Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection
| Autor: | Hervé Falciani, Antonia Ferrer-Sapena, Pablo Lara Navarra, Enrique A. Sánchez-Pérez |
|---|---|
| Přispěvatelé: | Universitat Politècnica de València, Tactical Whistleblower Association, Universitat Oberta de Catalunya. Estudis de Ciències de la Informació i de la Comunicació |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Information management
fake news Information Management Computer science atención sanitaria Health Toxicology and Mutagenesis Health Behavior lcsh:Medicine 02 engineering and technology Global Health Graph gráfico Reinforcement learning 0202 electrical engineering electronic engineering information engineering aprendizaje de refuerzo atenció sanitària health care economics and organizations Healthcare healthcare graph Open data Fake news Online Social Networking Graph (abstract data type) 020201 artificial intelligence & image processing The Internet 0305 other medical science MATEMATICA APLICADA Environmental Health environment Algorithms reinforcement learning education BIBLIOTECONOMIA Y DOCUMENTACION Environment Article 03 medical and health sciences medio ambiente Web page notícies falses Humans Social media gràfic Internet 030505 public health Consumer Health Information Social network business.industry Communications Media lcsh:R Public Health Environmental and Occupational Health Data science Health Literacy medi ambient aprenentatge de reforç business Social Media noticias falsas |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 17 Issue 3 RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname O2, repositorio institucional de la UOC Universitat Oberta de Catalunya (UOC) International Journal of Environmental Research and Public Health, Vol 17, Iss 3, p 1066 (2020) |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph17031066 |
| Popis: | 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&rsquo 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&mdash a database implemented with Neo4j&mdash 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. |
| Databáze: | OpenAIRE |
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