More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior

Abstract

Is social media a valid indicator of political behavior? There is considerable debate about the validity of data extracted from social media for studying offline behavior. To address this issue, we show that there is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance. We demonstrate this result with an analysis of 542,969 tweets mentioning candidates selected from a random sample of 3,570,054,618, as well as Federal Election Commission data from 795 competitive races in the 2010 and 2012 U.S. congressional elections. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition. Our findings show that reliable data about political behavior can be extracted from social media.

The ‘Hungry Gap’: Twitter, local press reporting and urban agriculture activism

“…Sustained debate and discussion were absent, and instead Twitter was used to reinforce and reflect on positions often developed 'off-line', suggesting that this form of social media is a way of signalling partisanship and allegiances rather the debate and engagement (Conover et al, 2012b, Papacharissi, 2002, DiGrazia et al, 2013.…”

“…The role of social media in electoral politics is becoming increasingly well understood, with Twitter in particular proving to be useful in understanding political messages, partisanship and voting intentions (DiGrazia et al, 2013, Conover et al, 2012a, Conover et al, 2011. Such quantitative studies have largely focused on aggregating behaviours rather than on collective action, although the role of Twitter in co-ordinating protests has been observed (Castells, 2012).…”

Tweeting Alone? An Analysis of Bridging and Bonding Social Capital in Online Networks

“…This approach, unlike using hashtags, has been shown to be more appropriate for the analysis of tweets during election campaigns (DiGrazia et al, 2013). The time period spanned from 7 weeks before the run-off election until December 17 2013, which covered the entire legal campaign period for both rounds (N= 1,556,109 tweets).…”

Using Twitter Language to Predict the Real Estate Market

“…While exploiting social media language has not been studied in the real estate domain, use of language predictors has been increasing for other economic-related applications, like measuring the public health using analysis of messages in social media (Paul and Dredze, 2011;Eichstaedt et al, 2015;Culotta, 2014), in addition to predicting stock market exploiting text in social media (Bollen et al, 2011;Zhang et al, 2011;Tsolacos, 2012), and predicting political behaviour considering tweets (DiGrazia et al, 2013). Perhaps the most similar work to ours used manually selected keywords in Google searches to predict the overall US housing market (Wu and Brynjolfsson, 2013).…”