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Exploiting Textual Information for Fake News Detection.

Dimitrios Panagiotis Kasseropoulos1, Paraskevas Koukaras1, Christos Tjortjis1

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Summary

This study evaluates machine learning (ML) algorithms for fake news detection using linguistic features and document embeddings. Convolutional Neural Networks (CNNs) achieved the highest accuracy, though Support Vector Machines (SVMs) also performed well.

Keywords:
Artificial Neural Networks (ANN)Association Rules Mining (ARM)Fake newsMachine Learning (ML)Natural Language Processing (NLP)

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Machine Learning

Background:

  • The deliberate dissemination of deceptive news, or "fake news," poses a significant challenge to public trust and information integrity.
  • Existing style-based techniques for fake news detection rely on textual features but can be enhanced for greater accuracy and insight.
  • Machine learning (ML) and deep learning models offer powerful tools for analyzing textual data and identifying patterns indicative of misinformation.

Purpose of the Study:

  • To assess the accuracy of various ML algorithms in detecting fake news using a style-based approach.
  • To propose and evaluate an enhanced linguistic feature set combining Named Entity Recognition (NER) and Frequent Pattern (FP) Growth.
  • To compare the performance of ML algorithms with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks.

Main Methods:

  • Extracted linguistic features, including part-of-speech counts and enhanced features from NER and FP Growth.
  • Constructed document embeddings using pre-trained word embeddings and TF-IDF weighting.
  • Combined document embeddings with linguistic features to create diverse training/test sets.
  • Applied recursive feature elimination to identify optimal linguistic characteristics.
  • Trained and fine-tuned ML algorithms, CNNs, and LSTMs for fake news classification.

Main Results:

  • Convolutional Neural Networks (CNNs) demonstrated superior accuracy when utilizing pre-trained word embeddings.
  • Support Vector Machines (SVMs) achieved comparable accuracy across a broader range of input feature sets.
  • The enhanced linguistic feature set improved the insight into sentence-level structure.
  • Style-based techniques, while yielding lower accuracy, offered explainable insights into authorial writing style.

Conclusions:

  • Combining advanced techniques like NER and FP Growth enhances the effectiveness of style-based fake news detection.
  • Deep learning models, particularly CNNs with appropriate embeddings, show strong potential for accurate fake news identification.
  • Style-based approaches remain valuable for providing interpretable results regarding writing style in misinformation.