2 citations · 8 across the 13 of their papers we have counts for
13 papers
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…
#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic
Ashwin Rao, Nazanin Sabri, Siyi Guo +2
Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also a…
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…
SoMeR: Multi-View User Representation Learning for Social Media
Siyi Guo, Keith Burghardt, Valeria Pantè +1
Social media user representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations are critical t…
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…
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…
The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape
Siyi Guo, Zihao He, Ashwin Rao +3
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely m…
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…
Measuring Online Emotional Reactions to Events
Siyi Guo, Zihao He, Ashwin Rao +5
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely m…
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Keith Burghardt, Ashwin Rao, Siyi Guo +6
Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are n…
Pandemic Culture Wars: Partisan Differences in the Moral Language of COVID-19 Discussions
Ashwin Rao, Siyi Guo, Sze-Yuh Nina Wang +2
Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demo…
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…
Inherent Trade-offs in the Fair Allocation of Treatments
Yuzi He, Keith Burghardt, Siyi Guo +1
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in hu…