87 citations · 377 across the 33 of their papers we have counts for
5 papers · 1 filter
Evaluating how LLM annotations represent diverse views on contentious topics
Megan A. Brown, Shubham Atreja, Libby Hemphill +1
Researchers have proposed the use of generative large language models (LLMs) to label data for research and applied settings. This literature emphasizes the improved performance of…
How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments
Angela Schöpke-Gonzalez, Siqi Wu, Sagar Kumar +2
Computational social science research has made advances in machine learning and natural language processing that support content moderators in detecting harmful content. These adva…
"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media
Lingyao Li, Lizhou Fan, Shubham Atreja +1
Harmful content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to address this issue is to develop detection m…
Leveraging Machine Learning to Detect Data Curation Activities
Sara Lafia, Andrea Thomer, David Bleckley +2
This paper describes a machine learning approach for annotating and analyzing data curation work logs at ICPSR, a large social sciences data archive. The systems we studied track c…
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Ping Liu, Joshua Guberman, Libby Hemphill +1
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health cons…