259 citations · 699 across the 30 of their papers we have counts for
35 papers
Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection
Razieh Chalehchaleh, Reza Farahbakhsh, Noel Crespi
Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored. This study presents t…
RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval
Emre Kuru, Mehmet Onur Keskin, Reza Farahbakhsh +1
Document retrieval increasingly supports high-stakes information access in finance, healthcare, and law. Modern retrieval pipelines vary both in modality (text or multimodal) and i…
PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models
Amir Reza Jafari, Praboda Rajapaksha, Reza Farahbakhsh +1
Despite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly fro…
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li +8
Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated dataset…
Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari +8
Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising si…
Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
Hrishikesh Dutta, Roberto Minerva, Reza Farahbakhsh +1
Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Traditional approaches, such as com…
Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages
Aditya Narayan Sankaran, Reza Farahbakhsh, Noel Crespi
Abusive and hateful speech is increasingly spoken rather than written, surfacing in voice notes, calls, and short-form videos. Most detection systems still transcribe speech to tex…
Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Charts
Aditya Narayan Sankaran, Reza Farahbakhsh, Noel Crespi
Code switching, particularly between Korean and English, has become a defining feature of modern K-pop, reflecting both aesthetic choices and global market strategies. This paper i…
Toxicity Is a Property of Communicative Events: A Contextual Stress Measurement Framework for NLP
Sergei Berezin, Reza Farahbakhsh, Noel Crespi
Toxicity detection has become core safety infrastructure for online moderation, dataset filtering, and deployed language-model systems. Yet most detectors still treat toxicity as a…
The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs
Sergey Berezin, Reza Farahbakhsh, Noel Crespi
We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddl…
Evading Toxicity Detection with ASCII-art: A Benchmark of Spatial Attacks on Moderation Systems
Sergey Berezin, Reza Farahbakhsh, Noel Crespi
We introduce a novel class of adversarial attacks on toxicity detection models that exploit language models' failure to interpret spatially structured text in the form of ASCII art…
Adversarial Botometer: Adversarial Analysis for Social Bot Detection
Shaghayegh Najari, Davood Rafiee, Mostafa Salehi +1
Social bots play a significant role in many online social networks (OSN) as they imitate human behavior. This fact raises difficult questions about their capabilities and potential…
No offence, Bert -- I insult only humans! Multiple addressees sentence-level attack on toxicity detection neural network
Sergey Berezin, Reza Farahbakhsh, Noel Crespi
We introduce a simple yet efficient sentence-level attack on black-box toxicity detector models. By adding several positive words or sentences to the end of a hateful message, we a…
On the definition of toxicity in NLP
Sergey Berezin, Reza Farahbakhsh, Noel Crespi
The fundamental problem in toxicity detection task lies in the fact that the toxicity is ill-defined. This causes us to rely on subjective and vague data in models' training, which…
Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder
Khouloud Mnassri, Praboda Rajapaksha, Reza Farahbakhsh +1
The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusiv…
BERT-based Ensemble Approaches for Hate Speech Detection
Khouloud Mnassri, Praboda Rajapaksha, Reza Farahbakhsh +1
With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and…
Transfer Learning for Multi-lingual Tasks -- a Survey
Amir Reza Jafari, Behnam Heidary, Reza Farahbakhsh +2
These days different platforms such as social media provide their clients from different backgrounds and languages the possibility to connect and exchange information. It is not su…
Characterising and Detecting Sponsored Influencer Posts on Instagram
Koosha Zarei, Damilola Ibosiola, Reza Farahbakhsh +4
Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting prod…
Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content
Koosha Zarei, Reza Farahbakhsh, Noel Crespi +1
Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake acco…
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model
Marzieh Mozafari, Reza Farahbakhsh, Noel Crespi
Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of bi…
A First Instagram Dataset on COVID-19
Koosha Zarei, Reza Farahbakhsh, Noel Crespi +1
The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown pol…
How Impersonators Exploit Instagram to Generate Fake Engagement?
Koosha Zarei, Reza Farahbakhsh, Noel Crespi
Impersonators on Online Social Networks such as Instagram are playing an important role in the propagation of the content. These entities are the type of nefarious fake accounts th…
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Marzieh Mozafari, Reza Farahbakhsh, Noel Crespi
Generated hateful and toxic content by a portion of users in social media is a rising phenomenon that motivated researchers to dedicate substantial efforts to the challenging direc…
Uncovering Flaming Events on News Media in Social Media
Praboda Rajapaksha, Reza Farahbakhsh, Noel Crespi +1
Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of re…