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

• URL: http://arxiv.org/abs/2503.10648v1
• Date: Mon, 03 Mar 2025 09:03:14 GMT
• Title: Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
• Authors: Simon Hofmann, Christoph Sommermann, Mathias Kraus, Patrick Zschech, Julian Rosenberger,
• Abstract summary: This study explores the prevalence of hate speech (HS) and sentiment in YouTube video comments concerning the Israel-Palestine conflict.<n>Machine learning (ML) models were applied to the extracted comment sections of YouTube videos from public and private sources.<n> Sentiment analysis revealed a predominantly neutral stance in both source types, with more pronounced sentiments towards Israel and Palestine observed in public sources.
• Score: 3.738325076054202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Mapping Controversies Using Artificial Intelligence: An Analysis of the Hamas-Israel Conflict on YouTube [2.5357699888548724]
  This article analyzes the Hamas-Israel controversy through 253,925 Spanish- YouTube comments posted between October 2023 and January 2024. Adopting an interdisciplinary approach, the study combines the analysis of controversies from Science and Technology Studies with advanced computational methodologies. Results show a predominance of pro-Palestinian comments, although pro-Israeli and anti-Palestinian comments received more "likes"
  arXiv  Detail & Related papers  (2025-04-16T15:27:57Z)
• Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments [0.0]
  This study analyzed 9,969 Reddit comments related to the Israel-Palestine conflict, collected between October 2023 and August 2024.<n>Various approaches, including machine learning, pre-trained language models, neural networks, and prompt engineering strategies were employed to classify these stances.
  arXiv  Detail & Related papers  (2025-02-01T12:26:11Z)
• Israel-Hamas war through Telegram, Reddit and Twitter [9.020777839880571]
  The study will cover an analysis of the related discussion in relation to different participants of the conflict and sentiment represented in those discussion.<n>We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis.<n>Our findings hint at polarized narratives as the hallmark of how political factions and outsiders mold public opinion.
  arXiv  Detail & Related papers  (2025-01-30T08:20:26Z)
• Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict [3.2430260063115224]
  This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict.<n>A lexicon-based, unsupervised methodology was developed to measure "extreme opinions"<n>The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events.
  arXiv  Detail & Related papers  (2024-12-14T17:52:28Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start [65.28355014154549]
  This paper investigates the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora. We show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide.
  arXiv  Detail & Related papers  (2023-04-06T23:36:45Z)
• Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
  We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
  arXiv  Detail & Related papers  (2023-03-28T03:31:37Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Sentiment Analysis of Political Tweets for Israel using Machine Learning [0.0]
  This research proposes an analytical study using Israeli political Twitter data to interpret public opinion towards the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using Machine Learning algorithms.
  arXiv  Detail & Related papers  (2022-04-12T12:07:43Z)
• What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses [58.75684238003408]
  psycho-linguistics analyses of social media text are vital for drawing meaningful conclusions to mitigate misinformation. This research contributes by performing an in-depth psycholinguistic analysis of rumours related to various kinds of events.
  arXiv  Detail & Related papers  (2021-11-09T07:45:11Z)
• News Consumption in Time of Conflict: 2021 Palestinian-Israel War as an Example [4.106987095869419]
  We conduct a detailed analysis of the news consumption of more than eight thousand Twitter users who are either pro-Palestinian or pro-Israeli. We identify the stance of users using unsupervised stance detection. We observe that users may consume more topically-related content from foreign and less popular sources.
  arXiv  Detail & Related papers  (2021-09-27T07:39:04Z)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)

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.