Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913735783743488 |
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| author | Hofmann, Simon Sommermann, Christoph Kraus, Mathias Zschech, Patrick Rosenberger, Julian |
| author_facet | Hofmann, Simon Sommermann, Christoph Kraus, Mathias Zschech, Patrick Rosenberger, Julian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10648 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict Hofmann, Simon Sommermann, Christoph Kraus, Mathias Zschech, Patrick Rosenberger, Julian Computation and Language Computers and Society Machine Learning Social and Information Networks 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. |
| title | Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict |
| topic | Computation and Language Computers and Society Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2503.10648 |