5 citations · 16 across the 9 of their papers we have counts for
10 papers
xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers
Calder Johnson, Robin Cohen
Recommendation systems, systems that predict a user's preference for items in order to curate that user's feed, are a ubiquitous and central feature in online social networks. Whil…
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…
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…
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
Alexandre Parmentier, Robin Cohen, Xueguang Ma +2
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modelin…
Towards A Multi-agent System for Online Hate Speech Detection
Gaurav Sahu, Robin Cohen, Olga Vechtomova
This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework em…