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Towards Automatic Veracity Assessment of Open Source Information
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-6779-7435
2015 (English)In: 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015, p. 199-206Conference paper, Published paper (Refereed)
Abstract [en]

Intelligence analysis is dependent on veracity assessment of Open Source Information (OSINF) which includes assessment of the reliability of sources and credibility of information. Traditionally, OSINF veracity assessment is done by intelligence analysts manually, but the large volumes, high velocity, and variety make it infeasible to continue doing so, and calls for automation. Based on meetings, interviews and questionnaires with military personnel, analysis of related work and state of the art, we identify the challenges and propose an approach and a corresponding framework for automated veracity assessment of OSINF. The framework provides a basis for new tools which will give the intelligence analysts the ability to automatically or semi-automatically assess veracity of larger amounts of data in a shorter amount of time. Instead of spending their time working with irrelevant, ambiguous, contradicting, biased, or plain wrong data, they can spend more time on analysis.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. p. 199-206
Keywords [en]
Big Data, public domain software, Big Data, OSINF, automatic data veracity assessment, intelligence analysis, open source information, Automation, Big data, Interviews, Probabilistic logic, Reliability, Semantics, Twitter, NATO STANAG 2511, OSINF, big data, data veracity, reliability and credibility, trust, veracity assessment
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-179402DOI: 10.1109/BigDataCongress.2015.36ISI: 000380443700026Scopus ID: 2-s2.0-84959501022ISBN: 978-1-4673-7277-0 (print)OAI: oai:DiVA.org:kth-179402DiVA, id: diva2:882888
Conference
Big Data (BigData Congress), 2015 IEEE International Congress on, New York, USA, June 27 - July 2, 2015.
Note

QC 20151217

Available from: 2015-12-16 Created: 2015-12-16 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, MarianelaVlassov, Vladimir

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