3 citations · 13 across the 10 of their papers we have counts for
6 papers · 1 filter
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Zihao He, Siyi Guo, Ashwin Rao +1
Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increas…
CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities
Zihao He, Jonathan May, Kristina Lerman
Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions. Existing machine learning approaches often struggle to ad…
Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit
Kai Chen, Zihao He, Rong-Ching Chang +2
Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has…
Infusing Knowledge from Wikipedia to Enhance Stance Detection
Zihao He, Negar Mokhberian, Kristina Lerman
Stance detection infers a text author's attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background know…
Speaker Turn Modeling for Dialogue Act Classification
Zihao He, Leili Tavabi, Kristina Lerman +1
Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utteran…
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings
Zihao He, Negar Mokhberian, Antonio Camara +2
Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter th…