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An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19
Deepika Varshney1, Dinesh Kumar Vishwakarma1
1Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi 110042, India.
Data & Knowledge Engineering
|November 21, 2022
Summary
Detecting COVID misinformation is crucial. A new multi-platform voting framework using content, linguistic, similarity, and sentiment features achieved 98% accuracy in identifying fake news, improving reliability through diverse data sources.
Area of Science:
- Computational Social Science
- Information Science
- Public Health Informatics
Background:
- Misleading information regarding COVID-19 on social media has caused public panic.
- Previous fake news detection methods often relied on single web platforms, limiting reliability.
- Cross-platform evidence gathering enhances prediction accuracy and confidence in claim validation.
Purpose of the Study:
- To develop a novel multi-web platform voting framework for detecting misleading information.
- To incorporate diverse features including content, linguistic patterns, similarity, and sentiments for robust analysis.
- To create a unique platform for researchers to gather evidence from multiple sources like YouTube and Google.
Main Methods:
- Proposed a multi-web platform voting framework integrating four feature sets: content, linguistic, similarity, and sentiments.
- Developed a unique source platform to collect headlines and features from two web platforms (YouTube, Google) based on specific queries.
- Utilized the collected data to train and validate a model for predicting misleading information.
Main Results:
- The developed model demonstrated high intelligence and effectiveness in predicting misleading information.
- Achieved 98% accuracy in detecting COVID-19 misinformation using the Covid-19 fake news dataset.
- Confirmed that aggregating clues from multiple web platforms leads to more reliable news validation and predictions.
Conclusions:
- The multi-web platform voting framework offers a more reliable approach to detecting fake news compared to single-platform methods.
- The model's high accuracy in identifying COVID-19 misinformation highlights its potential for real-world application.
- This research provides valuable tools for health policymakers and practitioners to combat misinformation during pandemics.
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