Automatic detection of fake news in Tweets during election periods
DOI:
https://doi.org/10.5380/atoz.v14.95677Keywords:
Fake news, Tweets, Elections, Fake news detection, Artificial intelligence, Convolutional neural networksAbstract
Introduction: The phenomenon of disinformation consists of the propagation of untrue and falsified information to obtain financial gains, manipulate public opinion, weaken a political field, alter power relations, strengthen hate groups, or feed prejudices through representations deliberately distorted. With the advent of social media, online news consumption has increased, and changes in information behavior have led to greater access to and sharing of fake news. Such news is often designed to look like real news and is created and disseminated very quickly. Method: In this work, we propose a methodology for detecting fake news using deep neural networks, with a sample of more than 2 million tweets from our own dataset, collected with the Twitter API during the Brazilian presidential elections in 2022. Tweets were automatically labeled by a weak supervision model. Results: The results obtained with the model were an F1-score of 98% in tweets with non-fake news and an F1-score of 47% in tweets containing fake news. The area under the ROC curve was 0.848, considered a value that shows potential. Conclusion: The results of artificial neural network models are promising to speed up the much-needed work of verifying the veracity of news, especially in elections, which require rapid detection of fake news. However, it remains a challenge to work with large volumes of unlabeled data, such as those used in this research. The main fake news topics found were related to moral and religious values, economic agendas, the suitability of institutions, endorsement of public figures, association of opponents, and parties with crime and scientific denialism.
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