98 citations · 349 across the 18 of their papers we have counts for
18 papers
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Trilok Padhi, Ramneet Kaur, Krishiv Agarwal +9
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capabi…
Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks
Trilok Padhi, Pinxian Lu, Abdulkadir Erol +5
Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm. Prior jailbreak research has largely focus…
From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data
Ugur Kursuncu, Trilok Padhi, Gaurav Sinha +3
The growing demand for accessible mental health support, compounded by workforce shortages and logistical barriers, has led to increased interest in utilizing Large Language Models…
Large Language Models for Cancer Communication: Evaluating Linguistic Quality, Safety, and Accessibility in Generative AI
Agnik Saha, Victoria Churchill, Anny D. Rodriguez +2
Effective communication about breast and cervical cancers remains a persistent health challenge, with significant gaps in public understanding of cancer prevention, screening, and…
Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models
Abdulkadir Erol, Trilok Padhi, Agnik Saha +2
The rapid advancement of Large Vision-Language Models (LVLMs) has enhanced capabilities offering potential applications from content creation to productivity enhancement. Despite t…
Telegram as a Battlefield: Kremlin-related Communications during the Russia-Ukraine Conflict
Apaar Bawa, Ugur Kursuncu, Dilshod Achilov +3
Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and…
Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications
Ugur Kursuncu, Aaron Baird, Yusen Xia
Predictive machine learning (ML) models are computational innovations that can enhance medical decision-making, including aiding in determining optimal timing for discharging patie…
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
Vedant Khandelwal, Manas Gaur, Ugur Kursuncu +2
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural net…
Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes
Rahul Garg, Trilok Padhi, Hemang Jain +2
Toxicity identification in online multimodal environments remains a challenging task due to the complexity of contextual connections across modalities (e.g., textual and visual). I…
The Adaptive Strategies of Anti-Kremlin Digital Dissent in Telegram during the Russian Invasion of Ukraine
Apaar Bawa, Ugur Kursuncu, Dilshod Achilov +1
During Russia's invasion of Ukraine in February 2022, Telegram became an essential social media platform for Kremlin-sponsored propaganda dissemination. Over time, Anti-Kremlin Rus…
Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning
Trilok Padhi, Ugur Kursuncu, Yaman Kumar +2
The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various mod…
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
Manas Gaur, Vamsi Aribandi, Amanuel Alambo +5
Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many indiv…
Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key
Amit Sheth, Valerie L. Shalin, Ugur Kursuncu
Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in…
ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu +5
The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim pe…
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Ugur Kursuncu, Manas Gaur, Amit Sheth
Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks. Deep learning that primarily…
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
Ugur Kursuncu, Manas Gaur, Carlos Castillo +6
Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction rel…
"What's ur type?" Contextualized Classification of User Types in Marijuana-related Communications using Compositional Multiview Embedding
Ugur Kursuncu, Manas Gaur, Usha Lokala +5
With 93% of pro-marijuana population in US favoring legalization of medical marijuana, high expectations of a greater return for Marijuana stocks, and public actively sharing infor…
Predictive Analysis on Twitter: Techniques and Applications
Ugur Kursuncu, Manas Gaur, Usha Lokala +3
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable info…