[KDD 2020] Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction
Aug 13, 20202 views
Modeling user engagement dynamics on social media has compelling applications in market trend analysis, user-persona detection, and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large number of discussions happen on platforms that either,lack any reliable social network (news portal, blogs, Buzzfeed) or reveal only partially the inter-user ties (Reddit, Stackoverflow). Many,approaches require observing a discussion for some considerable,period before they can make useful predictions. In real-time streaming scenarios, observations incur costs. Lastly, most models do not,capture complex interactions between exogenous events (such as,news articles published externally) and in-network effects (such as,follow-up discussions on Reddit) to determine engagement levels.,To address the three limitations noted above, we propose a novel,framework,,ChatterNet,, which, to our knowledge, is the first that,can model and predict user engagement,without considering the,underlying user network,. Given streams of timestamped news articles and discussions, the task is to observe the streams for a short,period leading up to a time horizon, then predict,chatter,: the volume of discussions through a specified period after the horizon.,ChatterNet,processes text from news and discussions using a novel,time-evolving recurrent network architecture that captures both,temporal properties within news and discussions, as well as influence of news on discussions. We report on extensive experiments,using a two-month-long discussion corpus of Reddit, and a contemporaneous corpus of online news articles from the Common,Crawl.,ChatterNet,shows considerable improvements beyond recent state-of-the-art models of engagement prediction. Detailed,studies controlling observation and prediction windows, over,43,different subreddits, yield further useful insights.