172 citations · 644 across the 18 of their papers we have counts for
21 papers
Beyond the Sycophancy Score: How Task, Model, and Pressure Shape LLM Yielding
Guang Yang, Homa Hosseinmardi, Fengchen Liu +1
Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back. This behavior, called sycophancy, is usually reported as a sin…
How China-Origin Vision-Language Models Move from Refusal to Reframing in State Alignment
Guang Yang, Fengchen Liu, Alex Wang +2
State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been sy…
How You Ask Shapes What You Get: Auditing Breast-Cancer Misinformation in TikTok Search
Pooriya Jamie, Homa Hosseinmardi, Rezvaneh Rezapour +3
Millions of people use TikTok to seek health information, yet little is known about how users' search queries shape exposure to health misinformation. Whereas prior algorithm audit…
Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit
Aria Pessianzadeh, Pooriya Jamie, Naima Sultana +6
Cancer-related discussions on social media provide important spaces for information exchange and peer support, but can also expose users to misinformation with implications for pre…
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Amir Ghasemian, Homa Hosseinmardi, Upasana Dutta +1
Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior. By controlling what we see, they shape what we perceive, raisin…
Asking Back: Interaction-Layer Antidistillation Watermarks
Guang Yang, Amir Ghasemian, Fengchen Liu +3
Detecting unauthorized knowledge distillation from a deployed LLM API is hard because the defender controls neither the attacker's training pipeline nor the next-token logits. Exis…
Asymmetric Phase Coding Audio Watermarking
Guang Yang, Amir Ghasemian, Ninareh Mehrabi +1
The proliferation of deepfake audio challenges voice-based authentication systems; passive forensic detectors are sensitive to evolving generative models and to real-world channel…
Seeking Help, Facing Harm: Auditing TikTok's Mental Health Recommendations
Pooriya Jamie, Amir Ghasemian, Homa Hosseinmardi
Recommender systems on social media increasingly mediate how users encounter mental health content, yet it remains unclear whether they distinguish help-seeking from distress expre…
The diminishing state of shared reality on US television news
Homa Hosseinmardi, Samuel Wolken, David M. Rothschild +1
The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a ``shared reality:'' a public sphere in which participants are exposed…
Causally estimating the effect of YouTube's recommender system using counterfactual bots
Homa Hosseinmardi, Amir Ghasemian, Miguel Rivera-Lanas +3
In recent years, critics of online platforms have raised concerns about the ability of recommendation algorithms to amplify problematic content, with potentially radicalizing conse…
Examining the consumption of radical content on YouTube
Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset +3
Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, Y…
Learning Behavioral Representations from Wearable Sensors
Nazgol Tavabi, Homa Hosseinmardi, Jennifer L. Villatte +4
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual's beha…
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan +2
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and impro…
Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization
Homa Hosseinmardi, Hsien-Te Kao, Kristina Lerman +1
In recent years, the rapid growth in technology has increased the opportunity for longitudinal human behavioral studies. Rich multimodal data, from wearables like Fitbit, online so…
Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Homa Hosseinmardi, Amir Ghasemian, Shrikanth Narayanan +2
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an indiv…
Capturing Edge Attributes via Network Embedding
Palash Goyal, Homa Hosseinmardi, Emilio Ferrara +1
Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction and node cla…
Evaluating Overfit and Underfit in Models of Network Community Structure
Amir Ghasemian, Homa Hosseinmardi, Aaron Clauset
A common data mining task on networks is community detection, which seeks an unsupervised decomposition of a network into structural groups based on statistical regularities in the…
Investigating Factors Influencing the Latency of Cyberbullying Detection
Rahat Ibn Rafiq, Homa Hosseinmardi, Richard Han +2
Cyberbullying in online social networks has become a critical problem, especially among teenagers who are social networks' prolific users. As a result, researchers have focused on…
Detection of Cyberbullying Incidents on the Instagram Social Network
Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq +3
Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and auto…
A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying
Homa Hosseinmardi, Rahat Ibn Rafiq, Shaosong Li +4
This paper examines users who are common to two popular online social networks, Instagram and Ask.fm, that are often used for cyberbullying. An analysis of the negativity and posit…
Towards Understanding Cyberbullying Behavior in a Semi-Anonymous Social Network
Homa Hosseinmardi, Amir Ghasemianlangroodi, Richard Han +2
Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audi…