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  • 1.
    Akrami, Nazar
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Berggren, Mathias
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
    Kaati, Lisa
    Swedish Defense Research Agency.
    Obaidi, Milan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
    Cohen, Katie
    Swedish Defense Research Agency.
    Assessment of risk in written communication: Introducing the Profile Risk Assessment Tool (PRAT)2018Report (Other academic)
  • 2.
    Ashcroft, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Johansson, Fredrik
    Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Multi-domain alias matching using machine learning2016In: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, p. 77-84Conference paper (Refereed)
    Abstract [en]

    We describe a methodology for linking aliases belonging to the same individual based on a user's writing style (stylometric features extracted from the user generated content) and her time patterns (time-based features extracted from the publishing times of the user generated content). While most previous research on social media identity linkage relies on matching usernames, our methodology can also be used for users who actively try to choose dissimilar usernames when creating their aliases. In our experiments on a discussion forum dataset and a Twitter dataset, we evaluate the performance of three different classifiers. We use the best classifier (AdaBoost) to evaluate how well it works on different datasets using different features. Experiments show that combining stylometric and time based features yield good results on our synthetic datasets and a small-scale evaluation on real-world blog data confirm these results, yielding a precision over 95%. The use of emotion-related and Twitter-related features yield no significant impact on the results.

  • 3.
    Atig, Mohamed Faouzi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cassel, Sofia
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Activity profiles in online social media2014In: Proc. 6th International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2014, p. 850-855Conference paper (Refereed)
  • 4. Cohen, Katie
    et al.
    Isbister, Tim
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Linguistic markers of a radicalized mind-set among extreme adopters2017In: Proc. 10th ACM International Conference on Web Search and Data Mining, New York: ACM Press, 2017, p. 823-824Conference paper (Refereed)
    Abstract [en]

    The words that we use when communicating in social media can reveal how we relate to ourselves and to others. For instance, within many online communities, the degree of adaptation to a community-specific jargon can serve as a marker of identification with the community. In this paper we single out a group of so called extreme adopters of community-specific jargon from the whole group of users of a Swedish discussion forum devoted to the topics immigration and integration. The forum is characterized by a certain xenophobic jargon, and we hypothesize that extreme adopters of this jargon also exhibit certain linguistic features that we view as markers of a radicalized mind-set. We use a Swedish translation of LIWC (linguistic inquiry word count) and find that the group of extreme adopters differs significantly from the whole group of forum users regarding six out of seven linguistic markers of a radicalized mind-set.

  • 5. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Detecting multiple aliases in social media2013In: Proc. 5th International Conference on Advances in Social Networks Analysis and Mining, New York: ACM Press, 2013, p. 1004-1011Conference paper (Refereed)
  • 6. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Time profiles for identifying users in online environments2014In: Proc. 1st Joint Intelligence and Security Informatics Conference, IEEE Computer Society, 2014, p. 83-90Conference paper (Refereed)
  • 7. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Timeprints for identifying social media users with multiple aliases2015In: Security Informatics, ISSN 2190-8532, Vol. 4, p. 7:1-11, article id 7Article in journal (Refereed)
  • 8.
    Kaati, Lisa
    et al.
    FOI, Stockholm, Sweden..
    Lundeqvist, Elias
    FOI, Stockholm, Sweden..
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Svensson, Maria
    FOI, Stockholm, Sweden..
    Author Profiling in the Wild2017In: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, p. 155-158Conference paper (Refereed)
    Abstract [en]

    In this paper, we use machine learning for profiling authors of online textual media. We are interested in determining the gender and age of an author. We use two different approaches, one where the features are learned from raw data and one where features are manually extracted. We are interested in understanding how well author profiling works in the wild and therefore we have tested our models on different domains than they are trained on. Our results show that applying models to a different domain then they were trained on significantly decreases the performance of the models. The results show that more efforts need to be put into making models domain independent if techniques such as author profiling should be used operationally, for example by training on many different datasets and by using domain independent features.

  • 9.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Omer, Enghin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Prucha, Nico
    ICSR, London, England.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Detecting multipliers of jihadism on twitter2015In: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, p. 954-960Conference paper (Refereed)
    Abstract [en]

    Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. In this paper we describe a first step towards automatically classifying twitter user accounts (tweeps) as supporters of jihadist groups who disseminate propaganda content online. We use a machine learning approach with two set of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and that can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added.

  • 10.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cohen, Katie
    Linguistic analysis of lone offender manifestos2016In: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Conference paper (Refereed)
  • 11.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cohen, Katie
    Lindquist, Sinna
    Automatic detection of xenophobic narratives: A case study on Swedish alternative media2016In: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, p. 121-126Conference paper (Refereed)
  • 12.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Sardella, Tony
    Washington Univ, St Louis, MO USA..
    Identifying Warning Behaviors of Violent Lone Offenders in Written Communication2016In: 2016 IEEE 16Th International Conference On Data Mining Workshops (ICDMW) / [ed] Domeniconi, C Gullo, F Bonchi, F DomingoFerrer, J BaezaYates, R Zhou, ZH Wu, X, New York: IEEE, 2016, p. 1053-1060Conference paper (Refereed)
    Abstract [en]

    Violent lone offenders such as school shooters and lone actor terrorists pose a threat to the modern society but since they act alone or with minimal help form others they are very difficult to detect. Previous research has shown that violent lone offenders show signs of certain psychological warning behaviors that can be viewed as indicators of an increasing or accelerating risk of committing targeted violence. In this work, we use a machine learning approach to identify potential violent lone offenders based on their written communication. The aim of this work is to capture psychological warning behaviors in written text and identify texts written by violent lone offenders. We use a set of features that are psychologically meaningful based on the different categories in the text analysis tool Linguistic Inquiry and Word Count (LIWC). Our study only contains a small number of known perpetrators and their written communication but the results are promising and there are many interesting directions for future work in this area.

