Search Results for author:
Found 26 papers, 13 papers with code
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names.
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions?
Temporal Graph Analysis with TGX
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.
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation
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.
Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation
Recent large language models (LLMs) have been shown to be effective for misinformation detection.
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Any prior knowledge can now be embedded into that metric space independently from the employed DA.
Uncertainty Resolution in Misinformation Detection
Misinformation poses a variety of risks, such as undermining public trust and distorting factual discourse.
Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Party Prediction for Twitter
A large number of studies on social media compare the behaviour of users from different political parties.
Open, Closed, or Small Language Models for Text Classification?
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
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.
Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
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.
ToxBuster: In-game Chat Toxicity Buster with BERT
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumption.
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
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.
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
how to capture temporal dependencies, and iii).
Active Keyword Selection to Track Evolving Topics on Twitter
How can we study social interactions on evolving topics at a mass scale?
Towards Better Evaluation for Dynamic Link Prediction
To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications.
Revisiting Hotels-50K and Hotel-ID
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID.
Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization
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.
Graph Attention Networks with Positional Embeddings
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.
The Surprising Performance of Simple Baselines for Misinformation Detection
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.
Laplacian Change Point Detection for Dynamic Graphs
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.
SCG: Spotting Coordinated Groups in Social Media
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.
Anomaly Detection with Joint Representation Learning of Content and Connection
Social media sites are becoming a key factor in politics.
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
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.
