Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection

Lara-Navarra, Pablo, Falciani, Hervè, Sánchez-Pérez, Enrique A. and Ferrer-Sapena, Antonia Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection. Information Management in Healthcare and Environment:, 0018, vol. 17, n. 1066. [Journal article (Unpaginated)]

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English abstract

Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool—a database implemented with Neo4j—and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health

Spanish abstract

Los comentarios e informaciones que aparecen en Internet y en diferentes medios sociales influyen en la opinión sobre los posibles remedios para el diagnóstico y la cura de las enfermedades. En muchos casos, esto repercute en la salud de los ciudadanos y afecta a los profesionales de la medicina, que se ven obligados a defender sus diagnósticos y los tratamientos que proponen contra los pacientes mal informados. La propagación de estas opiniones sigue el mismo patrón que la difusión de noticias falsas sobre otros temas importantes, como el medio ambiente, a través de las redes de medios sociales, que utilizamos como campo de pruebas para comprobar nuestro procedimiento. En este artículo presentamos un algoritmo para analizar el comportamiento de los usuarios de Twitter, la red social más importante con respecto a este tema, así como un método de construcción de gráficos de conocimiento dinámico basado en la información recogida en Twitter y otras fuentes de datos abiertas como las páginas web. Para mostrar nuestra metodología, presentamos un ejemplo concreto de cómo se utiliza la estructura gráfica asociada de los tweets relacionados con el Día Mundial del Medio Ambiente 2019 para desarrollar un análisis heurístico de la validez de la información. El esquema analítico propuesto se basa en la interacción entre la herramienta informática -una base de datos implementada con Neo4j- y el analista, que debe hacer las preguntas adecuadas a la herramienta, permitiendo seguir la línea de cualquier dato dudoso. También mostramos cómo se puede utilizar este método. También presentamos algunas pautas metodológicas sobre cómo nuestro sistema podría permitir, en el futuro, una automatización de los procedimientos para la construcción de un algoritmo autónomo para la detección de noticias falsas en Internet relacionadas con la salud

Item type:	Journal article (Unpaginated)
Keywords:	healthcare; environment; fake news; reinforcement learning; graph
Subjects:	H. Information sources, supports, channels. > HZ. None of these, but in this section. L. Information technology and library technology > LP. Intelligent agents.
Depositing user:	Antonia Ferrer
Date deposited:	13 Nov 2020 21:57
Last modified:	13 Nov 2020 21:57
URI:	http://hdl.handle.net/10760/40630

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