Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
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Abstract (arXiv)
This study explores the prevalence of hate speech (HS) and sentiment in YouTube video comments concerning the Israel-Palestine conflict by analyzing content from both public and private news sources. The research involved annotating 4983 comments for HS and sentiments (neutral, pro-Israel, and pro-Palestine). Subsequently, machine learning (ML) models were developed, demonstrating robust predictive capabilities with area under the receiver operating characteristic (AUROC) scores ranging from 0.83 to 0.90. These models were applied to the extracted comment sections of YouTube videos from public and private sources, uncovering a higher incidence of HS in public sources (40.4%) compared to private sources (31.6%). Sentiment analysis revealed a predominantly neutral stance in both source types, with more pronounced sentiments towards Israel and Palestine observed in public sources. This investigation highlights the dynamic nature of online discourse surrounding the Israel-Palestine conflict and underscores the potential of moderating content in a politically charged environment.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 3 Mar 2025.
Builds on
7 of 40 references.
References · 7
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
- 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 216
- 2018Characterizing and Detecting Hateful Users on Twittercited by 55
- 2023Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselinescited by 8
- 2021"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreakcited by 6
- 2022The Virality of Hate Speech on Social Mediacited by 6
- 2022A Survey of Text Representation Methods and Their Genealogycited by 5
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Citing papers per year: 2025: 1.
1 paper.