Understanding user beliefs about algorithmic curation in the facebook news feed

Conference on Human Factors in Computing Systems - Proceedings

Volumn 2015-April, Issue , 2015, Pages 173-182

  Rader, Emilee ^a   Gray, Rebecca ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Algorithms; Facebook news feed; Feedback loop; Intuitive theories

Indexed keywords

BEHAVIORAL RESEARCH; COMPUTATION THEORY; HUMAN COMPUTER INTERACTION; HUMAN ENGINEERING; ONLINE SYSTEMS; SOCIAL NETWORKING (ONLINE); SURVEYS;

CAUSAL INFERENCES; FACEBOOK; FEED-BACK LOOP; INTUITIVE THEORIES; NEWS FEEDS; OPEN-ENDED RESPONSE; SOCIOTECHNICAL SYSTEMS; USER BEHAVIORS;

ALGORITHMS;

EID: 84951152865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2702123.2702174     Document Type: Conference Paper

References (32)

1. Social Networking Fact Sheet, 2014. http://www.pewinternet.org/fact-sheets/socialnetworking-fact-sheet/.
2. Backstrom, L. A Window Into News Feed, Aug. 2013. https://www.facebook.com/business/news/News-Feed-FYI-A-Window-Into-News-Feed.
3. Bakshy, E., Rosenn, I., Marlow, C., and Adamic, L. The Role of Social Networks in Information Diffusion. In WWW 2012 (2012), 519-528.
4. Bernstein, M. S., Suh, B., Hong, L., Chen, J., Kairam, S., and Chi, E. H. Eddi: Interactive topic-based browsing of social status streams. In UIST '10, ACM (2010), 303-312.
5. Bozdag, E. Bias in algorithmic filtering and personalization. Ethics and Information Technology 15, 3 (2013), 209-227.
6. Bucher, T. Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society 14, 7 (Oct. 2012), 1164-1180.
7. Chen, J., Nairn, R., and Chi, E. H. Speak Little and Well: Recommending Conversations in Online Social Streams. In CHI '11 (2011), 217-226.
8. Clegg, C. W. Sociotechnical principles for system design. Applied Ergonomics (2000).
9. Facebook. Company Info: Facebook Newsroom. http://newsroom.fb.com/company-info/, 2014.
10. Facebook. How News Feed Works. https://www.facebook.com/help/327131014036297/, 2014.
11. Fleder, D., and Hosanagar, K. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management science 55, 5 (2009), 697-712.
12. Gelman, S. A., and Legare, C. H. Concepts and folk theories. Annual Review of Anthropology (2011).
13. Gillespie, T. The Relevance of Algorithms. In Media Technologies Essays on Communication, Materiality, and Society, P. Boczkowski and K. Foot, Eds. MIT Press, 2014.
14. Hallinan, B., and Striphas, T. Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society (2014).
15. Hamilton, K., Karahalios, K., Sandvig, C., and Eslami, M. A path to understanding the effects of algorithm awareness. In CHI EA '14 (2014), 631-642.
16. Hogan, B. From Invisible Algorithms to Interactive Affordances: Data After the Ideology of Machine Learning. In Roles, Trust, and Reputation in Social Media Knowledge Markets. Springer International Publishing, Cham, 2015, 103-117.
17. Jannach, D., Lerche, L., Gedikli, F., and Bonnin, G. What recommenders recommend-an analysis of accuracy, popularity, and sales diversity effects. User Modeling, Adaptation and Personalization (2013), 25-37.
18. Joinson, A. N. 'Looking at', 'Looking up' or 'Keeping up with' People? In CHI '08 (Florence, Italy, Apr. 2008), 1027-1036.
19. Jones, N. A., Ross, H., Lynam, T., Perez, P., and Leitch, A. Mental Models: An Interdisciplinary Synthesis of Theory and Methods. Ecology And Society 16, 1 (2011).
20. Kramer, A. D. I., Guillory, J. E., and Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111, 24 (2014), 8788-8790.
21. Lavie, T., Sela, M., Oppenheim, I., Inbar, O., and Meyer, J. User attitudes towards news content personalization. International Journal of Human-Computer Studies 68, 8 (2010), 483-495.
22. Lenhart, A. Adults and social network sites. Tech. rep., Pew Research Center, 2009.
23. Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X., and Chang, Y. Learning to Model Relatedness for News Recommendation. In WWW 2011 (2011), 57-66.
24. Meadows, D. H. Thinking in Systems: A Primer. Chelsea Green Publishing, 2008.
25. Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., and Konstan, J. A. Exploring the filter bubble: The effect of using recommender systems on content diversity. In WWW '14 (2014), 677-686.
26. Owens, E., and Vickrey, D. News Feed FYI: Showing More Timely Stories from Friends and Pages. http://newsroom.fb.com/news/2014/09/news-feed-fyishowing-more-timely-stories-from-friends-and-pages/, September 2014.
27. Rouse, W. B., and Serban, N. Understanding change in complex socio-technical systems. Information Knowledge Systems Management 10, 1-4 (Jan. 2011).
28. Sharma, A., and Cosley, D. Network-Centric Recommendation: Personalization with and in Social Networks. In IEEE SocialCom 2011 (2011), 282-289.
29. Sharma, A., Gemici, M., and Cosley, D. Friends, Strangers, and the Value of Ego Networks for Recommendation. In ICWSM '13 (2013), 721-724.
30. Singer, J. B. User-generated visibility: Secondary gatekeeping in a shared media space. New Media & Society 16, 1 (Jan. 2014), 55-73.
31. Steck, H. Item popularity and recommendation accuracy. In RecSys '11 (2011), 125-132.
32. Sukumaran, A., Vezich, S., McHugh, M., and Nass, C. Normative influences on thoughtful online participation. In CHI '11, ACM (2011), 3401-3410.