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AbstractAlthough previous scholars have used image data to answer important political science questions, less attention has been paid to video-based measures. In this study, I use motion detection to understand the extent to which members of Congress (MCs) literally cross the aisle, but motion detection can be used to study a wide range of political phenomena, like protests, political speeches, campaign events, or oral arguments. I find not only are Democrats and Republicans less willing to literally cross the aisle, but this behavior is also predictive of future party voting, even when previous party voting is included as a control. However, this is one of the many ways motion detection can be used by social scientists. In this way, the present study is not the end, but the beginning of an important new line of research in which video data is more actively used in social science research.

Despite their ubiquity, few have used traffic camera networks for social science research. Using 1,312,977 images collected from 768 London-based cameras leading up to the 2015 UK general election, this study not only demonstrates how traffic camera data can be used to effectively measure same-day turnout, but we also provide ways such data can be used to assess political behavior more broadly. Such automated enumeration is especially important in countries where official results are only returned for the current election, making it difficult for those interested in assessing turnout at lower levels of aggregation, even when those elections are next on the calendar. Although we are not the first to suggest the value of images-as-data, this study hopes to underline the importance of video-as-data, while simultaneously offering an important foundation for future research.

Although audio archives are available for a number of political institutions, the data they provide receive scant attention from researchers. Yet, audio data offer important insights, including information about speakers' emotional states. Using one of the largest collections of natural audio ever compiled—74,158 Congressional floor speeches—we introduce a novel measure of legislators' emotional intensity: small changes in vocal pitch that are difficult for speakers to control. Applying our measure to MCs' floor speeches about women, we show that female MCs speak with greater emotional intensity when talking about women as compared with both their male colleagues and their speech on other topics. Our two supplementary analyses suggest that increased vocal pitch is consistent with legislators' broader issue commitments, and that emotionally intense speech may affect other lawmakers' behavior. More generally, by demonstrating the utility of audio-as-data approaches, our work highlights a new way of studying political speech.

Do judges telegraph their preferences during oral arguments? Using the U.S. Supreme Court as our example, we demonstrate that Justices implicitly reveal their leanings during oral arguments, even before arguments and deliberations have concluded. Specifically, we extract the emotional content of over 3,000 hours of audio recordings spanning 30 years of oral arguments before the Court. We then use the level of emotional arousal, as measured by vocal pitch, in each of the Justices' voices during these arguments to accurately predict many of their eventual votes on these cases. Our approach yields predictions that are statistically and practically significant and robust to including a range of controls; in turn, this suggests that subconscious vocal inflections carry information that legal, political, and textual information do not.

The impact of personality traits on people's attitudes and behaviors is widely recognized, yet systematic attention to personality in large‐N research on elected officials has been rare. Among psychologists, five‐factor frameworks that focus on openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability have gained tremendous prominence in the past two decades. Applications of these frameworks to the study of mass political behavior have been highly fruitful, but corresponding applications in the study of legislators have been rare. In an effort to assess the utility of a Big Five approach in the study of legislative politics, this article addresses three questions: whether elected officials will be willing to provide personality self‐assessments, whether any data they do provide will exhibit meaningful variance, and whether the Big Five trait dimensions will correspond with patterns in respondents' attitudes and behaviors. These questions are addressed using data from members of the state legislatures in Arizona, Connecticut, and Maine. Results provide considerable grounds for optimism regarding the likely utility of more extensive applications of the Big Five in research on elected officials.