Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict

• URL: http://arxiv.org/abs/2412.10913v1
• Date: Sat, 14 Dec 2024 17:52:28 GMT
• Title: Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict
• Authors: Alessio Guerra, Marcello Lepre, Oktay Karakus,
• Abstract summary: This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict. A lexicon-based, unsupervised methodology was developed to measure "extreme opinions" The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events.
• Score: 3.2430260063115224
• License:
• Abstract: This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed to measure "extreme opinions" by considering factors such as anger, polarity, and subjectivity. The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events, such as the IDF's bombings of Al Quds Hospital and the Jabalia Refugee Camp, and the end of a ceasefire following a terrorist attack. Additionally, this study explores the distribution and correlation of these scores across different subreddits and over time, providing insights into the propagation of polarised sentiments in response to conflict events. By examining the quantitative effects of each score on extremism and analysing word cloud similarities through Jaccard indices, the research offers a nuanced understanding of the factors driving extreme online opinions. This approach underscores the potential of social media analytics in capturing the complex interplay between real-world events and online discourse, while also highlighting the limitations and challenges of measuring extremism in social media contexts.

Related papers

• 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. 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. We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis. 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)
• What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse [42.0918839418817]
  We propose a novel topic-agnostic annotation scheme that distinguishes between conspiracies and critical texts. We also contribute with the multilingual XAI-DisInfodemics corpus (English and Spanish), which contains a high-quality annotation of Telegram messages.
  arXiv  Detail & Related papers  (2024-07-15T14:18:47Z)
• The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition [64.5207572897806]
  The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems. In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals. The Cross-Cultural Humor Detection Sub-Challenge (MuSe-Humor) dataset expands upon the Passau Spontaneous Football Coach Humor dataset.
  arXiv  Detail & Related papers  (2024-06-11T22:26:20Z)
• IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict [13.92311040225417]
  We present a meticulously compiled dataset-IsamasRed spanning from August 2023 to November 2023. Our initial analysis on the dataset, examining topics, controversy, emotional and moral language trends over time, highlights the emotionally charged and complex nature of the discourse. This dataset aims to enrich the understanding of online discussions, shedding light on the complex interplay between ideology, sentiment, and community engagement in digital spaces.
  arXiv  Detail & Related papers  (2024-01-16T08:30:41Z)
• 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)
• ExtremeBB: A Database for Large-Scale Research into Online Hate, Harassment, the Manosphere and Extremism [12.647120939857635]
  We introduce ExtremeBB, a textual database of over 53.5M posts made by 38.5k users on 12 extremist bulletin board forums promoting online hate, harassment, the manosphere and other forms of extremism. It enables large-scale analyses of qualitative and quantitative historical trends going back two decades. ExtremeBB comes with a robust ethical data-sharing regime that allows us to share data with academics worldwide.
  arXiv  Detail & Related papers  (2021-11-08T13:15:25Z)
• ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19 [63.72766553648224]
  ExcavatorCovid is a machine reading system that ingests open-source text documents. It extracts COVID19 related events and relations between them, and builds a Temporal and Causal Analysis Graph.
  arXiv  Detail & Related papers  (2021-05-05T01:18:46Z)
• #ISIS vs #ActionCountersTerrorism: A Computational Analysis of Extremist and Counter-extremist Twitter Narratives [2.685668802278155]
  This study will apply computational techniques to analyse the narratives of various pro-extremist and counter-extremist Twitter accounts. Our findings show that pro-extremist accounts often use different strategies to disseminate content when compared to counter-extremist accounts across different types of organisations.
  arXiv  Detail & Related papers  (2020-08-26T20:46:45Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

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.