210 citations · 826 across the 57 of their papers we have counts for
63 papers
Expectation, Backlash, Recovery, and Excitement: How Model Releases Shape Reddit Perceptions of Conversational AI Systems
Vahid Rahimzadeh, Yury Zhauniarovich, Savvas Zannettou
Conversational AI systems (CAISes) continuously change through model releases, feature updates, safety interventions, and access-policy shifts, yet user perceptions are often studi…
Misinformation Span Detection in Videos via Audio Transcripts
Breno Matos, Rennan C. Lima, Savvas Zannettou +2
Online misinformation is one of the most challenging issues lately, yielding severe consequences, including political polarization, attacks on democracy, and public health risks. M…
Understanding LLM Behavior When Encountering User-Supplied Harmful Content in Harmless Tasks
Junjie Chu, Yiting Qu, Ye Leng +4
Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute directly harmful tasks. However, a sub…
The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT
Abhisek Dash, Soumi Das, Elisabeth Kirsten +6
To enable personalized and context-aware interactions, conversational AI systems have introduced a new mechanism: Memory. Memory creates what we refer to as the Algorithmic Self-po…
Bowling with ChatGPT: On the Evolving User Interactions with Conversational AI Systems
Sai Keerthana Karnam, Abhisek Dash, Krishna P. Gummadi +3
Recent studies have discussed how users are increasingly using conversational AI systems, powered by LLMs, for information seeking, decision support, and even emotional support. Ho…
Does Ad-Free Mean Less Data Collection? An Empirical Study of Platform Data Practices and User Expectations
Sepehr Mousavi, Abhisek Dash, Savvas Zannettou +1
Online platforms increasingly offer "paid" ad-free subscriptions as an alternative to the traditional "free" ad-based model. The transition to ad-free models ostensibly removes adv…
Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
Yiting Qu, Ziqing Yang, Yihan Ma +3
Recent advances in text-to-image diffusion models have enabled the creation of a new form of digital art: optical illusions--visual tricks that create different perceptions of real…
The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act
Catalina Goanta, Savvas Zannettou, Rishabh Kaushal +6
To facilitate accountability and transparency, the Digital Services Act (DSA) sets up a process through which Very Large Online Platforms (VLOPs) need to grant vetted researchers a…
Setting the Course, but Forgetting to Steer: Analyzing Compliance with GDPR's Right of Access to Data by Instagram, TikTok, and YouTube
Sai Keerthana Karnam, Abhisek Dash, Antariksh Das +6
The GDPR's Right of Access aims to empower users with control over their personal data via Data Download Packages (DDPs). However, their effectiveness is often compromised by incon…
HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
Xinyue Shen, Yixin Wu, Yiting Qu +3
Large Language Models (LLMs) have raised increasing concerns about their misuse in generating hate speech. Among all the efforts to address this issue, hate speech detectors play a…
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
Boyang Zhang, Yicong Tan, Yun Shen +4
Recently, autonomous agents built on large language models (LLMs) have experienced significant development and are being deployed in real-world applications. These agents can exten…
UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images
Yiting Qu, Xinyue Shen, Yixin Wu +3
With the advent of text-to-image models and concerns about their misuse, developers are increasingly relying on image safety classifiers to moderate their generated unsafe images.…
TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds
Karan Vombatkere, Sepehr Mousavi, Savvas Zannettou +2
Recommendation algorithms for social media feeds often function as black boxes from the perspective of users. We aim to detect whether social media feed recommendations are persona…
A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions
Guilherme H. Resende, Luiz F. Nery, Fabrício Benevenuto +2
Language is a dynamic aspect of our culture that changes when expressed in different technologies/communities. Online social networks have enabled the diffusion and evolution of di…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Auditing the Compliance and Enforcement of Twitter's Advertising Policy
Yash Vekaria, Zubair Shafiq, Savvas Zannettou
Online platforms have enacted various policies to maintain a safe and trustworthy advertising environment. However, the extent to which these policies are adhered to and enforced r…
You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content
Xinlei He, Savvas Zannettou, Yun Shen +1
The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of…
Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
Yiting Qu, Xinyue Shen, Xinlei He +3
State-of-the-art Text-to-Image models like Stable Diffusion and DALLE2 are revolutionizing how people generate visual content. At the same time, society has serious concerns…
Likes and Fragments: Examining Perceptions of Time Spent on TikTok
Angelica Goetzen, Ruizhe Wang, Elissa M. Redmiles +2
Researchers use information about the amount of time people spend on digital media for numerous purposes. While social media platforms commonly do not allow external access to meas…
Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
Savvas Zannettou, Olivia-Nemes Nemeth, Oshrat Ayalon +4
Short-format videos have exploded on platforms like TikTok, Instagram, and YouTube. Despite this, the research community lacks large-scale empirical studies into how people engage…
LAMBRETTA: Learning to Rank for Twitter Soft Moderation
Pujan Paudel, Jeremy Blackburn, Emiliano De Cristofaro +2
To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing mo…
On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning
Yiting Qu, Xinlei He, Shannon Pierson +3
The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evo…
Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit
Alexandros Efstratiou, Jeremy Blackburn, Tristan Caulfield +3
Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenom…
Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots
Wai Man Si, Michael Backes, Jeremy Blackburn +4
Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not b…