collaborators

7 papers

cs.CL2026

Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection

Meysam Sabbaghan, Arman Zareian Jahromi, Doina Caragea

Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large l…

cs.AI2026

LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

Jacob Ativo, Bharaneeshwar Balasubramaniyam, Anh Tran +4

Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the fir…

cs.CV2026

MSGL-Transformer: A Multi-Scale Global-Local Transformer for Rodent Social Behavior Recognition

Muhammad Imran Sharif, Doina Caragea

Recognition of rodent behavior is important for understanding neural and behavioral mechanisms. Traditional manual scoring is time-consuming and prone to human error. We propose MS…

cs.CV2026

Practical Insights into Semi-Supervised Object Detection Approaches

Chaoxin Wang, Bharaneeshwar Balasubramaniyam, Anurag Sangem +2

Learning in data-scarce settings has recently gained significant attention in the research community. Semi-supervised object detection(SSOD) aims to improve detection performance b…

cs.SI2025

The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media

Nikesh Gyawali, Doina Caragea, Cornelia Caragea +1

In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 20…

cs.CL2025

Evaluating Large Language Models for Stance Detection on Financial Targets from SEC Filing Reports and Earnings Call Transcripts

Nikesh Gyawali, Doina Caragea, Alex Vasenkov +1

Financial narratives from U.S. Securities and Exchange Commission (SEC) filing reports and quarterly earnings call transcripts (ECTs) are very important for investors, auditors, an…