Using a model of social dynamics to predict popularity of news

By Kristina Lerman, Tad Hogg. The Web Conference, 2010

326 citations recorded by the source corpus.

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 companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity a priori, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/78a906fd6f0785509af980986f11fd84d851b20b

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In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes (2014, background): “Furthermore, intrinsic attractiveness of item and the underlying social network structure are explored and incorporated into the time series analysis to improve the predictive power of these methodology [3, 14].”
Chic or Social: Visual Popularity Analysis in Online Fashion Networks (2014, background): “Work in this direction has mainly looked into early social reaction to content and the prediction of popularity growth in videos [5, 16, 14, 3, 4], news [10, 18], and discussion forums [9].”
Modeling Interestingness with Deep Neural Networks (2014, unknown): “There are many systems that identify popular content in the Web or recommend content (e.g., Bandari et al. 2012; Lerman and Hogg 2010; Szabo and Huberman 2010), which is closely re-lated to the highlighting task.”
Social dynamics of Digg (2010, methodology): “This model successfully captured the qualitative behavior of typical stories on Digg and how that behavior depended on the number of fans of the story’s submitter [2, 9].”
Using early view patterns to predict the popularity of youtube videos (2013, methodology): “…Subject Descriptors C.4 [Computer Systems Organization]: Performance of Systems - Measurement techniques; H.3.5 [Information Storage and Retrieval]: Online Information Services - Web-based services General Terms Experimentation, Measurement Keywords popularity prediction; YouTube; regression models”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “Lerman et al (2009; 2010) studied popularity on Digg, and demonstrated that popularity prediction accuracy could be improved by tailoring a model to the algorithm and interface that Digg used.”
The Pulse of News in Social Media: Forecasting Popularity (2012, methodology): “[5] predict digg counts using stochastic models that combine design elements of the site -that in turn lead to collective user behavior- with information from early votes.”
Offline and online evaluation of news recommender systems at swissinfo.ch (2014, unknown): “improve the performance [13].”