Cargando…
Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19
BACKGROUND: Online misogyny is a violation of women’s digital rights. Empirical studies on this topic are however lacking, particularly in low- and middle- income countries. The current study aimed to estimate whether prevalence of online misogyny on Twitter in India changed since the pandemic. METH...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599529/ https://www.ncbi.nlm.nih.gov/pubmed/37878555 http://dx.doi.org/10.1371/journal.pone.0292121 |
_version_ | 1785125783732224000 |
---|---|
author | Dehingia, Nabamallika McAuley, Julian McDougal, Lotus Reed, Elizabeth Silverman, Jay G. Urada, Lianne Raj, Anita |
author_facet | Dehingia, Nabamallika McAuley, Julian McDougal, Lotus Reed, Elizabeth Silverman, Jay G. Urada, Lianne Raj, Anita |
author_sort | Dehingia, Nabamallika |
collection | PubMed |
description | BACKGROUND: Online misogyny is a violation of women’s digital rights. Empirical studies on this topic are however lacking, particularly in low- and middle- income countries. The current study aimed to estimate whether prevalence of online misogyny on Twitter in India changed since the pandemic. METHODS: Based on prior theoretical work, we defined online misogyny as consisting of six overlapping forms: sexist abuses, sexual objectification, threatening to physically or sexually harm women, asserting women’s inferiority, justifying violence against women, and dismissing feminist efforts. Qualitative analysis of a small subset of tweets posted from India (40,672 tweets) substantiated this definition and taxonomy for online misogyny. Supervised machine learning models were used to predict the status of misogyny across a corpus of 30 million tweets posted from India between 2018 and 2021. Next, interrupted time series analysis examined changes in online misogyny prevalence, before and during COVID-19. RESULTS: Qualitative assessment showed that online misogyny in India existed most in the form of sexual objectification and sexist abusive content, which demeans women and shames them for their presumed sexual activity. Around 2% of overall tweets posted from India between 2018 and 2021 included some form of misogynistic content. The absolute volume as well as proportion of misogynistic tweets showed significant increasing trends after the onset of COVID-19, relative to trends prior to the pandemic. CONCLUSION: Findings highlight increasing gender inequalities on Twitter since the pandemic. Aggressive and hateful tweets that target women attempt to reinforce traditional gender norms, especially those relating to idealized sexual behavior and framing of women as sexual beings. There is an urgent need for future research and development of interventions to make digital spaces gender equitable and welcoming to women. |
format | Online Article Text |
id | pubmed-10599529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105995292023-10-26 Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 Dehingia, Nabamallika McAuley, Julian McDougal, Lotus Reed, Elizabeth Silverman, Jay G. Urada, Lianne Raj, Anita PLoS One Research Article BACKGROUND: Online misogyny is a violation of women’s digital rights. Empirical studies on this topic are however lacking, particularly in low- and middle- income countries. The current study aimed to estimate whether prevalence of online misogyny on Twitter in India changed since the pandemic. METHODS: Based on prior theoretical work, we defined online misogyny as consisting of six overlapping forms: sexist abuses, sexual objectification, threatening to physically or sexually harm women, asserting women’s inferiority, justifying violence against women, and dismissing feminist efforts. Qualitative analysis of a small subset of tweets posted from India (40,672 tweets) substantiated this definition and taxonomy for online misogyny. Supervised machine learning models were used to predict the status of misogyny across a corpus of 30 million tweets posted from India between 2018 and 2021. Next, interrupted time series analysis examined changes in online misogyny prevalence, before and during COVID-19. RESULTS: Qualitative assessment showed that online misogyny in India existed most in the form of sexual objectification and sexist abusive content, which demeans women and shames them for their presumed sexual activity. Around 2% of overall tweets posted from India between 2018 and 2021 included some form of misogynistic content. The absolute volume as well as proportion of misogynistic tweets showed significant increasing trends after the onset of COVID-19, relative to trends prior to the pandemic. CONCLUSION: Findings highlight increasing gender inequalities on Twitter since the pandemic. Aggressive and hateful tweets that target women attempt to reinforce traditional gender norms, especially those relating to idealized sexual behavior and framing of women as sexual beings. There is an urgent need for future research and development of interventions to make digital spaces gender equitable and welcoming to women. Public Library of Science 2023-10-25 /pmc/articles/PMC10599529/ /pubmed/37878555 http://dx.doi.org/10.1371/journal.pone.0292121 Text en © 2023 Dehingia et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dehingia, Nabamallika McAuley, Julian McDougal, Lotus Reed, Elizabeth Silverman, Jay G. Urada, Lianne Raj, Anita Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title | Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title_full | Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title_fullStr | Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title_full_unstemmed | Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title_short | Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19 |
title_sort | violence against women on twitter in india: testing a taxonomy for online misogyny and measuring its prevalence during covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10599529/ https://www.ncbi.nlm.nih.gov/pubmed/37878555 http://dx.doi.org/10.1371/journal.pone.0292121 |
work_keys_str_mv | AT dehingianabamallika violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT mcauleyjulian violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT mcdougallotus violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT reedelizabeth violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT silvermanjayg violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT uradalianne violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 AT rajanita violenceagainstwomenontwitterinindiatestingataxonomyforonlinemisogynyandmeasuringitsprevalenceduringcovid19 |