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.

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.

Objectives. This study addresses differences in the predictors of participation in different forms of protest activity using nationally representative data. The two types of protest examined, referred to as conventional and unconventional forms of activism, are differentiated by differing levels of risk, demands, and political legitimacy. Methods. The analysis uses multinominal logistic regression and data from the World Values Survey to assess the effects of a wide range of independent variables on participation in protest. Results. The results indicate that participation in conventional forms of protest, activities that are relatively undemanding, socially legitimate, and low risk, tend to follow patterns that are consistent with participation in institutional politics. That is, participants in this form of activism tend to be socially privileged and ideologically moderate. Participants in unconventional protest, those that are highly demanding, socially illegitimate, or carry substantial risks, tend to be more ideologically extreme, socially disadvantaged, and more alienated from the conventional political system. Conclusions.

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This study proposes using Internet search data from search engines like Google to produce state-level metrics that are useful in social science research. Generally, state-level research relies on demographic statistics, official statistics produced by government agencies, or aggregated survey data. However, each of these data sources has serious limitations in terms of both the availability of the data and its ability to capture important concepts. This study demonstrates how state-level Google search measures can be produced and then demonstrates the effectiveness of such measures in an empirical case: predicting state-level Tea Party movement mobilization. Drawing on existing studies of the Tea Party movement and theories of right-wing and conservative mobilization, state-level Google search measures for anti-immigrant sentiment and economic distress are developed and compared to traditional metrics that are typically used to measure these concepts, such as the unemployment rate and the international immigration rate in their ability to successfully predict Tea Party event counts. The results show that the Google search measures are effective in predicting Tea Party mobilization in a way that is consistent with existing theory, while the traditional measures are not.