4 citations · 51 across the 43 of their papers we have counts for
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Estimating Causal Effects of Text Interventions Leveraging LLMs
Siyi Guo, Myrl G. Marmarelis, Fred Morstatter +1
Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interve…
Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities
Minh Duc Chu, Zihao He, Rebecca Dorn +1
Large language models (LLMs) have shown promise in representing individuals and communities, offering new ways to study complex social dynamics. However, effectively aligning LLMs…
Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias
Rebecca Dorn, Lee Kezar, Fred Morstatter +1
Content moderation on social media platforms shapes the dynamics of online discourse, influencing whose voices are amplified and whose are suppressed. Recent studies have raised co…
COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities
Zihao He, Minh Duc Chu, Rebecca Dorn +2
Social scientists use surveys to probe the opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. Recent advances in large language models (L…
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar +5
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we…
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
Zihao He, Ashwin Rao, Siyi Guo +2
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberal…
How Susceptible are Large Language Models to Ideological Manipulation?
Kai Chen, Zihao He, Jun Yan +2
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal i…
Whose Emotions and Moral Sentiments Do Language Models Reflect?
Zihao He, Siyi Guo, Ashwin Rao +1
Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as c…
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…
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Negar Mokhberian, Myrl G. Marmarelis, Frederic R. Hopp +3
Supervised classification heavily depends on datasets annotated by humans. However, in subjective tasks such as toxicity classification, these annotations often exhibit low agreeme…
Discovering collective narratives shifts in online discussions
Wanying Zhao, Siyi Guo, Kristina Lerman +1
Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the perv…
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…
A Data Fusion Framework for Multi-Domain Morality Learning
Siyi Guo, Negar Mokhberian, Kristina Lerman
Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and moralit…
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…
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah +3
The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferrin…
Noise Audits Improve Moral Foundation Classification
Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh +2
Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed…
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah +3
Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition mo…
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…
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…