48 citations · 57 across the 6 of their papers we have counts for
6 papers
Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
LLMs offer valuable capabilities, yet they can be utilized by malicious users to disseminate deceptive information and generate fake news. The growing prevalence of LLMs poses diff…
HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?
Shubhashis Roy Dipta, Sadat Shahriar
This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated te…
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration an…
SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation
Sadat Shahriar, Thamar Solorio
Subjectivity and difference of opinion are key social phenomena, and it is crucial to take these into account in the annotation and detection process of derogatory textual content.…
Deception Detection with Feature-Augmentation by soft Domain Transfer
Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research h…
Improving Phishing Detection Via Psychological Trait Scoring
Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
Phishing emails exhibit some unique psychological traits which are not present in legitimate emails. From empirical analysis and previous research, we find three psychological trai…