Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model
This addresses the issue of AI-generated news facilitating misinformation for the general public, but it is incremental as it applies an existing method to a new data type.
The research tackled the problem of detecting news articles written by ChatGPT to combat fake news and misinformation, achieving 91.57% accuracy using a Bidirectional LSTM model.
The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set.