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This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in 6 million speeches given in U.S. Congress over the years 1858-2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased significantly starting in the late 1970s. Across Congress Members, emotionality is higher for Democrats, for women, for ethnic/religious minorities, for the opposition party, and for members with ideologically extreme roll-call voting records. ; ISSN:0013-0133 ; ISSN:1468-0297

Abstract
This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in six million speeches given in U.S. Congress over the years 1858–2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased significantly starting in the late 1970s. Across Congress members, emotionality is higher for Democrats, for women, for ethnic/religious minorities, for the opposition party and for members with ideologically extreme roll-call voting records.

We leverage on important findings in social psychology to build a behavioral theory of protest vote. An individual develops a feeling of resentment if she loses income over time while richer people do not, or if she does not gain as others do, i.e. when her relative deprivation increases. In line with the Intergroup Emotions Theory, this feeling is amplified if the individual identifies with a community experiencing the same feeling. Such a negative collective emotion, which we define as aggrievement, fuels the desire to take revenge against traditional parties and the richer elite, a common trait of populist rhetoric. The theory predicts higher support for the protest party when individuals identify more strongly with their local community and when a higher share of community members are aggrieved. We test this theory using longitudinal data on British households and exploiting the emergence of the UK Independence Party (UKIP) in Great Britain in the 2010 and 2015 national elections. Empirical findings robustly support theoretical predictions. The psychological mechanism postulated by our theory survives the controls for alternative non-behavioral mechanisms (e.g. information sharing or political activism in local communities).

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation—either by governments or social media companies—can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies—empathy, warning of consequences, and humor—or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions. ; ISSN:0027-8424 ; ISSN:1091-6490

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation—either by governments or social media companies—can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies—empathy, warning of consequences, and humor—or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.