Abstract

Information spread in social media depends on a number of factors, including how the site displays information, how users navigate it to find items of interest, users’ tastes, and the ‘virality’ of information, i.e., its propensity to be adopted, or retweeted, upon exposure. Probabilistic models can learn users’ tastes from the history of their item adoptions and recommend new items to users. However, current models ignore cognitive biases that are known to affect behavior. Specifically, people pay more attention to items at the top of a list than those in lower positions. As a consequence, items near the top of a user’s social media stream have higher visibility, and are more likely to be seen and adopted, than those appearing below. Another bias is due to the item’s fitness: some items have a high propensity to spread upon exposure regardless of the interests of adopting users. We propose a probabilistic model that …

Date

March 4, 2026

Authors

Jeon-Hyung Kang, Kristina Lerman

Conference

Social Computing, Behavioral-Cultural Modeling, and Prediction: 8th International Conference, SBP 2015, Washington, DC, USA, March 31-April 3, 2015. Proceedings 8

Pages

101-110

Publisher

Springer International Publishing