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  • 1.
    Akrami, Nazar
    et al.
    Uppsala universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Samhällsvetenskapliga fakulteten, Institutionen för psykologi.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Berggren, Mathias
    Uppsala universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Samhällsvetenskapliga fakulteten, Institutionen för psykologi.
    Kaati, Lisa
    Swedish Defense Research Agency.
    Obaidi, Milan
    Uppsala universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Samhällsvetenskapliga fakulteten, Institutionen för psykologi.
    Cohen, Katie
    Swedish Defense Research Agency.
    Assessment of risk in written communication: Introducing the Profile Risk Assessment Tool (PRAT)2018Rapport (Annet vitenskapelig)
  • 2.
    Ashcroft, Michael
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datalogi.
    Johansson, Fredrik
    Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. Swedish Def Res Agcy FOI, Stockholm, Sweden..
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Multi-domain alias matching using machine learning2016Inngår i: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, s. 77-84Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Cassel, Sofia
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Activity profiles in online social media2014Inngår i: Proc. 6th International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2014, s. 850-855Konferansepaper (Fagfellevurdert)
  • 4. Cohen, Katie
    et al.
    Isbister, Tim
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Linguistic markers of a radicalized mind-set among extreme adopters2017Inngår i: Proc. 10th ACM International Conference on Web Search and Data Mining, New York: ACM Press, 2017, s. 823-824Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Detecting multiple aliases in social media2013Inngår i: Proc. 5th International Conference on Advances in Social Networks Analysis and Mining, New York: ACM Press, 2013, s. 1004-1011Konferansepaper (Fagfellevurdert)
  • 6. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Time profiles for identifying users in online environments2014Inngår i: Proc. 1st Joint Intelligence and Security Informatics Conference, IEEE Computer Society, 2014, s. 83-90Konferansepaper (Fagfellevurdert)
  • 7. Johansson, Fredrik
    et al.
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Timeprints for identifying social media users with multiple aliases2015Inngår i: Security Informatics, ISSN 2190-8532, Vol. 4, s. 7:1-11, artikkel-id 7Artikkel i tidsskrift (Fagfellevurdert)
  • 8.
    Kaati, Lisa
    et al.
    FOI, Stockholm, Sweden..
    Lundeqvist, Elias
    FOI, Stockholm, Sweden..
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Svensson, Maria
    FOI, Stockholm, Sweden..
    Author Profiling in the Wild2017Inngår i: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, s. 155-158Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Omer, Enghin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Prucha, Nico
    ICSR, London, England.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Detecting multipliers of jihadism on twitter2015Inngår i: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, s. 954-960Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Cohen, Katie
    Linguistic analysis of lone offender manifestos2016Inngår i: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Konferansepaper (Fagfellevurdert)
  • 11.
    Kaati, Lisa
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Cohen, Katie
    Lindquist, Sinna
    Automatic detection of xenophobic narratives: A case study on Swedish alternative media2016Inngår i: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, s. 121-126Konferansepaper (Fagfellevurdert)
  • 12.
    Kaati, Lisa
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Sardella, Tony
    Washington Univ, St Louis, MO USA..
    Identifying Warning Behaviors of Violent Lone Offenders in Written Communication2016Inngår i: 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, s. 1053-1060Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Sardella, Tony
    Identifying warning behaviors of violent lone offenders in written communication2016Inngår i: Proc. 16th ICDM Workshops, IEEE Computer Society, 2016, s. 1053-1060Konferansepaper (Fagfellevurdert)
  • 14.
    Ngai, Edith C.-H.
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Brandauer, Stephan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datalogi.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Vandikas, Konstantinos
    Ericsson Res, Kista, Sweden..
    Personalized Mobile-Assisted Smart Transportation2016Inngår i: 2016 Digital Media Industry And Academic Forum (DMIAF), 2016, s. 158-160Konferansepaper (Fagfellevurdert)
    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 universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    A tool for visualizing and analyzing users on discussion boards2013Inngår i: European Intelligence and Security Informatics Conference: 2013, IEEE Computer Society, 2013, s. 229-229Konferansepaper (Fagfellevurdert)
  • 16.
    Shrestha, Amendra
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för datorteknik.
    Techniques for analyzing digital environments from a security perspective2019Doktoravhandling, med artikler (Annet vitenskapelig)
    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.

