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

VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting. Kang, J. & Lerman, K. In International Conference on Social Computing, Behavioral Modeling and Prediction, April, 2015.

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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 incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.

@INPROCEEDINGS{Kang15sbp,
  AUTHOR =       {Jeon-hyung Kang and Kristina Lerman},
  TITLE =        {VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting},
  BOOKTITLE =    {International Conference on Social Computing, Behavioral Modeling and Prediction},
  YEAR =         {2015},
  month =        {April},
  url = {http://arxiv.org/abs/1502.00582},
  keywords =     {social-networks},
  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 incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.},
}

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