ArticleScientific reports2021
Dynamics of online hate and misinformation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Benchmarking explainable offensive language detection in Somali with human-annotated rationales.Scientific reports · 2026Article
- From toxicity to conformity: adaptive user behavior to social norms in Telegram communities.Scientific reports · 2026Article
- Article
- Tracking patterns in toxicity and antisocial behavior over user lifetimes on large social media platforms.Scientific reports · 2025Article
- Will You Become the Next Troll? A Computational Mechanics Approach to the Contagion of Trolling Behavior.Entropy (Basel, Switzerland) · 2025Article
- Dynamics and triggers of misinformation on vaccines.PloS one · 2025Article
- Article
- Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries.Nature communications · 2024Article
- Article
- How U.S. Presidential elections strengthen global hate networks.Npj complexity · 2024Article
- Controlling bad-actor-artificial intelligence activity at scale across online battlefields.PNAS nexus · 2024Article
- The systemic impact of deplatforming on social media.PNAS nexus · 2023Article
- Hatemongers ride on echo chambers to escalate hate speech diffusion.PNAS nexus · 2023Article
- Article
- Natural language processing in the era of large language models.Frontiers in artificial intelligence · 2023Article
- The Role of Victim's Resilience and Self-Esteem in Experiencing Internet Hate.International journal of environmental research and public health · 2022Article
- The supply and demand of news during COVID-19 and assessment of questionable sources production.Nature human behaviour · 2022Article
- Retweet communities reveal the main sources of hate speech.PloS one · 2022Article
- Evolution of topics and hate speech in retweet network communities.Applied network science · 2021Article
- Hate Speech in a Telegram Conspiracy Channel During the First Year of the COVID-19 Pandemic.Social media + societyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "pure haters", meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents' community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.