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Author Spotlight: Addressing Regulatory Gaps in Molecular Studies by Quantifying Viral Vectors in Complex Matrices

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Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative

Thirunavukarasu Balasubramaniam¹, Richi Nayak¹, Khanh Luong¹

¹School of Computer Science and Centre for Data Science, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000 Australia.

Social Network Analysis and Mining

|June 21, 2021

Summary

This study introduces a novel two-step technique for detecting misinformation on social media. The method effectively identifies false information in large datasets, improving topic discovery for better online content analysis.

Keywords:

Covid-19 Misinformation detection Nonnegative tensor factorization Ranking Saturating Coordinate Descent Spatio-temporal patterns Topic modelling

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Area of Science:

Computer Science
Information Science

Background:

Social media platforms facilitate information exchange but also enable the rapid spread of misinformation.
Misinformation poses significant risks, especially during critical events like the COVID-19 pandemic.

Purpose of the Study:

To propose and evaluate a novel two-step ranking-based misinformation detection (RMiD) technique.
To enhance the discovery of spatio-temporal topic dynamics within large collections of social media data.

Main Methods:

A ranking-based approach using scalable information retrieval infrastructure to detect misinformation from unlabeled tweets using a small labeled dataset.
Representation of detected misinformation tweets using a coupled matrix tensor model and Nonnegative Coupled Matrix Tensor Factorization for topic analysis.

Main Results:

The RMiD technique demonstrates superior misinformation detection with improved coverage and reduced noise compared to existing methods.
The coupled matrix tensor representation enhanced the quality of discovered topics from unlabeled data by up to 4% through semantic similarity leveraging.

Conclusions:

The proposed RMiD technique offers an effective solution for identifying misinformation on social media platforms.
Leveraging coupled matrix tensor models improves the understanding of spatio-temporal topic dynamics in large-scale text data.