Toward automated veracity assessment of data from open sources using features and indicators
Time: Mon 2024-06-03 13.30
Location: Sal C, Kistagången 16
Video link: https://kth-se.zoom.us/j/63226866138
Language: English
Subject area: Information and Communication Technology
Doctoral student: Marianela García Lozano , Programvaruteknik och datorsystem, SCS
Opponent: Valentina Dragos, ONERA The French Aerospace Lab
Supervisor: Vladimir Vlassov, Programvaruteknik och datorsystem, SCS; Joel Brynielsson, Teoretisk datalogi, TCS; Anne Håkansson, Programvaruteknik och datorsystem, SCS; Magnus Rosell,
QC 20240514
Abstract
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.