Social media sites such as Twitter have faced significant pressure to mitigate spam and abuse on their platform in the aftermath of congressional investigations into Russian interference in the 2016 U.S. presidential election. Twitter publicly acknowledged the exploitation of their platform and has since conducted aggressive cleanups to suspend the involved accounts. To shed light on Twitter's countermeasures, we conduct a postmortem analysis of about one million Twitter accounts who engaged in the 2016 U.S. presidential election but were later suspended by Twitter. To systematically analyze coordinated activities of these suspended accounts, we group them into communities based on their retweet/mention network and analyze different characteristics such as popular tweeters, domains, and hashtags. The results show that suspended and regular communities exhibit significant differences in terms of popular tweeter and hashtags. Our qualitative analysis also shows that suspended communities are heterogeneous in terms of their characteristics. We further find that accounts suspended by Twitter's new countermeasures are tightly connected to the original suspended communities.

The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls.

Political sentiment analysis using social media, especially Twitter, has attracted wide interest in recent years. In such research, opinions about politicians are typically divided into positive, negative, or neutral. In our research, the goal is to mine political opinion from social media at a higher resolution by assessing statements of opinion related to the personality traits of politicians; this is an angle that has not yet been considered in social media research. A second goal is to contribute a novel retrieval‐based approach for tracking public perception of personality using Gough and Heilbrun's Adjective Check List (ACL) of 110 terms describing key traits. This is in contrast to the typical lexical and machine‐learning approaches used in sentiment analysis. High‐precision search templates developed from the ACL were run on an 18‐month span of Twitter posts mentioning Obama and Romney and these retrieved more than half a million tweets. For example, the results indicated that Romney was perceived as more of an achiever and Obama was perceived as somewhat more friendly. The traits were also aggregated into 14 broad personality dimensions. For example, Obama rated far higher than Romney on the Moderation dimension and lower on the Machiavellianism dimension. The temporal variability of such perceptions was explored.

As mainstream news media and political campaigns start to pay attention to the political discourse online, a systematic analysis of political speech in social media becomes more critical. What exactly do people say on these sites, and how useful is this data in estimating political popularity? In this study we examine Twitter discussions surrounding seven US Republican politicians who were running for the US Presidential nomination in 2011. We show this largely negative rhetoric to be laced with sarcasm and humor and dominated by a small portion of users. Furthermore, we show that using out-of-the-box classification tools results in a poor performance, and instead develop a highly optimized multi-stage approach designed for general-purpose political sentiment classification. Finally, we compare the change in sentiment detected in our dataset before and after 19 Republican debates, concluding that, at least in this case, the Twitter political chatter is not indicative of national political polls.