Dataset Information

Partisan styles of self-presentation in U.S. Twitter bios.

ABSTRACT: Political polarization in the United States goes beyond divided opinions on key political issues, extending to realms of culture, lifestyle, and social identity once thought to be apolitical. Using a sample of 1 million Twitter bios, this study investigates how users' partisan self-presentation on social media tends to include cultural as well as political markers. Representing the text in Twitter bios as semantic networks, the study reveals clear partisan differences in how users describe themselves, even on topics that seem apolitical. Consequently, active Twitter users' political alignments can be statistically inferred from the non-political references in their bios, even in the absence of explicitly partisan language. These findings offer further evidence of partisan polarization that is aligned with lifestyle preferences. Further research is needed to determine if users are aware of that alignment, which might indicate the politicization of lifestyle preferences. The findings also suggest an under-recognized way social media can promote polarization, not through political discourse or argument, but simply in how users present cultural and lifestyle preferences on those platforms.

SUBMITTER: Essig L

PROVIDER: S-EPMC10784547 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Partisan styles of self-presentation in U.S. Twitter bios.

Essig Liam L DellaPosta Daniel D

Scientific reports 20240111 1

Political polarization in the United States goes beyond divided opinions on key political issues, extending to realms of culture, lifestyle, and social identity once thought to be apolitical. Using a sample of 1 million Twitter bios, this study investigates how users' partisan self-presentation on social media tends to include cultural as well as political markers. Representing the text in Twitter bios as semantic networks, the study reveals clear partisan differences in how users describe thems ...[more]

PMID: 38212630

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Project description:The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.Supplementary informationThe online version contains supplementary material available at 10.1140/epjds/s13688-021-00308-4.

Project description:BackgroundAs policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems.ObjectiveWe examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared.MethodsWe used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation.ResultsWe found that health care-related themes in Democratic legislators' tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators' discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care-related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23).ConclusionsDivergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis.

Project description:BackgroundThe COVID-19 era has been characterized by the politicization of health-related topics. This is especially concerning given evidence that politicized discussion of vaccination may contribute to vaccine hesitancy. No research, however, has examined the content and politicization of legislator communication with the public about vaccination during the COVID-19 era.ObjectiveThe aim of this study was to examine vaccine-related tweets produced by state and federal legislators during the COVID-19 era to (1) describe the content of vaccine-related tweets; (2) examine the differences in vaccine-related tweet content between Democrats and Republicans; and (3) quantify (and describe trends over time in) partisan differences in vaccine-related communication.MethodsWe abstracted all vaccine-related tweets produced by state and federal legislators between February 01, 2020, and December 11, 2020. We used latent Dirichlet allocation to define the tweet topics and used descriptive statistics to describe differences by party in the use of topics and changes in political polarization over time.ResultsWe included 14,519 tweets generated by 1463 state legislators and 521 federal legislators. Republicans were more likely to use words (eg, "record time," "launched," and "innovation") and topics (eg, Operation Warp Speed success) that were focused on the successful development of a SARS-CoV-2 vaccine. Democrats used a broader range of words (eg, "anti-vaxxers," "flu," and "free") and topics (eg, vaccine prioritization, influenza, and antivaxxers) that were more aligned with public health messaging related to the vaccine. Polarization increased over most of the study period.ConclusionsRepublican and Democratic legislators used different language in their Twitter conversations about vaccination during the COVID-19 era, leading to increased political polarization of vaccine-related tweets. These communication patterns have the potential to contribute to vaccine hesitancy.

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Partisan styles of self-presentation in U.S. Twitter bios.

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Partisan styles of self-presentation in U.S. Twitter bios.

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