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Veracity assessment of online data
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS. FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.ORCID iD: 0000-0002-2677-9759
RISE Res Inst Sweden, POB 1263, SE-16429 Kista, Sweden..
FOI Swedish Def Res Agcy, SE-16490 Stockholm, Sweden..
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2020 (English)In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 129, article id 113132Article in journal (Refereed) Published
Abstract [en]

Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research. The most common veracity assessment method among the studied set of papers is to perform text analysis using supervised learning. Regarding methods for machine learning much has happened in the last couple of years related to the advancements made in deep learning. However, very few papers make use of these advancements. Also, the papers in general tend to have a narrow scope, as they focus on solving a small task with only one type of data from one main source. The overall veracity assessment problem is complex, requiring a combination of data sources, data types, indicators, and methods. Only a few papers take on such a broad scope, thus, demonstrating the relative immaturity of the veracity assessment domain.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 129, article id 113132
Keywords [en]
Veracity assessment, Credibility, Data quality, Online data, Social media, Fake news
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-268789DOI: 10.1016/j.dss.2019.113132ISI: 000510956500001Scopus ID: 2-s2.0-85076227196OAI: oai:DiVA.org:kth-268789DiVA, id: diva2:1395576
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QC 20200224

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2020-02-24Bibliographically approved

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Brynielsson, JoelVarga, StefanVlassov, Vladimir

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