method that combines traditional methods of scale development and a novel approach that triangulates objective (e.g. online social media comments) and subjective (e.g. self-report) assessment. The current project has a strong focus on the voices of young people and it will be based on a co-production model with young people. We will draw from different disciplines including digital behaviour and social media, mental health, loneliness, psychometrics, and computer science. The project has the potential to improve the way we measure, and therefore understand, young people's social media experience and how that influences their mental health, wellbeing, and loneliness. It will also provide a...

Community detection is an interesting field of online social networks. Most existing approaches either consider common attributes of social network users or rely on only social connections among the users. However, not enough attention is paid to the degree of interactions among the community members in the retrieved communities, resulting in less interactive community members. This inactivity will create problems for many businesses as they require highly interactive users to efficiently advertise their marketing information. In this paper, we propose a model to detect topic-oriented densely-connected communities in which community members have active interactions among each other. We conduct experiments on a real dataset to demonstrate the effectiveness of our proposed approach.

In 2019, everyday retail brands such as Forever21 or Fashion Nova generated a total of 1.7 total social media actions. Luxury retail brands generated the second-most user engagement on social media with 416 million actions. Online retail or big box retail had the smallest social media engagement, earning only 74.6 million social media actions in the year.

According to a survey among internet users worldwide during the third quarter of 2023, 73.9 percent of global respondents said they used social media platforms to conduct research on brands. The top three countries with the highest share of internet users using social media for brand research were all African: Nigeria came first with 97.4 percent of respondents, while Ghana followed with 94.5 percent and Kenya clinched third place with 92.2 percent.