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Public sentiment toward the COVID-19 vaccine as expressed on social media can interfere with communication by public health agencies on the importance of getting vaccinated. We investigated Twitter data to understand differences in sentiment, moral values, and language use between political ideologies on the COVID-19 vaccine. We estimated political ideology, conducted a sentiment analysis, and guided by the tenets of moral foundations theory (MFT), we analyzed 262,267 English language tweets from the United States containing COVID-19 vaccine-related keywords between May 2020 and October 2021. We applied the Moral Foundations Dictionary and used topic modeling and Word2Vec to understand moral values and the context of words central to the discussion of the vaccine debate. A quadratic trend showed that extreme ideologies of both Liberals and Conservatives expressed a higher negative sentiment than Moderates, with Conservatives expressing more negative sentiment than Liberals. Compared to Conservative tweets, we found the expression of Liberal tweets to be rooted in a wider set of moral values, associated with moral foundations of care (getting the vaccine for protection), fairness (having access to the vaccine), liberty (related to the vaccine mandate), and authority (trusting the vaccine mandate imposed by the government). Conservative tweets were found to be associated with harm (around safety of the vaccine) and oppression (around the government mandate). Furthermore, political ideology was associated with the expression of different meanings for the same words, e.g. “science” and “death.” Our results inform public health outreach communication strategies to best tailor vaccine information to different groups.
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This content will become publicly available on March 20, 2027
From empathy to exclusion: how Immigrant groups are framed in online discourse
Introduction. When examining moral foundations in immigration discourse on social media, most studies focus on ideology-based groups rather than across specific immigrant groups. This ignores moral framings that depict some communities with empathy and others as threats. This research explores how moral foundations vary in Twitter conversations about five immigrant groups. Method. Tweets were sorted into five categories (based on immigrant groups being discussed: African, Asian, European, Latin American, or Middle Eastern) utilising keyword searches and AI LLM modeling. GPT-3.5 Turbo was employed and achieved a satisfactory performance (0.83) compared to manual human labeling. Analysis. Scores for foundation variables (care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/sanctity) were analysed using enhMFD1 dictionary. One-way ANOVA tested overall differences between groups and Tukey’s HSD post-hoc test identified specific patterns. Results. Latin American immigrant discourse emphasised care and authority. European-focused tweets featured stronger loyalty. African immigrant discourse highlighted loyalty with moderate authority, discourse about Middle Eastern immigrants showed elevated harm and betrayal, and Asian immigrant discourse portrayed higher fairness-vice. Conclusion(s). Immigrant groups are framed differently through moral language in social media conversations, which may influence perceptions and can inform strategies for addressing harmful narratives on social media.
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- PAR ID:
- 10708993
- Publisher / Repository:
- Information Research
- Date Published:
- Journal Name:
- Information Research an international electronic journal
- Volume:
- 31
- Issue:
- iConf
- ISSN:
- 1368-1613
- Page Range / eLocation ID:
- 1174 to 1186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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