Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Abstract

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 f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

A systematic literature review of the motivations to share fake news on social media platforms and how to fight them

“…There are many works investigating interventions focused on fake news detection (Leonardi et al, 2021; Metzger et al, 2021; Taskin et al, 2022; Verma et al, 2021) and other ways to minimize the fake news spread on SMP that include banning accounts of fake news spreaders and spreading real information on SMPs (Shu et al, 2017). Another initiative in the discourse of governments is promoting education as a tool to fight fake news content.…”

“…This study is not limited to a specific geographic region or population; therefore, the solutions presented here must be interpreted as general guidelines that must be adapted to the reality of specific regions or populations. There are many works investigating interventions focused on fake news detection (Leonardi et al, 2021;Metzger et al, 2021;Taskin et al, 2022;Verma et al, 2021) and other ways to minimize the fake news spread on SMP that include banning accounts of fake news spreaders and spreading real information on SMPs (Shu et al, 2017). Another initiative in the discourse of governments is promoting education as a tool to fight fake news content.…”

Identifying and characterizing superspreaders of low-credibility content on Twitter

“…These results, though encouraging, rely on model-based simulations and decade-old data. More recent work has proposed methods for identifying fake news spreaders and influential actors within disinformation networks that rely on deep neural networks and other machine learning algorithms [ 32 , 33 ]. These methods, however, are hard to interpret.…”

Identification and characterization of misinformation superspreaders on social media

“…These results, though encouraging, rely on model-based simulations and decade-old data. More recent work has proposed methods for identifying fake news spreaders and influential actors within disinformation networks that rely on deep neural networks and other machine learning algorithms [26,46]. These methods, however, are complex and hard to interpret.…”