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Weaving M, Alshaabi T, Arnold MV, Blake K, Danforth CM, Dodds PS, Haslam N, Fine C. Twitter misogyny associated with Hillary Clinton increased throughout the 2016 U.S. election campaign. Sci Rep 2023; 13:5266. [PMID: 37002316 PMCID: PMC10066361 DOI: 10.1038/s41598-023-31620-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/14/2023] [Indexed: 04/03/2023] Open
Abstract
Online misogyny has become a fixture in female politicians' lives. Backlash theory suggests that it may represent a threat response prompted by female politicians' counterstereotypical, power-seeking behaviors. We investigated this hypothesis by analyzing Twitter references to Hillary Clinton before, during, and after her presidential campaign. We collected a corpus of over 9 million tweets from 2014 to 2018 that referred to Hillary Clinton, and employed an interrupted time series analysis on the relative frequency of misogynistic language within the corpus. Prior to 2015, the level of misogyny associated with Clinton decreased over time, but this trend reversed when she announced her presidential campaign. During the campaign, misogyny steadily increased and only plateaued after the election, when the threat of her electoral success had subsided. These findings are consistent with the notion that online misogyny towards female political nominees is a form of backlash prompted by their ambition for power in the political arena.
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Affiliation(s)
- Morgan Weaving
- School of Historical and Philosophical Studies, The University of Melbourne, Parkville, VIC, Australia.
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | | | - Michael V Arnold
- Computational Story Lab, Vermont Complex Systems Center, MassMutual Center of Excellence for Complex Systems and Data Science, University of Vermont, Burlington, VT, USA
| | - Khandis Blake
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Christopher M Danforth
- Computational Story Lab, Vermont Complex Systems Center, MassMutual Center of Excellence for Complex Systems and Data Science, University of Vermont, Burlington, VT, USA
| | - Peter S Dodds
- Computational Story Lab, Vermont Complex Systems Center, MassMutual Center of Excellence for Complex Systems and Data Science, University of Vermont, Burlington, VT, USA
| | - Nick Haslam
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Cordelia Fine
- School of Historical and Philosophical Studies, The University of Melbourne, Parkville, VIC, Australia
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Kennedy JA, Kray LJ. Gender similarities and differences in dishonesty. Curr Opin Psychol 2022; 48:101461. [PMID: 36116425 DOI: 10.1016/j.copsyc.2022.101461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 01/28/2023]
Abstract
We review the evidence linking gender to dishonesty and conclude that men are often more dishonest than women, especially in competitive settings where lies advance self-interest. However, gender differences in dishonesty are often small and mutable across situations. We propose that attending to self-regulatory constructs such as moral identity might help researchers move beyond the evolutionary-cultural debates over the origin of gender differences toward identifying factors that promote honesty from both genders.
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Affiliation(s)
- Jessica A Kennedy
- Vanderbilt University, Owen Graduate School of Management, Nashville, TN 37203, USA.
| | - Laura J Kray
- University of California, Haas School of Business, Berkeley, CA 94720-1900, USA
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