SynthesisPloS one2020
Directions in abusive language training data, a systematic review: Garbage in, garbage out.
Synthesis in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Social media users experience more political hostility in less economically equal and less democratic societies.Nature human behaviour · 2026Article
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
- Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach.Scientometrics · 2025Article
- From hate to harmony: Leveraging large language models for safer speech in times of COVID-19 crisis.Heliyon · 2024Article
- Article
- A survey on multi-lingual offensive language detection.PeerJ. Computer science · 2024Article
- The Gates Foundation's new AI initiative: attempting to leapfrog global health inequalities?BMJ global health · 2023Article
- Who are the haters? A corpus-based demographic analysis of authors of hate speech.Frontiers in artificial intelligence · 2023Article
- Label modification and bootstrapping for zero-shot cross-lingual hate speech detection.Language resources and evaluation · 2023Article
- A review on abusive content automatic detection: approaches, challenges and opportunities.PeerJ. Computer science · 2022Article
- Bias and comparison framework for abusive language datasets.AI and ethics · 2022Review
- Artificial Intelligence to Address Cyberbullying, Harassment and Abuse: New Directions in the Midst of Complexity.International journal of bullying prevention : an official publication of the International Bullying Prevention Association · 2022Article
- In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers.Frontiers in artificial intelligence · 2022Article
- Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data.Frontiers in cardiovascular medicine · 2022Review
- Asian hate speech detection on Twitter during COVID-19.Frontiers in artificial intelligence · 2022Article
- Emotionally Informed Hate Speech Detection: A Multi-target Perspective.Cognitive computation · 2022Article
- Addressing religious hate online: from taxonomy creation to automated detection.PeerJ. Computer science · 2022Article
- Data and its (dis)contents: A survey of dataset development and use in machine learning research.Patterns (New York, N.Y.) · 2021Review
- Abusive language detection in youtube comments leveraging replies as conversational context.PeerJ. Computer science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.
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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.