no code implementations • EMNLP (WNUT) 2020 • Kellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, Reihaneh Rabbany
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions?
no code implementations • Findings (ACL) 2022 • Yifei Li, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany
In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names.
2 code implementations • 6 Feb 2024 • Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar, Reihaneh Rabbany, Farimah Poursafaei
Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs.
no code implementations • 13 Jan 2024 • Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany, Kellin Pelrine
We propose an uncertainty quantification framework that leverages both direct confidence elicitation and sampled-based consistency methods to provide better calibration for NLP misinformation mitigation solutions.
no code implementations • 12 Jan 2024 • Tyler Vergho, Jean-Francois Godbout, Reihaneh Rabbany, Kellin Pelrine
Recent large language models (LLMs) have been shown to be effective for misinformation detection.
1 code implementation • 3 Jan 2024 • Aarash Feizi, Randall Balestriero, Adriana Romero-Soriano, Reihaneh Rabbany
Any prior knowledge can now be embedded into that metric space independently from the employed DA.
no code implementations • 2 Jan 2024 • Yury Orlovskiy, Camille Thibault, Anne Imouza, Jean-François Godbout, Reihaneh Rabbany, Kellin Pelrine
Misinformation poses a variety of risks, such as undermining public trust and distorting factual discourse.
no code implementations • 20 Oct 2023 • Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany
Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms.
1 code implementation • 6 Oct 2023 • Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
1 code implementation • 25 Aug 2023 • Kellin Pelrine, Anne Imouza, Zachary Yang, Jacob-Junqi Tian, Sacha Lévy, Gabrielle Desrosiers-Brisebois, Aarash Feizi, Cécile Amadoro, André Blais, Jean-François Godbout, Reihaneh Rabbany
A large number of studies on social media compare the behaviour of users from different political parties.
no code implementations • 19 Aug 2023 • Hao Yu, Zachary Yang, Kellin Pelrine, Jean Francois Godbout, Reihaneh Rabbany
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks.
2 code implementations • NeurIPS 2023 • Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
1 code implementation • 24 May 2023 • Kellin Pelrine, Anne Imouza, Camille Thibault, Meilina Reksoprodjo, Caleb Gupta, Joel Christoph, Jean-François Godbout, Reihaneh Rabbany
We propose focusing on generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible.
no code implementations • 21 May 2023 • Zachary Yang, Yasmine Maricar, MohammadReza Davari, Nicolas Grenon-Godbout, Reihaneh Rabbany
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumption.
2 code implementations • 15 May 2023 • Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau, Reihaneh Rabbany
Current solutions do not scale well to large real-world graphs, lack robustness to large amounts of node additions/deletions, and overlook changes in node attributes.
2 code implementations • 2 Feb 2023 • Shenyang Huang, Samy Coulombe, Yasmeen Hitti, Reihaneh Rabbany, Guillaume Rabusseau
how to capture temporal dependencies, and iii).
1 code implementation • 22 Sep 2022 • Sacha Lévy, Farimah Poursafaei, Kellin Pelrine, Reihaneh Rabbany
How can we study social interactions on evolving topics at a mass scale?
1 code implementation • 20 Jul 2022 • Farimah Poursafaei, Shenyang Huang, Kellin Pelrine, Reihaneh Rabbany
To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications.
1 code implementation • 20 Jul 2022 • Aarash Feizi, Arantxa Casanova, Adriana Romero-Soriano, Reihaneh Rabbany
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID.
no code implementations • NeurIPS Workshop LatinX_in_AI 2021 • Albert Manuel Orozco Camacho, Reihaneh Rabbany
In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important events happening around the world.
no code implementations • 9 May 2021 • Liheng Ma, Reihaneh Rabbany, Adriana Romero-Soriano
In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node.
2 code implementations • 14 Apr 2021 • Kellin Pelrine, Jacob Danovitch, Reihaneh Rabbany
As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unverified rumours.
1 code implementation • 2 Jul 2020 • Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau, Reihaneh Rabbany
To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings.
no code implementations • 16 Oct 2019 • Junhao Wang, Sacha Levy, Ren Wang, Aayushi Kulshrestha, Reihaneh Rabbany
Recent events have led to a burgeoning awareness on the misuse of social media sites to affect political events, sway public opinion, and confuse the voters.
no code implementations • 16 Jun 2019 • Junhao Wang, Renhao Wang, Aayushi Kulshrestha, Reihaneh Rabbany
Social media sites are becoming a key factor in politics.
no code implementations • NeurIPS 2014 • Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
The cutting plane method is an augmentative constrained optimization procedure that is often used with continuous-domain optimization techniques such as linear and convex programs.