Search Results for author:
Found 18 papers, 7 papers with code
Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020
In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference.
Recalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research.
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models
To help address this issue, we introduce Multilingual HateCheck (MHC), a suite of functional tests for multilingual hate speech detection models.
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks
To address this issue, we propose two contrasting paradigms for data annotation.
An influencer-based approach to understanding radical right viral tweets
We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets.
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate
We show that both text- and visual- enrichment improves model performance, with the multimodal model (0. 771) outperforming other models' F1 scores (0. 544, 0. 737, and 0. 754).
Introducing CAD: the Contextual Abuse Dataset
Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
An Expert Annotated Dataset for the Detection of Online Misogyny
Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women.
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning
Yet, most research in online hate detection to date has focused on hateful content.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
We provide a new dataset of ~40, 000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation.
Detecting East Asian Prejudice on Social Media
The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic.
Directions in Abusive Language Training Data: Garbage In, Garbage Out
Data-driven analysis and detection of abusive online content covers many different tasks, phenomena, contexts, and methodologies.
Islamophobes are not all the same! A study of far right actors on Twitter
Far-right actors are often purveyors of Islamophobic hate speech online, using social media to spread divisive and prejudiced messages which can stir up intergroup tensions and conflict.
Social and Information Networks Computers and Society Physics and Society Applications
Challenges and frontiers in abusive content detection
Online abusive content detection is an inherently difficult task.
Detecting weak and strong Islamophobic hate speech on social media
Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms.
