# Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict

arXiv: https://arxiv.org/abs/2503.10648 · HTML: https://mlsanity.com/paper/2503.10648/

## Key facts

- Cited by 1 papers 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 (most cited first, 7 of 7)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateCheck: Functional Tests for Hate Speech Detection Models](https://mlsanity.com/paper/2012.15606/) (2020)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines](https://mlsanity.com/paper/2306.14764/) (2023)
- ["A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak](https://mlsanity.com/paper/2107.05104/) (2021)
- [The Virality of Hate Speech on Social Media](https://mlsanity.com/paper/2210.13770/) (2022)
- [A Survey of Text Representation Methods and Their Genealogy](https://mlsanity.com/paper/2211.14591/) (2022)

## Built on by (most cited first, 1 of 1)

- [Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification](https://mlsanity.com/paper/2508.03181/) (2025)

## 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.