activity
20172026
most citedFriends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

25 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

The Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Prediction

Mirko Lai, Alessandra Urbinati, Simona Frenda +2

Current research primarily focuses on model performance, while comparatively less attention has been devoted to uncertainty estimation, particularly in settings where LLMs are incr…

cs.CL2025

Are you sure? Measuring models bias in content moderation through uncertainty

Alessandra Urbinati, Mirko Lai, Simona Frenda +1

Automatic content moderation is crucial to ensuring safety in social media. Language Model-based classifiers are being increasingly adopted for this task, but it has been shown tha…

cs.CL2022★ 1 cited

O-Dang! The Ontology of Dangerous Speech Messages

Marco A. Stranisci, Simona Frenda, Mirko Lai +5

Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Desp…

cs.CL2020★ 12 cited

#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection

Mirko Lai, Viviana Patti, Giancarlo Ruffo +1

Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isol…

cs.CL2017★ 25 cited

Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

Mirko Lai, Delia Irazú Hernández Farías, Viviana Patti +1

Stance detection, the task of identifying the speaker's opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for d…