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Exposing and Explaining Fake News On-the-Fly

Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo

TL;DR

This proposal is the first to jointly provide data stream processing, profiling, classification and explainability, and the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Abstract

Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Exposing and Explaining Fake News On-the-Fly

TL;DR

This proposal is the first to jointly provide data stream processing, profiling, classification and explainability, and the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Abstract

Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.
Paper Structure (24 sections, 2 figures, 6 tables, 1 algorithm)

This paper contains 24 sections, 2 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Profiling
  4. Classification
  5. Research contribution
  6. Proposed method
  7. Stream-based data processing
  8. Feature engineering
  9. Feature analysis and selection
  10. Stream-based classification
  11. Frequency-based lexicon
  12. Unsupervised and supervised classification
  13. Stream-based explainability module
  14. Experimental results
  15. Experimental data sets
  16. ...and 9 more sections

Figures (2)

  • Figure 1: System diagram composed of: (i) stream-based data processing, (ii) online classification, and (iii) stream-based explainability.
  • Figure 2: Explainability dashboard comprising: (i) selected features from the content, context, and creator, (ii) the prediction, (iii) representative entries of the frequency-based lexicon and the clustering procedure, and (iv) the decision path and its natural language transcription.