An analysis of sentiments on facebook during the 2016 U.S. presidential election
Social networking sites (SNS) such as Facebook and Twitter, are important spaces for political engagement. SNS have become common elements in political participation, campaigns, and elections. However, little is known about the dynamics between candidate posts and commentator sentiment in response to those posts on SNS. This study enriches computational political science by studying the 2016 U.S. elections and how candidates and commentators engage on Facebook. This paper also examines how online activity might be connected to offline activity and vice versa. We extracted 9,700 Facebook posts by five presidential candidates (Hillary Clinton, Donald Trump, Bernie Sanders, Ted Cruz, and John Kasich) from their official Facebook pages and 12,050,595 comments on those posts. We employed topic modeling, sentiment analysis, and trends detection using wavelet transforms to discover topics, trends, and reactions. Our findings suggest that Republican candidates are more likely to share information on controversial events that have taken place during the election cycle, while Democratic candidates focus on social policy issues. As expected, commentators on Republican candidate pages express negative sentiments toward current public policies as they seldom support decisions made by the Obama administration, while commentators on democratic candidate pages are more likely to express support for continuation or advancement of existing policies. However, the significance (strong/weak) and nature (positive/negative) of sentiments varied between candidates within political parties based on perceived credibility of the candidate's degree of credibility on a given issue. Additionally, we explored correlation between online trends of comments/sentiment and offline events. When analyzing the trend patterns, we found that changes in online trends are driven by three factors: 1) popular post, 2) offline debates, and 3) candidates dropping out of the race.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)