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Identifying deceptive reviews: Feature exploration, model transferability and classification attack
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.
FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
2019 (English)In: Proceedings of the 2019 European Intelligence and Security Informatics Conference, EISIC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 109-116Conference paper, Published paper (Refereed)
Abstract [en]

The temptation to influence and sway public opinion most certainly increases with the growth of open online forums where anyone anonymously can express their views and opinions. Since online review sites are a popular venue for opinion influencing attacks, there is a need to automatically identify deceptive posts. The main focus of this work is on automatic identification of deceptive reviews, both positive and negative biased. With this objective, we build a deceptive review SVM based classification model and explore the performance impact of using different feature types (TF-IDF, word2vec, PCFG). Moreover, we study the transferability of trained classification models applied to review data sets of other types of products, and, the classifier robustness, i.e., the accuracy impact, against attacks by stylometry obfuscation trough machine translation. Our findings show that i) we achieve an accuracy of over 90% using different feature types, ii) the trained classification models do not perform well when applied on other data sets containing reviews of different products, and iii) machine translation only slightly impacts the results and can not be used as a viable attack method. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 109-116
Keywords [en]
Classification, Deceptive, Fake, PCFG, SVM, Word2vec, Automation, Computational linguistics, Computer aided language translation, Social aspects, Support vector machines, Attack methods, Classification models, Feature types, Machine translations, Model transferabilities, Online reviews, Performance impact, Public opinions, Classification (of information)
National Category
Computer Sciences Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-285414DOI: 10.1109/EISIC49498.2019.9108852Scopus ID: 2-s2.0-85087087979ISBN: 9781728167350 (print)OAI: oai:DiVA.org:kth-285414DiVA, id: diva2:1505105
Conference
2019 European Intelligence and Security Informatics Conference, EISIC 2019, 26 November 2019 through 27 November 2019
Note

QC 20201130

Available from: 2020-11-30 Created: 2020-11-30 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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García Lozano, Marianela

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