Ideological differences in engagement in public debate on Twitter

Americans misperceive the frequency and format of political debate

Disagreement over divergent viewpoints seems like an ever-present feature of American life-but how common is debate and with whom do debates most often occur? In the present research, we theorize that the landscape of debate is distorted by social media and the salience of negativity present in high-profile spats. To understand the true landscape of debate, we conducted three studies (N = 2985) across online and lab samples. In contrast to the high-profile nature of negative debates with strangers, we found that people most commonly debate close contacts, namely family members and good friends. In addition, they often report feeling positive after engaging in debate. We then directly measured misperceptions regarding debate in a representative sample of Americans (N = 1991). We found that Americans systematically overestimated how often others engage in debate. This overestimation extended across debate partners (family members, good friends, acquaintances, coworkers, and strangers) and contexts (in-person and online; p's < 0.001, d's > 0.98), most strongly overestimating how often Americans debate strangers online. This misprediction may be psychologically costly: overestimating how often Americans debate strangers online significantly predicted greater hopelessness in the future of America. Together, our findings suggest that Americans may experience a false reality about the landscape of debate which can unnecessarily undermine their hope about the future.

Debating stereotypes: Online reactions to the vice-presidential debate of 2020

The 2020 Vice-Presidential debate afforded the opportunity to examine online reactions toward a woman of color, Kamala Harris, and a white man, Mike Pence, as they vied for the same position. We collected tweets from the Twitter API related to Harris and Pence, mainly using neutral hashtags. We examined keywords for gender and race slurs and conducted a multivariate analysis of tweet sentiment. Gender and racial slurs surface in both Harris and Pence datasets, showcasing the insidious nature of sexist and racist stereotypes that seep into online conversations regarding a high-status job debate. As anticipated, tweets regarding Harris contained a higher proportion of racist and sexist curse words, highlighting greater levels of harassment and "intersectional," multi-ethnic/gender attacks. Racial insults targeting Blacks or Asians were more negative than those associated with Whites. Unexpectedly, tweets related to Harris were more positive in average sentiment than those regarding Pence. Yet, there were significantly more retweets, and more negativity of retweets, relating to Harris than to Pence, underscoring the relatively widespread broadcasting of derogatory messages about Harris. Overall, we found that harassing messages toward the candidates reinforced traditional race and gender stereotypes and bolstered the status of those who posted negative content by attaining more retweets. Harassers routinely invoked well-worn, stereotypical insults in their attacks, especially when targeting a multiracial woman.

Studying topic engagement and synergy among candidates for 2020 US Elections

This article provides a comprehensive summary of how candidates running in the 2020 US Presidential Elections used Twitter to communicate with the public. More specifically, it aims to uncover elements linked to public engagement and internal cooperation (in terms of content and stance similarity among the candidates from the same political front, and with respect to the official Twitter accounts of their political parties). Our main subjects are the Presidential and Vice-Presidential candidates who contested for the 2020 US Elections from the two major political fronts—Republicans and Democrats. Their tweets were evaluated for social reach, content similarity and stance similarity on 22 topics. According to the findings, Joe Biden had the highest engagement and impact (user impact: 177.08k, normalized to 0.99), followed by Donald Trump (user impact: 164.19k, normalized to 0.92). The Democrats depicted a clearer understanding of their audience, portraying an essential link between public participation, internal cooperation and the electoral campaign. The results also demonstrate that specific topics (like US Elections, and Inauguration Ceremony) were more engaging than others (Trump Healthcare Plan, and The Supreme Court Appointments). This study adds to the existing work on using social media platforms for electoral campaigns and can be effectively utilized by contesting candidates.

Big data and development sociology: An overview and application on governance and accountability through digitalization in Tanzania

The digital revolution and the widespread use of the internet have changed many realms of empirical social science research. In this paper, we discuss the use of big data in the context of development sociology and highlight its potential as a new source of data. We provide a brief overview of big data and development research, discuss different data types, and review example studies, before introducing our case study on active citizenship in Tanzania which expands on an Oxfam-led impact evaluation. The project aimed at improving community-driven governance and accountability through the use of digital technology. Twitter and other social media platforms were introduced to community animators as a tool to hold national and regional key stakeholders accountable. We retrieve the complete Twitter timelines up to October 2021 from all ~200 community animators and influencers involved in the project (over 1.5 million tweets). We find that animators have started to use Twitter as part of the project, but most have stopped tweeting in the long term. Employing a dynamic difference-in-differences design, we also do not find effects of Oxfam-led training workshops on different aspects of animators' tweeting behavior. While most animators have stopped using Twitter in the long run, a few have continued to use social media to raise local issues and to be part of conversations to this day. Our case study showcases how (big) social media data can be part of an intervention, and we end with recommendations on how to use digital data in development sociology.