Information Management in Healthcare and Environment

Towards an Automatic System for Fake News Detection

Bibliographic Data

ID15487589
AuthorsPablo Lara Navarra (0000-0003-0595-3161, Universitat Oberta de Catalunya), H Falciani (0000-0001-8733-1045, Tactical Whistleblower Association, 46022 València, Spain), Enrique A Sánchez‐Pérez (0000-0001-8854-3154, Universitat Politècnica de València), Antonia Ferrer-Sapena (0000-0001-6432-917X, Universitat Politècnica de València, corresponding author)
Year2020
Volume17
Issue3
Pages1066-1066
Publication date2020-02-08
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17031066
PMID32046238
OpenAlexW3005029819
LanguageEN
Citations received8
References cited10

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

Data science · Graph · Health care · Heuristic · Information retrieval · Social media · The Internet · World Wide Web · Complex Network Analysis Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection · Artificial Intelligence

Tools

Open DOISci-HubOpen Access