A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech

• URL: http://arxiv.org/abs/2407.15227v1
• Date: Sun, 21 Jul 2024 17:27:17 GMT
• Title: A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
• Authors: Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun De Choudhury, Srijan Kumar,
• Abstract summary: Violence-provoking speech contributed to a massive surge in anti-Asian crimes during the pandemic. We develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-sourced dataset. We show that accurate and reliable detection of violence-provoking speech is a challenging task.
• Score: 31.98433210638392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech ($F_1 = 0.89$), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1 = 0.69$). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises. The resources related to the study are available at https://claws-lab.github.io/violence-provoking-speech/.

Related papers

• SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection [3.0460060805145517]
  We propose a novel hate speech detection framework called SWE2, which only relies on the content of messages and automatically identifies hate speech. Experimental results show that our proposed model achieves 0.975 accuracy and 0.953 macro F1, outperforming 7 state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-09-25T07:05:44Z)
• Demarked: A Strategy for Enhanced Abusive Speech Moderation through Counterspeech, Detoxification, and Message Management [71.99446449877038]
  We propose a more comprehensive approach called Demarcation scoring abusive speech based on four aspect -- (i) severity scale; (ii) presence of a target; (iii) context scale; (iv) legal scale. Our work aims to inform future strategies for effectively addressing abusive speech online.
  arXiv  Detail & Related papers  (2024-06-27T21:45:33Z)
• A Group-Specific Approach to NLP for Hate Speech Detection [2.538209532048867]
  We propose a group-specific approach to NLP for online hate speech detection. We analyze historical data about discrimination against a protected group to better predict spikes in hate speech against that group. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech.
  arXiv  Detail & Related papers  (2023-04-21T19:08:49Z)
• Assessing the impact of contextual information in hate speech detection [0.48369513656026514]
  We provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2022-10-02T09:04:47Z)
• Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation [59.41186714127256]
  Dysarthric speech reconstruction (DSR) aims to improve the quality of dysarthric speech. Speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. We propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA)
  arXiv  Detail & Related papers  (2022-02-18T08:59:36Z)
• COLD: A Benchmark for Chinese Offensive Language Detection [54.60909500459201]
  We use COLDataset, a Chinese offensive language dataset with 37k annotated sentences. We also propose textscCOLDetector to study output offensiveness of popular Chinese language models. Our resources and analyses are intended to help detoxify the Chinese online communities and evaluate the safety performance of generative language models.
  arXiv  Detail & Related papers  (2022-01-16T11:47:23Z)
• "Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic [2.5227595609842206]
  COVID-19 pandemic has fueled a surge in anti-Asian xenophobia and prejudice. We create and annotate a corpus of Twitter tweets using 2 experimental approaches to explore anti-Asian abusive and hate speech.
  arXiv  Detail & Related papers  (2021-12-04T06:55:19Z)
• One to rule them all: Towards Joint Indic Language Hate Speech Detection [7.296361860015606]
  We present a multilingual architecture using state-of-the-art transformer language models to jointly learn hate and offensive speech detection. On the provided testing corpora, we achieve Macro F1 scores of 0.7996, 0.7748, 0.8651 for sub-task 1A and 0.6268, 0.5603 during the fine-grained classification of sub-task 1B.
  arXiv  Detail & Related papers  (2021-09-28T13:30:00Z)
• 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)
• Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection [55.445023584632175]
  We build an offensive language detection system, which combines multi-task learning with BERT-based models. Our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place.
  arXiv  Detail & Related papers  (2020-04-28T11:27:24Z)

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.