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Information spread in social media depends on a number of factors, includinghow the site displays information, how users navigate it to find items ofinterest, users' tastes, and the `virality' of information, i.e., itspropensity to be adopted, or retweeted, upon exposure. Probabilistic models canlearn users' tastes from the history of their item adoptions and recommend newitems to users. However, current models ignore cognitive biases that are knownto affect behavior. Specifically, people pay more attention to items at the topof a list than those in lower positions. As a consequence, items near the topof a user's social media stream have higher visibility, and are more likely tobe seen and adopted, than those appearing below. Another bias is due to theitem's fitness: some items have a high propensity to spread upon exposureregardless of the interests of adopting users. We propose a probabilistic modelthat incorporates human cognitive biases and personal relevance in thegenerative model of information spread. We use the model to predict howmessages containing URLs spread on Twitter. Our work shows that models of userbehavior that account for cognitive factors can better describe and predictuser behavior in social media.

VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting