Uncovering topic dynamics of social media and news: The case of ferguson

Abstract

Looking at the dynamics of news content and social media content can help us understand the increasingly complex dynamics of the relationship between the media and the public surrounding noteworthy news events. Although topic models such as latent Dirichlet allocation (lda) are valuable tools, they are a poor fit for analyses in which some documents, like news articles, tend to incorporate multiple topics, while others, like tweets, tend to be focused on just one. In this paper, we propose Single Topic lda (st-lda) which jointly models news-type documents as distributions of topics and tweets as having a single topic; the model improves topic discovery in news and tweets within a unified topic space by removing noisy topics that conventional lda tends to assign to tweets. Using st-lda, we focus on the unrest in Ferguson, Missouri after the fatal shooting of Michael Brown on August 9, 2014, looking in particular at the topic dynamics of tweets in and out of St. Louis area, and at differences and relationships between topic coverage in news and tweets.