Mitigating Misinformation: A Data-Driven Approach to Fake News Detection and Dissemination on Social Network X

Diego Gamboa () and Graciela Guerrero ()
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Diego Gamboa: Universidad de las Fuerzas Armadas ESPE, Departamento de Ciencias de la Computación
Graciela Guerrero: Universidad de las Fuerzas Armadas ESPE, Departamento de Ciencias de la Computación

Chapter 1 in Management, Tourism, and Smart Technologies, Vol 2, 2026, pp 3-14 from Springer

Abstract: Abstract Social networks and the information they disseminate play a vital role in today’s society, enabling global communication on both a national and international scale. The rapid exchange of information has transformed platforms such as X (formerly Twitter) into the fastest real-time news network, used by more than 36% of the population. However, this rapid dissemination has also led to the widespread spread of misinformation, as current mechanisms are insufficient to detect fake news. This study aims to address the problem of misinformation in X by developing a sophisticated detection algorithm using data mining techniques and integrating an intelligent chatbot agent. The algorithm will analyze the contents and store the detected fake news to feed them into a mathematical model based on keyword and content analysis. The main contribution of this work lies in the development of a comprehensive program designed to identify fake news, providing the user community with a reliable tool to verify information and thus reduce the spread of misinformation in social networks.

Keywords: Fake News Detection; Misinformation; Algorithm; X (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-24600-4_1

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DOI: 10.1007/978-3-032-24600-4_1

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