Gender Differences in Abuse: The Case of Dutch Politicians on Twitter

• URL: http://arxiv.org/abs/2306.10769v1
• Date: Mon, 19 Jun 2023 08:23:24 GMT
• Title: Gender Differences in Abuse: The Case of Dutch Politicians on Twitter
• Authors: Isabelle van der Vegt
• Abstract summary: This paper analyses gender differences in abuse received by Dutch politicians on Twitter. All tweets directed at party leaders throughout the entire year of 2022 were collected. Female ethnic minority politicians received the highest levels of threats compared to all groups.
• Score: 0.10152838128195464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online abuse and threats towards politicians have become a significant concern in the Netherlands, like in many other countries across the world. This paper analyses gender differences in abuse received by Dutch politicians on Twitter, while taking into account the possible additional impact of ethnic minority status. All tweets directed at party leaders throughout the entire year of 2022 were collected. The effect of gender and ethnic minority status were estimated for six different linguistic measures of abuse, namely, toxicity, severe toxicity, identity attacks, profanity, insults, and threats. Contrary to expectations, male politicians received higher levels of all forms of abuse, with the exception of threats, for which no significant gender difference was found. Significant interaction effects between gender and ethnic minority status were found for a number of abuse measures. In the case of severe toxicity, identity attacks, and profanity, female ethnic minority politicians were more severely impacted than their ethnic majority female colleagues, but not worse than male politicians. Finally, female ethnic minority politicians received the highest levels of threats compared to all groups. Given that online abuse and threats are reported to have a negative effect on political participation and retention, these results are particularly worrying.

Related papers

• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Understanding gender differences in experiences and concerns surrounding online harms: A short report on a nationally representative survey of UK adults [0.8567685792108676]
  We present preliminary results from a large, nationally representative survey of UK adults. We ask about exposure to 15 specific harms, along with fears surrounding exposure and comfort engaging in certain online behaviours. We find that women are significantly more fearful of experiencing every type of harm that we asked about, and are significantly less comfortable partaking in several online behaviours.
  arXiv  Detail & Related papers  (2024-02-01T10:10:52Z)
• The Uli Dataset: An Exercise in Experience Led Annotation of oGBV [3.1060730586569427]
  We present a dataset on gendered abuse in three languages- Hindi, Tamil and Indian English. The dataset comprises of tweets annotated along three questions pertaining to the experience of gender abuse, by experts who identify as women or a member of the LGBTQIA community in South Asia.
  arXiv  Detail & Related papers  (2023-11-15T16:30:44Z)
• Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
  We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
  arXiv  Detail & Related papers  (2023-03-28T03:31:37Z)
• Gender stereotypes in the mediated personalization of politics: Empirical evidence from a lexical, syntactic and sentiment analysis [2.7071541526963805]
  We show that the political personalization in Italy is more detrimental for women than men. Women politicians are covered with a more negative tone than their men counterpart when personal details are reported. The major contribution to the observed gender differences comes from online news rather than print news.
  arXiv  Detail & Related papers  (2022-02-07T11:40:44Z)
• Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection [75.54119209776894]
  We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations. We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity. Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
  arXiv  Detail & Related papers  (2021-11-15T18:58:20Z)
• #ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics [0.15749416770494704]
  ParityBOT was deployed across elections in Canada, the United States and New Zealand. It was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones. We examine the rate of false negatives, where ParityBOT failed to pick up on insults directed at specific high profile women.
  arXiv  Detail & Related papers  (2021-09-30T22:55:49Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)
• Young Adult Unemployment Through the Lens of Social Media: Italy as a case study [108.33144653708091]
  We employ survey data together with social media data to analyse personality, moral values, but also cultural elements of the young unemployed population in Italy. Our findings show that there are small but significant differences in personality and moral values, with the unemployed males to be less agreeable. Unemployed have a more collectivist point of view, valuing more in-group loyalty, authority, and purity foundations.
  arXiv  Detail & Related papers  (2020-10-09T10:56:04Z)
• A Framework for the Computational Linguistic Analysis of Dehumanization [52.735780962665814]
  We analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. We find increasingly humanizing descriptions of LGBTQ people over time. The ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
  arXiv  Detail & Related papers  (2020-03-06T03:02:12Z)
• Online Abuse toward Candidates during the UK General Election 2019: Working Paper [0.9741305928417096]
  We collected 4.2 million tweets sent to or from election candidates in the six week period spanning from the start of November until shortly after the December 12th election. We found abuse in 4.46% of replies received by candidates, up from 3.27% in the matching period for the 2017 UK general election. On average, men received more general and political abuse; women received more sexist abuse.
  arXiv  Detail & Related papers  (2020-01-23T17:28:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.