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Social Media Networks at University: Who Hangs Out with Who, Where and Why?

This research study examines how university students use different social media platforms, their behaviors on these platforms, and the factors that explain existing networks. The study also explores the type of people who gravitate towards Facebook versus Twitter and investigates the significance of homophily and internet usage in shaping these networks.

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Social Media Networks at University: Who Hangs Out with Who, Where and Why?

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  1. Social Media Networks at University: Who Hangs Out with Who, Where and Why? Narisong Huhe and Mark Shephard

  2. Importance • Young people are most likely to rely on social media for news: 37% use Facebook 22% use Twitter (Digital News Report 2017) • Not just elections, there is an increased use of referenda in UK (and often big constitutional issues e.g. 2014 + 2016)

  3. Consequently…Research Questions • How do university students use different types of social media? • Do they behave variably on different social media platforms? • What explains existing networks? • What type of people do Facebook vs Twitter?

  4. Literature 1:Market Share &User Distribution • Market share (usage) in UK: 74% Facebook; 12% Twitter (McGrory, 2018) H1: Facebook > Twitter (usage+ networks) • A few people dominate online discussions (Heil et al., 2009; Quinlan et. al., 2015) H2: Networks are skewed. A few people dominate

  5. Literature 2: Networks • Transitivity – each friend added provides new possibilities for the added friend’s network of friends to interact with (Golder et al. 2010) H3: Users with more friends will have even more options for friends and posting • Users with more followers tweet more (Rui et al., 2012) H4: Users with more friends/followers post more

  6. Literature 3: Homophily • Users with similar interests are more likely to be networked with each other (McPherson et al., 2001; Kivran-Swaine, 2011; Aiello, 2012) H5: Male/male + female/female networks > male/female networks H6: Those from similar socio-economic backgrounds will network together

  7. Literature 4 – Usage & Interest • Internet use coincides with more online socialising (Vergeer, 2009) H7: The more you use, the more networked & active you are H8: Those who say they comment a lot will be networked & active • Facebook users tend to be ‘ordinary’ voters, Twitter users tend to be politicos (Williamson, 2010) H9: Those with political interest will be more active on Twitter & less active on Facebook

  8. Methods • N = 120 students, 3rd year class, University of Strathclyde • Complete sociomatrix survey • vs. name-generator technique (e.g., BES three names) • But… inherent interdependence between ALL respondents & so name-generator = inadequate • We capture this by looking at the complete sociomatrixof not just 3 names but 120 students.

  9. Results – Facebook Network (Friends)

  10. Results – FacebookNetwork Clusters (Friends)

  11. Results – Facebook Network (interaction over the past day)

  12. Results – Facebook Usage Clusters (interaction over the past day)

  13. Results – Twitter Network (Followers)

  14. Results –Twitter Network Clusters (Followers)

  15. Results – Twitter Network(interaction over the past day)

  16. Results – Twitter Usage Clusters (interaction over the past day)

  17. Results:Facebook Friendship +Activity Networks

  18. Results:Twitter Friendship + Activity Networks

  19. Bayesian Exponential Random Graph Model (ERGM) • The Bayesian ERGM estimates the posterior distribution of the model parameters, θ, of an observed graph y. The likelihood p(y|θ) s(y) is a know vector of network statistics; θ are model parameters whose prior distribution is p(θ)

  20. Dependent variable: NetworkA visual example

  21. Network covariates, s(y) • Basic network effects • Edge (i.e., general tendency of connectedness) • Transitivity (i.e., “Friends of friends are friends”) • Popularity (e.g., Matthew’s effect) • Dyadic gender homophily • Dyadic difference in SIMD • Monadic attributes (internet usage (hrs), self-expressive (Likert, likely to comment/not likely), and political interest (Likert)

  22. Results Summary • Facebook > Twitter (usage + networks) • Networks are skewed. A few people dominate (even more acute for Twitter) • No transitivity – people interact with popular users on Twitter, but not the friends of these people • Users with more followers tweet more (no significant finding re: Facebook) • Gender homophily • Internet use explains Facebook/Twitter networks, but only Facebook activity • Commentingonly accounts for Facebook networks & activity. On Twitter high commenters comment less • Twitter is used by politicos, Facebook is not

  23. To do… • Geographical propinquity - do those who live in same areas flock together? • Partisanship – do those with similar partisan preferences flock together?

  24. Social Media Networks at University: Who Hangs Out with Who, Where and Why? Narisong Huhe and Mark Shephard

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