    Delarbeid
    1. A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities
    Åpne denne publikasjonen i ny fane eller vindu >>A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities
    2017 (engelsk)Inngår i: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) / [ed] Tjoa, AM Wagner, RR, IEEE, 2017, s. 1-5Konferansepaper, Publicerat paper (Annet vitenskapelig)
    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.

    sted, utgiver, år, opplag, sider
    IEEE, 2017
    Serie
    International Workshop on Database and Expert Systems Applications-DEXA, ISSN 1529-4188
    Emneord
    Discussion forums, Support vector machines, Pragmatics, Manuals, Radio frequency, Electronic mail, Social network services
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-351187 (URN)10.1109/DEXA.2017.17 (DOI)000426078300001 ()978-1-5386-1051-0 (ISBN)
    Konferanse
    28th International Workshop on Database and Expert Systems Applications (DEXA), AUG 28-31, 2017, Lyon3 Univ, Lyon, FRANCE
    Tilgjengelig fra: 2018-05-23 Laget: 2018-05-23 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    2. Identifying warning behaviors of violent lone offenders in written communication
    Åpne denne publikasjonen i ny fane eller vindu >>Identifying warning behaviors of violent lone offenders in written communication
    2016 (engelsk)Inngår i: Proc. 16th ICDM Workshops, IEEE Computer Society, 2016, s. 1053-1060Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    IEEE Computer Society, 2016
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-306943 (URN)10.1109/ICDMW.2016.0152 (DOI)978-1-5090-5910-2 (ISBN)
    Konferanse
    ICDM Workshop on Social Media and Risk, SOMERIS 2016, December 12, Barcelona, Spain
    Tilgjengelig fra: 2017-02-02 Laget: 2016-11-07 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    3. Automatic detection of xenophobic narratives: A case study on Swedish alternative media
    Åpne denne publikasjonen i ny fane eller vindu >>Automatic detection of xenophobic narratives: A case study on Swedish alternative media
    2016 (engelsk)Inngår i: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, s. 121-126Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    IEEE, 2016
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-306903 (URN)10.1109/ISI.2016.7745454 (DOI)000390129600021 ()978-1-5090-3865-7 (ISBN)
    Konferanse
    ISI 2016, September 28–30, Tucson, AZ
    Tilgjengelig fra: 2016-11-17 Laget: 2016-11-04 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    4. Linguistic analysis of lone offender manifestos
    Åpne denne publikasjonen i ny fane eller vindu >>Linguistic analysis of lone offender manifestos
    2016 (engelsk)Inngår i: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    IEEE, 2016
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-306941 (URN)10.1109/ICCCF.2016.7740427 (DOI)000390123800007 ()978-1-5090-6096-2 (ISBN)
    Konferanse
    ICCCF 2016, June 12–14, Vancouver, Canada
    Tilgjengelig fra: 2016-11-17 Laget: 2016-11-07 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    5. Detecting multipliers of jihadism on twitter
    Åpne denne publikasjonen i ny fane eller vindu >>Detecting multipliers of jihadism on twitter
    2015 (engelsk)Inngår i: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, s. 954-960Konferansepaper, Publicerat paper (Fagfellevurdert)
    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.

