TAI: a lightweight network for content-based fake news detection

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.