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  • 1.
    Garcia Lozano, Marianela
    et al.
    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.
    Brynielsson, Joel
    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.
    Franke, Ulrik
    RISE Res Inst Sweden, POB 1263, SE-16429 Kista, Sweden..
    Rosell, Magnus
    FOI Swedish Def Res Agcy, SE-16490 Stockholm, Sweden..
    Tjörnhammar, Edward
    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.
    Varga, Stefan
    KTH. Swedish Armed Forces Headquarters, Stockholm, SE-107 85, Sweden.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Veracity assessment of online data2020In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 129, article id 113132Article in journal (Refereed)
    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.

  • 2.
    Garcia Lozano, Marianela
    et al.
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Franke, Ulrik
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Rosell, Magnus
    FOI, Swedish Defence Research Agency, Department of Decision Support Systems.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Towards Automatic Veracity Assessment of Open Source Information2015In: 2015 IEEE International Congress on Big Data (BigData Congress), IEEE Computer Society, 2015, p. 199-206Conference 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.

  • 3.
    García Lozano, Marianela
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Toward automated veracity assessment of data from open sources using features and indicators2024Doctoral 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.

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  • 4.
    García Lozano, Marianela
    et al.
    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.
    Fernquist, Johan
    FOI Swedish Defence Research Agency, Stockholm, SE-164 90, Sweden.
    Identifying deceptive reviews: Feature exploration, model transferability and classification attack2019In: Proceedings of the 2019 European Intelligence and Security Informatics Conference, EISIC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 109-116Conference 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. 

  • 5.
    García Lozano, Marianela
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.
    Lilja, Hanna
    FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.
    Tjörnhammar, Edward
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Karasalo, Maja
    FOI Swedish Defence Research Agency, FOI Swedish Defence Research Agency.
    Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules2017In: ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop, Association for Computational Linguistics (ACL) , 2017, p. 481-485Conference 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.

  • 6.
    García Lozano, Marianela
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Sweden.
    Schreiber, Jonah
    KTH, School of Computer Science and Communication (CSC). Google, Inc., United States.
    Brynielsson, Joel
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS. FOI Swedish Defence Research Agency, Sweden.
    Tracking Geographical Locations using a Geo-Aware Topic Model for Analyzing Social Media Data2017In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 99, no SI, p. 18-29Article in journal (Refereed)
    Abstract [en]

    Tracking how discussion topics evolve in social media and where these topics are discussed geographically over time has the potential to provide useful information for many different purposes. In crisis management, knowing a specific topic’s current geographical location could provide vital information to where, or even which, resources should be allocated. This paper describes an attempt to track online discussions geographically over time. A distributed geo-aware streaming latent Dirichlet allocation model was developed for the purpose of recognizing topics’ locations in unstructured text. To evaluate the model it has been implemented and used for automatic discovery and geographical tracking of election topics during parts of the 2016 American presidential primary elections. It was shown that the locations correlated with the actual election locations, and that the model provides a better geolocation classification compared to using a keyword-based approach.

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  • 7.
    Hansen, Patrik
    et al.
    FOI Swedish Defence Research Agency, Stockholm, Sweden, SE-164 90.
    García Lozano, Marianela
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. FOI Swedish Defence Research Agency, Stockholm, Sweden, SE-164 90.
    Kamrani, Farzad
    FOI Swedish Defence Research Agency, Stockholm, Sweden, SE-164 90.
    Brynielsson, Joel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS. FOI Swedish Defence Research Agency, Stockholm, Sweden.
    Real-time estimation of heart rate in situations characterized by dynamic illumination using remote photoplethysmography2023In: Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6094-6103Conference paper (Refereed)
    Abstract [en]

    Remote photoplethysmography (rPPG) is a technique that aims to remotely estimate the heart rate of an individual using an RGB camera. Although several studies use the rPPG methodology, it is usually applied in a laboratory in a controlled environment, where both the camera and the subject are static, and the illumination is ideal for the task. However, applying rPPG in a real-life scenario is much more demanding, since dynamic illumination issues arise. The work presented in this paper introduces a framework to estimate the heart rate of an individual in real-time using an RGB camera in a situation characterized by dynamic illumination. Such situations occur, for example, when either the camera or the subject is moving, and/or the face visibility is limited. The framework uses a face detection program to extract regions of interest on an individual's face. These regions are combined and constitute the input to a convolutional neural network, which is trained to estimate the heart rate in real-time. The method is evaluated on three publicly available datasets, and an in-house dataset specifically collected for the purpose of this study, that includes motions and dynamic illumination. The method shows good performance on all four datasets, outperforming other methods.

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