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ABSTRACTCongressional candidates regularly turn their frustration into posts on Facebook, fueling extreme partisanship and "echo-chamber" dialogue with their negative sentiment. In this research, we provide new evidence demonstrating the power of that negative sentiment to elicit more user engagement on Facebook across various metrics, illustrating how congressional candidates' use of negativity corresponds with greater negativity in public responses. To fully comprehend the impact of these online political messages, we use a dictionary-based computational approach to catalog the tone of US House of Representatives candidates' messages on Facebook and the user responses they elicit during the 2020 election. This research speaks to the power of elite rhetoric to shape political climates and pairs candidate strategies with user responses—contributing new insights into the mechanisms for voter engagement.

Understanding how Members of Congress (MCs) distribute their political attention is key to a number of areas of political science research including agenda setting, framing, and issue evolution. Tweets illuminate what lawmakers are paying attention to by aggregating information from newsletters, press releases, and floor debates to provide a birds-eye view of a lawmaker's diverse agenda. In order to leverage this data efficiently, we trained a supervised machine learning classifier to label tweets according to the Comparative Agenda Project's Policy Codebook and used the results to examine the differential attention that policy topics receive from MCs. The classifier achieved an F1 score of 0.79 and a Cohen's kappa with human labelers of 0.78, suggesting good performance. Using this classifier, we labeled 1,485,834 original MC tweets (Retweets were excluded) and conducted a multinomial logistic regression to understand what influenced the policy areas MCs Tweeted about. Our model reveals differences in political attention along party, chamber, and gender lines and their interactions. Our approach allows us to study MCs' political attention in near real-time and to uncover both intra- and inter-group differences. ; https://deepblue.lib.umich.edu/bitstream/2027.42/148323/1/Rhetorical Agenda for MPSA 2019.pdf ; https://deepblue.lib.umich.edu/bitstream/2027.42/148323/5/Political Attention under review.pdf ; https://deepblue.lib.umich.edu/bitstream/2027.42/148323/6/Political Attention Supplementary Docs.pdf ; http://deepblue.lib.umich.edu/bitstream/2027.42/148323/9/Hemphill et al What drives attention.pdf ; 50 ; 56 ; Description of Rhetorical Agenda for MPSA 2019.pdf : Paper presented at MPSA 2019 ; Description of Political Attention under review.pdf : Manuscript submitted for peer review ; Description of Political Attention Supplementary Docs.pdf : Peer review copy supplementary docs

AbstractObjectiveCongressional candidates use digital platforms to bolster and define their political reputation, and political stalemates over inflation, reproductive rights, and the lasting impact of Trump politics are fueling candidates' emotionally charged rhetoric on Twitter, especially for women. Against the backdrop of President Donald Trump's presidency and the #MeToo movement, previous research has shown that women running for Congress are leading with angry rhetoric on Twitter. In this article, we ask whether anger is a persistent feature of women's digital appeals on Twitter over time.MethodUsing a data set of tweets by candidates for the U.S. House from 2016 to 2022, we highlight the escalating anger in the emotional appeals candidates make on Twitter and the resiliency of angry rhetoric as a modern feature of political Twitter.ResultsWe find that women, most notably Democratic candidates, are more likely to convey angry emotions on Twitter, not only matching male colleagues but defying gendered social stereotypes to turn frustration into a valuable political asset. Across the four last congressional elections, women have averaged more angry words in their digital appeals, with that anger as a consistent facet of how women engage online. Women are leaning into angry emotional appeals and adopting a negative appeal in their digital engagement that highlights their policy and political frustrations for voters.

"The Mass Media and the Policy Process" published on by Oxford University Press.

Existing literature on the agenda-setting process is grounded and well cited in studies of U.S. national institutions, but emerging scholarship has taken the fundamental principles of agenda setting-attention, information, and learning-and has extended their applicability to understudied participants and institutions. This essay highlights three areas of study that have undergone particular growth during the last few years and best represent the trend of applying the well understood dynamics of agenda setting to a broader swath of participants in the policy process. We first examine how scholars have focused on agenda setting within U.S. state and local governments and the way these institutions balance their agenda-setting needs internally, while still trying to be heard within a federal system. Second, we highlight policy scholars' contributions to create better definitions and measures of the relationship between the media and policy process. Finally, we explore the contributions to the broader agenda-setting literature made by scholars examining non-U.S. institutions. These three categories are but a part of the growing trend in the subfield to expand the scope of agenda-setting research. Adapted from the source document.

ABSTRACTThis article considers whether candidates strategically use emotional rhetoric in social media messages similar to the way that fear appeals are used strategically in televised campaign advertisements. We use a dataset of tweets issued by the campaign accounts of candidates for the US House of Representatives during the last two months of the 2018 midterm elections to determine whether candidate vulnerability predicts the presence of certain emotions in social media messages. Contrary to theoretical expectations, we find that vulnerability does not appear to inspire candidates to use more anxious language in their tweets. However, we do find evidence of a surprising relationship between sad rhetoric and vulnerability and that campaign context influences the use of other forms of negative rhetoric in tweets.

Social media platforms like Twitter enable policymakers to communicate their policy preferences directly and provide a bird's‐eye view of their diverse policy agendas. In this article, we leverage politicians' social media data to study political attention using a supervised machine‐learning classifier that detects policy areas in individual tweets. We examine how individual diversity and institutional factors affect differential attention to public policy among members of the U.S. Congress. Our novel approach to measuring policy attention builds on work by the Comparative Agendas Project, in order to study members' political attention in near real‐time and to uncover both intragroup and intergroup differences. Using this classifier, we labeled more than one million tweets and found statistically significant differences in both the level and distribution of attention between parties, chambers, and genders. However, these differences were small enough to suggest that other Congressional members' characteristics are also at play. We explored institutional factors (e.g., committee assignment, caucus), partisan issue preferences (e.g., issue ownership), and the political environment (e.g., partisan issues, confirmations, etc.) that may help explain the patterns of political attention that appear in Congress's tweets.

AbstractThis study examines the effects of both the substantive and affective dimensions of issue attributes in the news coverage of climate change on the public's perception of the importance of this environmental issue. Results from our analysis show that the four affective dimensions (e.g., positive and negative emotions, anger, and sadness) of the three attributes (e.g., existence, effects, and solutions) exerted strong influence on public issue priority. This study extends the concept of compelling arguments in agenda setting research by suggesting that compelling arguments effects are not solely dependent on substantive attributes. Their affective dimensions are influential, as well.

Policy Analysis in the United States brings together contributions from some of the world's leading scholars and practitioners of public policy analysis including Beryl Radin, David Weimer, Rebecca Maynard, Laurence Lynn, and Guy Peters. This volume represents an indispensable companion to other volumes in the International Library of Policy Analysis series, enabling scholars to compare cross-nationally concepts and practices of public policy analysis in the media, sub-national governments, and many more institutional settings. The volume represents an invaluable contribution to public policy analysis and can be used widely in teaching at both graduate and undergraduate levels in schools of public affairs and public policy as well as in comparative politics and policy