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Found 4 papers, 2 papers with code
Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data.
An Embedding-based Joint Sentiment-Topic Model for Short Texts
Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users' behaviour.
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media.
Multitask Learning for Blackmarket Tweet Detection
Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences.
