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Kaati, Lisa
Publications (10 of 28) Show all publications
Kaati, L., Shrestha, A., Cohen, K. & Lindquist, S. (2016). Automatic detection of xenophobic narratives: A case study on Swedish alternative media. In: Proc. 14th International Conference on Intelligence and Security Informatics: . Paper presented at ISI 2016, September 28–30, Tucson, AZ (pp. 121-126). IEEE.
Open this publication in new window or tab >>Automatic detection of xenophobic narratives: A case study on Swedish alternative media
2016 (English)In: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, 121-126 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-306903 (URN)10.1109/ISI.2016.7745454 (DOI)000390129600021 ()978-1-5090-3865-7 (ISBN)
Conference
ISI 2016, September 28–30, Tucson, AZ
Available from: 2016-11-17 Created: 2016-11-04 Last updated: 2018-01-13Bibliographically approved
Kaati, L. & Johansson, F. (2016). