VIP: Incorporating human cognitive biases in a probabilistic model of retweeting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9021, Issue , 2015, Pages 101-110

  Kang, Jeon Hyung ^a   Lerman, Kristina ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Cognitive factors; Information diffusion; Social media

Indexed keywords

FORECASTING; SOCIAL NETWORKING (ONLINE);

COGNITIVE FACTORS; GENERATIVE MODEL; INFORMATION DIFFUSION; NUMBER OF FACTORS; PROBABILISTIC MODELING; PROBABILISTIC MODELS; SOCIAL MEDIA; USER BEHAVIORS;

BEHAVIORAL RESEARCH;

EID: 84925302545     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16268-3_11     Document Type: Conference Paper

References (25)

1. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM (2011)
2. Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: WWW (2012)
3. Blunch, N.J.: Position bias in multiple-choice questions. Journal of Marketing Research 21(2), 216–220 (1984)
4. Buscher, G., Cutrell, E., Morris, M.R.: What do you see when you’re surfing?: using eye tracking to predict salient regions of web pages. In: SIGCHI (2009)
5. Chaudhry, A., Glodé, L.M., Gillman, M., Miller, R.S.: Trends in twitter use by physicians at the american society of clinical oncology annual meeting, 2010 and 2011. Journal of Oncology Practice 8(3), 173–178 (2012)
6. Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: WWW (2014)
7. Counts, S., Fisher, K.: Taking it all in? visual attention in microblog consumption. In: ICWSM (2011)
8. Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: WSDM (2008)
9. Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: EC (2012)
10. Hodas, N., Lerman, K.: How limited visibility and divided attention constrain social contagion. In: SocialCom (2012)
11. Hogg, T., Lerman, K.: Social dynamics of digg. EPJ Data Science 1(5) (June 2012)
12. Hogg, T., Lerman, K., Smith, L.M.: Stochastic models predict user behavior in social media. In: SocialCom (2013)
13. Huberman, B.A.: Strong Regularities in World Wide Web Surfing. Science 280(5360), 95–97 (1998)
14. Kang, J.-H., Lerman, K.: LA-CTR: A limited attention collaborative topic regression for social media. In: AAAI (2013)
15. Kang, J.-H., Lerman, K., Getoor, L.: LA-LDA: A limited attention model for social recommendation. In: SBP (2013)
16. Lerman, K., Hogg, T.: Leveraging position bias to improve peer recommendation. PLoS One 9(6), e98914 (2014)
17. Ma, H., Yang, H., Lyu, M., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940. ACM (2008)
18. Payne, S.L.: The Art of Asking Questions. Princeton University Press (1951)
19. Rodriguez, M.G., Gummadi, K., Schoelkopf, B.: Quantifying information overload in social media and its impact on social contagions. In: ICWSM (2014)
20. Romero, D.M., Galuba, W., Asur, S., Huberman, B.A.: Influence and passivity in social media. In: WWW (2011)
21. Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In: WWW (2011)
22. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20, 1257–1264 (2008)
23. Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: KDD (2011)
24. Wang, D., Song, C., Barabási, A.-L.: Quantifying long-term scientific impact. arXiv preprint (2013). arXiv:1306.3293
25. Weng, L., Menczer, F., Ahn, Y.-Y.: Virality prediction and community structure in social networks. arXiv preprint (2013). arXiv:1306.0158