    sted, utgiver, år, opplag, sider
    IEEE Computer Society, 2015
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-272243 (URN)10.1109/ICDMW.2015.9 (DOI)000380556700127 ()9781467384926 (ISBN)
    Eksternt samarbeid:
    Konferanse
    ICDM Workshop on Intelligence and Security Informatics, ISI-ICDM 2015, November 14, Atlantic City, NJ
    Tilgjengelig fra: 2015-11-14 Laget: 2016-01-12 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    6. Detecting multiple aliases in social media
    Åpne denne publikasjonen i ny fane eller vindu >>Detecting multiple aliases in social media
    2013 (engelsk)Inngår i: Proc. 5th International Conference on Advances in Social Networks Analysis and Mining, New York: ACM Press, 2013, s. 1004-1011Konferansepaper, Publicerat paper (Fagfellevurdert)
    sted, utgiver, år, opplag, sider
    New York: ACM Press, 2013
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-216568 (URN)10.1145/2492517.2500261 (DOI)978-1-4503-2240-9 (ISBN)
    Konferanse
    ASONAM 2013, August 25-29, Niagara Falls, Canada
    Forskningsfinansiär
    Vinnova
    Tilgjengelig fra: 2013-08-29 Laget: 2014-01-23 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    7. Timeprints for identifying social media users with multiple aliases
    Åpne denne publikasjonen i ny fane eller vindu >>Timeprints for identifying social media users with multiple aliases
    2015 (engelsk)Inngår i: Security Informatics, ISSN 2190-8532, Vol. 4, s. 7:1-11, artikkel-id 7Artikkel i tidsskrift (Fagfellevurdert) Published
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-272242 (URN)10.1186/s13388-015-0022-z (DOI)
    Tilgjengelig fra: 2015-09-24 Laget: 2016-01-12 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    8. Multi-domain alias matching using machine learning
    Åpne denne publikasjonen i ny fane eller vindu >>Multi-domain alias matching using machine learning
    2016 (engelsk)Inngår i: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, s. 77-84Konferansepaper, Publicerat paper (Fagfellevurdert)
    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.

    sted, utgiver, år, opplag, sider
    IEEE, 2016
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-306944 (URN)10.1109/ENIC.2016.019 (DOI)000399097600011 ()9781509034550 (ISBN)
    Konferanse
    ENIC 2016, September 5–7, Wroclaw, Poland
    Tilgjengelig fra: 2017-02-02 Laget: 2016-11-07 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    9. Assessment of risk in written communication: Introducing the Profile Risk Assessment Tool (PRAT)
    Åpne denne publikasjonen i ny fane eller vindu >>Assessment of risk in written communication: Introducing the Profile Risk Assessment Tool (PRAT)
    Vise andre…
    2018 (engelsk)Rapport (Annet vitenskapelig)
    sted, utgiver, år, opplag, sider
    Belgium: EUROPOL, 2018. s. 24
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-367346 (URN)
    Merknad

    This paper was presented at the 2nd European Counter-Terrorism Centre (ECTC) Advisory Groupconference, 17-18 April 2018, at Europol Headquarters, The Hague.

    Tilgjengelig fra: 2018-11-30 Laget: 2018-11-30 Sist oppdatert: 2019-03-22bibliografisk kontrollert
    10. Linguistic markers of a radicalized mind-set among extreme adopters
    Åpne denne publikasjonen i ny fane eller vindu >>Linguistic markers of a radicalized mind-set among extreme adopters
    2017 (engelsk)Inngår i: Proc. 10th ACM International Conference on Web Search and Data Mining, New York: ACM Press, 2017, s. 823-824Konferansepaper, Publicerat paper (Fagfellevurdert)
    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.

    sted, utgiver, år, opplag, sider
    New York: ACM Press, 2017
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-379919 (URN)10.1145/3018661.3022760 (DOI)978-1-4503-4675-7 (ISBN)
    Konferanse
    WSDM 2017, 1st International Workshop on Cyber Deviance Detection
    Tilgjengelig fra: 2017-02-02 Laget: 2019-03-21 Sist oppdatert: 2019-04-08bibliografisk kontrollert
  • 17.
    Shrestha, Amendra
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Cassel, Sofia
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Atig, Mohamed Faouzi
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Author recognition in discussion boards2013Inngår i: National Symposium on Technology and Methodology for Security and Crisis Management, 2013Konferansepaper (Fagfellevurdert)
  • 18.
    Shrestha, Amendra
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
    Kaati, Lisa
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. FOI, Stockholm, Sweden..
    Cohen, Katie
    FOI, Stockholm, Sweden..
    A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities2017Inngår i: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) / [ed] Tjoa, AM Wagner, RR, IEEE, 2017, s. 1-5Konferansepaper (Annet vitenskapelig)
    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.

1 - 18 of 18
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