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Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.ORCID iD: 0000-0002-0408-1421
FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-1610-0917
FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.
Number of Authors: 42017 (English)In: ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop, Association for Computational Linguistics (ACL) , 2017, p. 481-485Conference paper, Published paper (Refereed)
Abstract [en]

For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9% on the competition's test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.

Place, publisher, year, edition, pages
Association for Computational Linguistics (ACL) , 2017. p. 481-485
National Category
Computer Sciences Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-332056Scopus ID: 2-s2.0-85097656375OAI: oai:DiVA.org:kth-332056DiVA, id: diva2:1783103
Conference
11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, Aug 4 2017 - Aug 3 2017
Note

Part of ISBN 9781945626555

QC 20230719

Available from: 2023-07-19 Created: 2023-07-19 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, MarianelaTjörnhammar, Edward

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