Nevada

In democracies around the world, researchers have used multiple methods to monitor elections using Twitter. These methods, however, have been limited. They often account only for the number of and keywords in relevant tweets. Our research team at the University of South Carolina is working to change this. The team is analyzing support on Twitter for the 2016 presidential candidates. It defines candidate support by four criteria: number of tweets, rhetorical tone, geographical location, and topic. By supplementing preexisting techniques, the team is not only able to say which candidates Twitter users regard more favorably—or unfavorably for that matter—but also where and why Twitter users favor that candidate. These results are promising for politicians and researchers alike hoping to influence and understand public opinion.

In the Nevada primary, for example, Donald Trump and Hillary Clinton should take notes. An analysis of 227,415 tweets gathered between November 2015 and mid-February 2016 revealed that Trump and Clinton are favored in the Nevadan Twittersphere. Aggregate results show that Trump was mentioned most frequently with 197,145 tweets, 73.5% of which were positive. Other candidates did not receive the same attention: Sanders received the second most tweets (12,013) followed by Clinton (7,448), Bush (5,376), Cruz (2,721), and Rubio (2,395). Attention, however, is not always a good thing. Regarding the overall tone of tweets, Sanders’ (69%) was more positive than Clinton’s (59.5%). Results varied on the Republican side. Cruz (57.1%) and Rubio (52.5%) earned mostly positive tweets, while Bush elicited 52.5% negative tweets.

Of course, aggregate results can be misleading. Month-to-month developments are key to conducting a comprehensive analysis. In the Democratic race, for example, Sanders’ aggregate tweet count was larger, but Clinton’s monthly count was more consistent. Clinton lost a few tweets between January and February, while Sanders lost 3,403. Moreover, Sanders’ positive tweets declined by 13.1% between December and February, and Clinton’s rose steadily.

The competition among Republican candidates was also dramatic. Formerly dominating in tweet volume, Trump received one-seventh the number of tweets in February as he did in January. This trend contrasts the increase in tweets related to Cruz, Rubio and Bush. Additionally, Trump received volatile rates of positivity. The Republican candidate earned a record of 84% positive tweets in December, 53% in November, and 66.1% in February. Other Republican candidates’ positive rates were negatively correlated with Trump’s for the same period.

These findings offer a glimpse of the insight that our team’s techniques can produce. While it is too early to say whether these techniques can predict elections, it is clear they can help. Since President Obama pioneered social media politicking in 2008, the strategy has become increasingly prevalent. These techniques thus offer a way to clarify the complex web of politics.