Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information

Giuseppe Rizzo¹  Simone Leonardi²  Maurizio Morisio² 

[1] LINKS Foundation, Via Pier Carlo Boggio, 61, 10138 Turin, Italy;Politecnico di Torino DAUIN, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy;

关键词: misinformation;    social media;    nlp;    deep learning;    sentence embeddings;    natural language processing;   

DOI  :  10.3390/info12060248

来源: DOAJ

【 摘 要 】

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and $f_1$ score of our model with different configurations and with a baseline classifier. We obtained an $f_1$ score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

【 授权许可】

Unknown