activity
20202025
most citedExploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment

Mohammad Mahdi Abootorabi, Alireza Ghahramani Kure, Mohammadali Mohammadkhani +2

This paper presents our system for SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In an era where misinformation spreads rapidly, effective fact-c…

cs.CL2025★ 1 cited

Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis

Mohammad Ali, Naeemul Hassan

This research presents a novel approach to computational framing analysis, called Semantic Relations-based Unsupervised Framing Analysis (SUFA). SUFA leverages semantic relations a…

cs.LG2024★ 2 cited

Graph Neural Networks Need Cluster-Normalize-Activate Modules

Arseny Skryagin, Felix Divo, Mohammad Amin Ali +2

Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep archi…

cs.CL2024★ 3 cited

Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines

Md Main Uddin Rony, Md Mahfuzul Haque, Mohammad Ali +2

In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explor…

stat.AP2020

To be or not to be? A spatial predictive crime model for Rochester

Ankani Chattoraj, Rupam Acharyya, Sabyasachi Shivkumar +2

This project uses a spatial model (Geographically Weighted Regression) to relate various physical and social features to crime rates. Besides making interesting predictions from ba…