Stochastic Models Predict User Behavior in Social Media

Stochastic Models Predict User Behavior in Social Media. Hogg, T., Lerman, K., & Smith, L. M. ASE HUMAN, 2013.

User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user transitioning through a series of states before responding to an advocate's post better predicts response than models that fail to take these states into account. We demonstrate other benefits of stochastic models, such as their ability to identify users who are highly interested in advocate's posts.

@article{Hogg13socialcom,
    abstract = {User response to contributed content in online social media depends on many
factors. These include how the site lays out new content, how frequently the
user visits the site, how many friends the user follows, how active these
friends are, as well as how interesting or useful the content is to the user.
We present a stochastic modeling framework that relates a user's behavior to
details of the site's user interface and user activity and describe a procedure
for estimating model parameters from available data. We apply the model to
study discussions of controversial topics on Twitter, specifically, to predict
how followers of an advocate for a topic respond to the advocate's posts. We
show that a model of user behavior that explicitly accounts for a user
transitioning through a series of states before responding to an advocate's
post better predicts response than models that fail to take these states into
account. We demonstrate other benefits of stochastic models, such as their
ability to identify users who are highly interested in advocate's posts.},
    author = {Hogg, Tad and Lerman, Kristina and Smith, Laura M.},
    journal = {ASE HUMAN},
volume={2},
number={1},
    keywords = {social-dynamics},
    title = {Stochastic Models Predict User Behavior in Social Media},
    urlPaper = {http://arxiv.org/abs/1308.2705},
    year = {2013}
}

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