99 citations · 149 across the 15 of their papers we have counts for
15 papers
Silence Speaks Volumes: Re-weighting Techniques for Under-Represented Users in Fake News Detection
Mansooreh Karami, David Mosallanezhad, Paras Sheth +1
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis model…
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
Paras Sheth, Tharindu Kumarage, Raha Moraffah +2
Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models…
Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study
Ujun Jeong, Paras Sheth, Anique Tahir +3
A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are, how different platforms impact us…
Fairness through Aleatoric Uncertainty
Anique Tahir, Lu Cheng, Huan Liu
We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions…
Towards Detecting Harmful Agendas in News Articles
Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua +3
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection…
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Ujun Jeong, Kaize Ding, Lu Cheng +3
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to…
Distributional Shift Adaptation using Domain-Specific Features
Anique Tahir, Lu Cheng, Ruocheng Guo +1
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming…
Domain Generalization -- A Causal Perspective
Paras Sheth, Raha Moraffah, K. Selçuk Candan +2
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which…
Improving Vaccine Stance Detection by Combining Online and Offline Data
Anique Tahir, Lu Cheng, Paras Sheth +1
Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine…
Toward Understanding Bias Correlations for Mitigation in NLP
Lu Cheng, Suyu Ge, Huan Liu
Natural Language Processing (NLP) models have been found discriminative against groups of different social identities such as gender and race. With the negative consequences of the…
Causal Disentanglement with Network Information for Debiased Recommendations
Paras Sheth, Ruocheng Guo, Lu Cheng +2
Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of inform…
Deep Graph Learning for Anomalous Citation Detection
Jiaying Liu, Feng Xia, Xu Feng +2
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to…
Domain Adaptive Fake News Detection via Reinforcement Learning
Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu +2
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate…
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami +5
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as c…