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.
Social media data offer a rich resource for researchers interested in public health, labor economics, politics, social behaviors, and other topics. However, scale and anonymity mean that researchers often cannot directly get permission from users to collect and analyze their social media data. This article applies the basic ethical principle of respect for persons to consider individuals’ perceptions of acceptable uses of data. We compare individuals’ perceptions of acceptable uses of other types of sensitive data, such as health records and individual identifiers, with their perceptions of acceptable uses of social media data. Our survey of 1018 people shows that individuals think of their social media data as moderately sensitive and agree that it should be protected. Respondents are generally okay with researchers using their data in social research but prefer that researchers clearly articulate benefits and seek explicit consent before conducting research. We argue that researchers must ensure that their research provides social benefits worthy of individual risks and that they must address those risks throughout the research process.
Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models—one supervised classifier and one unsupervised topic model—provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.
In 2018 the United Nations High Commissioner for Refugees asserted that there are 25.4 million refugees worldwide. News media, state actors, and other bodies speak about refugees in ways that emphasize certain aspects of their experiences. We do not often hear how those identified as refugees speak about themselves and how they navigate their identities in the context of information. This article asks: How do self-identified refugee communities in Athens, Greece, and Hamburg, Germany, engage with information spaces during their refugee experiences to navigate identity in new receiving-society contexts? Drawing on Erving Goffman (1959) and Webb Keane’s (1997) idea that information transmission through interaction is at the center of identity development, this research uses a mixed method of semi-structured interviews and embedded participant observation. The findings expose three challenges to identity navigation at both sites: prolonged liminality, unfamiliar information spaces within receiving societies, and misinformed information spaces within receiving societies. In addressing these challenges, participants balanced tremendous effort and agency with the effects of systems beyond their control. The implications of our findings relate to the viability of liminality theories and the need for policy modifications to encourage receiving societies to assume responsibility for aspects of identity work within their control.
Moderating content on social media can lead to severe psychological distress. However, little is known about the type, severity, and consequences of distress experienced by volunteer content moderators (VCMs), who do this work voluntarily. We present results from a survey that investigated why Facebook Group and subreddit VCMs quit, and whether reasons for quitting are correlated with psychological distress, demographics, and/or community characteristics. We found that VCMs are likely to experience psychological distress that stems from struggles with other moderators, moderation team leads’ harmful behaviors, and having too little available time, and these experiences of distress relate to their reasons for quitting. While substantial research has focused on making the task of detecting and assessing toxic content easier or less distressing for moderation workers, our study shows that social interventions for VCM workers, for example, to support them in navigating interpersonal conflict with other moderators, may be necessary.
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