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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
Note

QC 20200224

Available from: 2020-02-24 Created: 2020-02-24 Last updated: 2024-05-14Bibliographically approved
In thesis
1. Toward automated veracity assessment of data from open sources using features and indicators
Open this publication in new window or tab >>Toward automated veracity assessment of data from open sources using features and indicators
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This dissertation hypothesizes that the key to automated veracity assessment of data from open sources is the careful estimation and extraction of relevant features and indicators. These features and indicators provide added value to a quantifiable veracity assessment, either directly or indirectly. The importance and usefulness of a veracity assessment largely depend on the specific situation and reason for which it is being conducted. Factors such as the recipient of the veracity assessment, the scope of the assessment, and the metrics used to measure accuracy and performance, all play a role in determining the value and perceived quality of the assessment.

Five peer-reviewed publications; two journal articles, two conference articles, and one workshop article, are included in this compilation thesis.

The main contributions of the work presented in this dissertation are: i) a compilation of challenges with manual methods of veracity assessment, ii) a road map for addressing the identified challenges, iii) identification of the state-of-the-art and gap analysis of veracity assessment of open-source data, iv) exploration of indicators such as topic geo-location tracking over time and stance classification, and v) evaluation of various feature types, model transferability, and style obfuscation attacks and the impact on accuracy for automated veracity assessment of a type of deception: fake reviews.

Abstract [sv]

Denna avhandling har som hypotes att nyckeln till automatiserad trovärdighetsbedömning av data från öppna källor ligger i det noggranna urvalet och estimeringen av relevanta särdrag och indikatorer. Dessa särdrag och indikatorer ger ett direkt eller indirekt mervärde till en kvantifierbar trovärdighetsbedömning. Betydelsen och användbarheten av en trovärdighetsbedömning beror till stor del på den specifika kontexten och anledningen till att den genomförs. Faktorer som mottagaren av trovärdighetsbedömningen, omfattningen av bedömningen och de mått som används för att mäta noggrannhet och prestanda, spelar alla in för att bestämma värdet och den upplevda kvalitén på bedömningen.

Fem referentgranskade publikationer ingår i denna sammanläggningsavhandling; två tidskriftsartiklar, två konferensartiklar och en workshopartikel.

De huvudsakliga bidragen från arbetet som presenteras i denna avhandling är: i) en sammanställning av utmaningar relaterade till manuella metoder för trovärdighetsbedömning, ii) en plan för att ta itu med de identifierade utmaningarna, iii) identifiering av forskningsfronten och en gapanalys av trovärdighetsbedömning av data från öppna källor, iv) studie av indikatorer såsom geolokalisering av ämnen och spårning av dem över tid samt klassificering av individers reaktioner i inlägg på sociala medier, och v) en utvärdering av särdragstyper som påverkar noggrannheten för automatisk trovärdighetsbedömning applicerat på en typ av bedrägeri: falska recensioner.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2024. p. 71
Series
TRITA-EECS-AVL ; 2024:47
Keywords
Veracity assessment, natural language processing, machine learning, open-source data, Trovärdighetsbedömning, naturlig språkbehandling, maskininlärning, data från öppna källor
National Category
Software Engineering
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-346353 (URN)978-91-8040-927-8 (ISBN)
Public defence
2024-06-03, https://kth-se.zoom.us/j/63226866138, Sal C, Kistagången 16, Stockholm, 13:30 (English)
Opponent
Supervisors
Note

QC 20240514

Available from: 2024-05-14 Created: 2024-05-13 Last updated: 2024-05-21Bibliographically approved

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Garcia Lozano, MarianelaBrynielsson, JoelTjörnhammar, EdwardVarga, StefanVlassov, Vladimir

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