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Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
ACM Transactions on Intelligent Systems and Technology | Year: 2012

The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network ("niche interests") from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story. © 2012 ACM. Source

Hogg T.,Institute for Molecular Manufacturing | Lerman K.,University of Southern California
EPJ Data Science | Year: 2012

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user’s friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users’ friends from those of interest to the entire user community. We show how this model predicts a story’s eventual popularity from users’ early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site. © 2012 Hogg and Lerman; licensee Springer. Source

Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
PLoS ONE | Year: 2014

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation. © 2014 Lerman, Hogg. Source

Hogg T.,Institute for Molecular Manufacturing | Lerman K.,Information science Institute | Smith L.M.,Information science Institute
Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 | Year: 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 discovering the advocate's post by scanning through a list of newer posts, better predicts response than models that do not. © 2013 IEEE. Source

Lerman K.,University of Southern California | Hogg T.,Institute for Molecular Manufacturing
AAAI Workshop - Technical Report | Year: 2013

Online crowdsourcing provides new opportunities for ordinary people to create original content. This has led to a rapidly growing volume of user-generated content, and consequently a challenge to readily identify high quality items. Due to people's limited attention, the presentation of content strongly affects how people allocate effort to the available content. We evaluate this effect experimentally using Amazon Mechanical Turk and show that it is possible to manipulate attention to accomplish desired goals. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved. Source

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