5 citations · 7 across the 7 of their papers we have counts for
7 papers
Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media
Liam Hebert, Lucas Kopp, Robin Cohen
Modelling the complex dynamics of online social platforms is critical for addressing challenges such as hate speech and misinformation. While Discussion Transformers, which model c…
GASTON: Graph-Aware Social Transformer for Online Networks
Olha Wloch, Liam Hebert, Robin Cohen +1
Online communities have become essential places for socialization and support, yet they also possess toxicity, echo chambers, and misinformation. Detecting this harmful content is…
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
Liam Hebert, Gaurav Sahu, Yuxuan Guo +3
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional co…
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
Liam Hebert, Lukasz Golab, Robin Cohen
We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-…
Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content
Liam Hebert, Hong Yi Chen, Robin Cohen +1
Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when h…
Robust Candidate Generation for Entity Linking on Short Social Media Texts
Liam Hebert, Raheleh Makki, Shubhanshu Mishra +3
Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings…
FedFormer: Contextual Federation with Attention in Reinforcement Learning
Liam Hebert, Lukasz Golab, Pascal Poupart +1
A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each partici…