How exposure to different opinions impacts the life cycle of social media
As a lot of communication and media consumption moves online, people may be exposed to a wider population and more diverse opinions. However, individuals may act differently when faced with opinions far removed from their own. Moreover, changes in the frequency of visits, posting, and other forms of expression could lead to narrowing of the opinions that each person observes, as well as changes in the customer base for online platforms. Despite increasing research on the rise and fall of online social media outlets, user activity in response to exposure to others’ opinions has received little attention. In this study, we first introduce a method that maps opinions of individuals and their generated content on a multi-dimensional space by factorizing an individual–object interaction (e.g., user-news rating) matrix. Using data on 6151 users interacting with 287,327 pieces of content over 21 months on a social media platform we estimate changes in individuals’ activities in response to interaction with content expressing a variety of opinions. We find that individuals increase their online activities when interacting with content close to their own opinions, and interacting with extreme opinions may decrease their activities. Finally, developing an agent-based simulation model, we study the effect of the estimated mechanisms on the future success of a simulated platform.
KeywordsSocial media User activity Opinion measuring Agent-based simulation
This study is based in part upon work supported by the National Science Foundation under Grant Numbers SES-1027413. Any opinions, findings, and conclusions, or recommendations expressed in this study are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- An, J., Quercia, D., Cha, M., Gummadi, K., & Crowcroft, J. (2014). Sharing political news: The balancing act of intimacy and socialization in selective exposure. EPJ Data Science, 3(1). doi: 10.1140/epjds/s13688-014-0012-2.
- Ashouri Rad, A. (2016). Three essays on dynamics of online communities (Doctor of Philosophy), Virginia Tech.Google Scholar
- Ashoury, A., Herrera-Restrepo, O., & Zhang, H. (2013). The diffusion of a petition in a social network. In Paper presented at the 63rd annual conference and expo of the Institute of Industrial Engineers.Google Scholar
- Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Paper presented at the proceedings of the 21st international conference on world wide web.Google Scholar
- Boyd, D. (2007). Why youth (heart) social network sites: The role of networked publics in teenage social life. MacArthur foundation series on digital learning–Youth, identity, and digital media volume, 119–142.Google Scholar
- Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-being. In Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.Google Scholar
- Cannarella, J., & Spechler, J. A. (2014). Epidemiological modeling of online social network dynamics. arXiv preprint arXiv:1401.4208.
- Dwyer, C., Hiltz, S., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. AMCIS 2007 proceedings, 339.Google Scholar
- Garcia, D., Mavrodiev, P., & Schweitzer, F. (2013). Social resilience in online communities: The autopsy of friendster. In Paper presented at the proceedings of the first ACM conference on online social networks.Google Scholar
- Giliette, F. (2011). The rise and inglorious fall of myspace. http://cli.gs/94lb3yf.
- Gose, C. (2013). Internal communications: The rise and fall of internal social networks. Retrieved from http://www.rmgnetworks.com/blog/bid/215425/Internal-communications-the-rise-and-fall-of-internal-social-networks.
- Gottfried, J., Barthel, M., Shearer, E., & Mitchell, A. (2016). The 2016 presidential campaign—A news event that’s hard to miss. Pew Research Center.Google Scholar
- Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016. Pew Research Center.Google Scholar
- Jalali, M. S. (2014). How individuals weigh their previous estimates to make a new estimate in the presence or absence of social influence. In Social computing, behavioral-cultural modeling and prediction (pp. 67–74). Springer International Publishing. doi: 10.1007/978-3-319-05579-4_9.
- Lampe, C., Ellison, N. B., & Steinfield, C. (2008). Changes in use and perception of Facebook. In Paper presented at the proceedings of the 2008 ACM conference on Computer supported cooperative work.Google Scholar
- Lee, J. (2014). Are some people less influenced by others’ opinions? The role of internal political self-efficacy and need for cognition in impression formation on social networking sites. Cyberpsychology Behavior and Social Networking, 17(9), 571–577. doi: 10.1089/cyber.2013.0713.CrossRefGoogle Scholar
- Madejski, M., Johnson, M., & Bellovin, S. M. (2011). The failure of online social network privacy settings. Department of Computer Science, Columbia University, Tech. Rep. CUCS-010-11.Google Scholar
- Rad, A. A., & Rahmandad, H. (2013). Reconstructing online behaviors by effort minimization. In Social computing, behavioral-cultural modeling and prediction (pp. 75–82). Springer. doi: 10.1007/978-3-642-37210-0_9.
- Torkjazi, M., Rejaie, R., & Willinger, W. (2009). Hot today, gone tomorrow: On the migration of MySpace users. In Paper presented at the proceedings of the 2nd ACM workshop on online social networks.Google Scholar
- Wadee, Z. (2013). Facebook your boss: Using social media in internal communications. Retrieved from http://www.theguardian.com/careers/careers-blog/facebook-employers-encourage-social-media.
- Wu, S., Das Sarma, A., Fabrikant, A., Lattanzi, S., & Tomkins, A. (2013). Arrival and departure dynamics in social networks. In Paper presented at the proceedings of the sixth ACM international conference on web search and data mining.Google Scholar