  • 13.
    Kaati, Lisa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Sardella, Tony
    Identifying warning behaviors of violent lone offenders in written communication2016In: Proc. 16th ICDM Workshops, IEEE Computer Society, 2016, p. 1053-1060Conference paper (Refereed)
  • 14.
    Ngai, Edith C.-H.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Brandauer, Stephan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Vandikas, Konstantinos
    Ericsson Res, Kista, Sweden..
    Personalized Mobile-Assisted Smart Transportation2016In: 2016 Digital Media Industry And Academic Forum (DMIAF), 2016, p. 158-160Conference paper (Refereed)
    Abstract [en]

    Digital media covers larger parts of our daily lives nowadays. Mobile services enable a better connected society where citizens can easily access public services, discover events, and obtain important information in the city. We observe the popularity of mobile car sharing applications, such as Uber and Didi Dache. Mobile social applications provide new ways of developing and optimizing public transportation. In this paper, we present a mobile platform for timetable-free traveling. It can capture the traffic demand of citizens in real-time, and support efficient planning and scheduling for vehicles on-demand. At the moment, the platform is targeted for public bus services, but it has great potential to be extended for self-driving vehicles in the future.

  • 15.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    A tool for visualizing and analyzing users on discussion boards2013In: European Intelligence and Security Informatics Conference: 2013, IEEE Computer Society, 2013, p. 229-229Conference paper (Refereed)
  • 16.
    Shrestha, Amendra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computer Systems.
    Techniques for analyzing digital environments from a security perspective2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The development of the Internet and social media has exploded in the last couple of years. Digital environments such as social media and discussion forums provide an effective method of communication and are used by various groups in our societies.  For example, violent extremist groups use social media platforms for recruiting, training, and communicating with their followers, supporters, and donors. Analyzing social media is an important task for law enforcement agencies in order to detect activity and individuals that might pose a threat towards the security of the society.

    In this thesis, a set of different technologies that can be used to analyze digital environments from a security perspective are presented. Due to the nature of the problems that are studied, the research is interdisciplinary, and knowledge from terrorism research, psychology, and computer science are required. The research is divided into three different themes. Each theme summarizes the research that has been done in a specific area.

    The first theme focuses on analyzing digital environments and phenomena. The theme consists of three different studies. The first study is about the possibilities to detect propaganda from the Islamic State on Twitter.  The second study focuses on identifying references to a narrative containing xenophobic and conspiratorial stereotypes in alternative immigration critic media. In the third study, we have defined a set of linguistic features that we view as markers of a radicalization.

    A group consists of a set of individuals, and in some cases, individuals might be a threat towards the security of the society.  The second theme focuses on the risk assessment of individuals based on their written communication. We use different technologies including machine learning to experiment the possibilities to detect potential lone offenders.  Our risk assessment approach is implemented in the tool PRAT (Profile Risk Assessment Tool).

    Internet users have the ability to use different aliases when they communicate since it offers a degree of anonymity. In the third theme, we present a set of techniques that can be used to identify users with multiple aliases. Our research focuses on solving two different problems: author identification and alias matching. The technologies that we use are based on the idea that each author has a fairly unique writing style and that we can construct a writeprint that represents the author. In a similar manner,  we also use information about when a user communicates to create a timeprint. By combining the writeprint and the timeprint, we can obtain a set of powerful features that can be used to identify users with multiple aliases.

    To ensure that the technologies can be used in real scenarios, we have implemented and tested the techniques on data from social media. Several of the results are promising, but more studies are needed to determine how well they work in reality.

  • 17.
    Shrestha, Amendra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Cassel, Sofia
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Atig, Mohamed Faouzi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Author recognition in discussion boards2013In: National Symposium on Technology and Methodology for Security and Crisis Management, 2013Conference paper (Refereed)
  • 18.
    Shrestha, Amendra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Kaati, Lisa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. FOI, Stockholm, Sweden..
    Cohen, Katie
    FOI, Stockholm, Sweden..
    A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities2017In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) / [ed] Tjoa, AM Wagner, RR, IEEE, 2017, p. 1-5Conference paper (Other academic)
    Abstract [en]

    In this study we try to identify extreme adopters on a discussion forum using machine learning. An extreme adopter is a user that has adopted a high level of a community-specific jargon and therefore can be seen as a user that has a high degree of identification with the community. The dataset that we consider consists of a Swedish xenophobic discussion forum where we use a machine learning approach to identify extreme adopters using a number of linguistic features that are independent on the dataset and the community. The results indicates that it is possible to separate these extreme adopters from the rest of the discussants on the discussion forum with more than 80% accuracy. Since the linguistic features that we use are highly domain independent, the results indicates that there is a possibility to use this kind of techniques to identify extreme adopters within other communities as well.

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