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ABSTRACT
Are women congressional candidates on Facebook disproportionately punished for using negative emotions? Scholars suggest the success of negative language used by campaigns is patterned by gender, but it is less clear whether women politicians are able to grow support on a digital platform where negativity spurs engagement. In this research note, we consider the relationship between candidate gender, negative appeals, and user engagement on Facebook. We argue that women candidates are not shying away from negative voter appeals on Facebook, posting more on average with anger, disgust, and sadness than male candidates, and those posts are likely to get increased engagement, but that interaction is conditional. Women candidates, while reinforcing connections by getting more likes and comments than their male counterparts, are not advantaged or disadvantaged in the number of times these types of posts are shared – even when the posts contain more negatively valenced language. Our research suggests that there are some limits to the benefits of using negative emotive language for congressional candidates.
KEYWORDS:
Acknowledgments
Earlier versions of this paper were presented at the 2021 Southern Political Science Association Conference and Women in Legislative Studies Research Seminar in 2021.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplementary data
Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/1554477X.2023.2198051
Notes
2. We use CrowdTangle to access these posts, as described in the data section.
3. Facebook self-describes the Feed here: https://www.facebook.com/help/1155510281178725.
6. These are the candidates on CrowdTangle’s curated congressional candidate lists of incumbents and non-incumbents.
7. It is available online and can be accessed here: http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm.
8. We use scores from the 2020 Cook Report to label each district as competitive or noncompetitive. If the Report labeled a district as likely Democrat/Republican, lean Democrat/Republican, or a toss-up as of November 2, 2020, then we consider the district to be competitive here. Of the 435 districts in 2020, 89 were considered competitive using this benchmark.
9. Table A2 in the Appendix estimates a similar model, with the addition of an interaction between gender and party.
10. Table A7 in the Appendix estimates a similar model, with the addition of an interaction between gender and party.
11. The control variables are not plotted, but provided in Tables A3-A6 in the Appendix.
Additional information
Notes on contributors
Maggie Macdonald
Maggie Macdonald is an assistant professor in the Political Science Department at the University of Kentucky.
Whitney Hua
Whitney Hua is the Director of Applied Data and Research at the Center for Election Science.
Annelise Russell
Annelise Russell is an assistant professor in the Martin School for Public Policy and Administration at the University of kentucky.