How Hate Speech Varies by Target Identity: A Computational Analysis

• URL: http://arxiv.org/abs/2210.10839v1
• Date: Wed, 19 Oct 2022 19:06:23 GMT
• Title: How Hate Speech Varies by Target Identity: A Computational Analysis
• Authors: Michael Miller Yoder, Lynnette Hui Xian Ng, David West Brown, Kathleen M. Carley
• Abstract summary: We investigate how hate speech varies in systematic ways according to the identities it targets. We find that the targeted demographic category appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group.
• Score: 5.746505534720595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification.

Related papers

• ProvocationProbe: Instigating Hate Speech Dataset from Twitter [0.39052860539161904]
  textitProvocationProbe is a dataset designed to explore what distinguishes instigating hate speech from general hate speech. For this study, we collected around twenty thousand tweets from Twitter, encompassing a total of nine global controversies.
  arXiv  Detail & Related papers  (2024-10-25T16:57:59Z)
• Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups [2.5849042763002426]
  We develop a Named Entity Recognition (NER) System for Identity Groups. Our tool not only detects whether a sentence contains an attack but also tags the sentence tokens corresponding to the mentioned group. We tested the utility of our tool in a case study on social media, annotating and comparing comments from Facebook related to news mentioning identity groups.
  arXiv  Detail & Related papers  (2024-05-13T19:33:18Z)
• Identity Construction in a Misogynist Incels Forum [5.260305201345233]
  We examine how identity groups are discussed on incels-dot-is, the largest black-pilled incels forum. We find that this community produces a wide range of novel identity terms and, while terms for women are most common, mentions of other minoritized identities are increasing. An analysis of the associations made with identity groups suggests an essentialist ideology where physical appearance, as well as gender and racial hierarchies, determine human value.
  arXiv  Detail & Related papers  (2023-06-27T18:56:28Z)
• CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network [52.85130555886915]
  CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
  arXiv  Detail & Related papers  (2023-03-02T17:30:43Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• 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)
• Latent Hatred: A Benchmark for Understanding Implicit Hate Speech [22.420275418616242]
  This work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message. We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech.
  arXiv  Detail & Related papers  (2021-09-11T16:52:56Z)
• Protecting gender and identity with disentangled speech representations [49.00162808063399]
  We show that protecting gender information in speech is more effective than modelling speaker-identity information. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers.
  arXiv  Detail & Related papers  (2021-04-22T13:31:41Z)
• Investigating Deep Learning Approaches for Hate Speech Detection in Social Media [20.974715256618754]
  The misuse of freedom of expression has led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media.
  arXiv  Detail & Related papers  (2020-05-29T17:28:46Z)
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis [51.39895377836919]
  COVID-19 has sparked racism and hate on social media targeted towards Asian communities. We study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
  arXiv  Detail & Related papers  (2020-05-25T21:58:09Z)
• 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)

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.