Detection of news written by the ChatGPT through authorship attribution
  performed by a Bidirectional LSTM model
Amanda Ferrari Iaquinta Gustavo Voltani von Atzingen
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.