99 citations · 245 across the 14 of their papers we have counts for
14 papers
RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools
Zeyad Alghamdi, Tharindu Kumarage, Garima Agrawal +3
Effective mental health support is crucial for alleviating psychological distress. While large language model (LLM)-based assistants have shown promise in mental health interventio…
Large Language Models for Data Annotation and Synthesis: A Survey
Zhen Tan, Dawei Li, Song Wang +7
Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learni…
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…
Estimating Topic Exposure for Under-Represented Users on Social Media
Mansooreh Karami, Ahmadreza Mosallanezhad, Paras Sheth +1
Online Social Networks (OSNs) facilitate access to a variety of data allowing researchers to analyze users' behavior and develop user behavioral analysis models. These models rely…
Text Transformations in Contrastive Self-Supervised Learning: A Review
Amrita Bhattacharjee, Mansooreh Karami, Huan Liu
Contrastive self-supervised learning has become a prominent technique in representation learning. The main step in these methods is to contrast semantically similar and dissimilar…
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…
A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
Faisal Alatawi, Lu Cheng, Anique Tahir +4
Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote c…
Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors
Mansooreh Karami, Tahora H. Nazer, Huan Liu
The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of m…
Mechanisms and Attributes of Echo Chambers in Social Media
Bohan Jiang, Mansooreh Karami, Lu Cheng +2
Echo chambers may exclude social media users from being exposed to other opinions, therefore, can cause rampant negative effects. Among abundant evidence are the 2016 and 2020 US p…
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…
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?
Mansooreh Karami, Ahmadreza Mosallanezhad, Michelle V Mancenido +1
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.…
Causal Adversarial Network for Learning Conditional and Interventional Distributions
Raha Moraffah, Bahman Moraffah, Mansooreh Karami +2
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which re…
Combating Disinformation in a Social Media Age
Kai Shu, Amrita Bhattacharjee, Faisal Alatawi +4
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and…
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo +2
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made b…