most citedUnderstanding gender differences in experiences and concerns surrounding online harms: A short report on a nationally representative survey of UK adults

3 citations · 3 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CY2024

Gendered Inequalities in Online Harms: Fear, Safety Work, and Online Participation

Florence E. Enock, Francesca Stevens, Tvesha Sippy +5

Online harms, such as hate speech, trolling and self-harm promotion, continue to be widespread. There are growing concerns that these harms may disproportionately affect women, ref…

cs.CY2024

Exploring responsible applications of Synthetic Data to advance Online Safety Research and Development

Pica Johansson, Jonathan Bright, Shyam Krishna +2

The use of synthetic data provides an opportunity to accelerate online safety research and development efforts while showing potential for bias mitigation, facilitating data storag…

cs.CY20243 cited

Understanding gender differences in experiences and concerns surrounding online harms: A short report on a nationally representative survey of UK adults

Florence E. Enock, Francesca Stevens, Jonathan Bright +4

Online harms, such as hate speech, misinformation, harassment and self-harm promotion, continue to be widespread. While some work suggests that women are disproportionately affecte…

cs.CY2024

Understanding engagement with platform safety technology for reducing exposure to online harms

Jonathan Bright, Florence E. Enock, Pica Johansson +2

User facing 'platform safety technology' encompasses an array of tools offered by platforms to help people protect themselves from harm, for example allowing people to report conte…

cs.CL2024

Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data

Leonardo Castro-Gonzalez, Yi-Ling Chung, Hannak Rose Kirk +4

The field of machine learning has recently made significant progress in reducing the requirements for labelled training data when building new models. These `cheaper' learning tech…

cs.CL2023

DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures

Angus R. Williams, Hannah Rose Kirk, Liam Burke +6

Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but la…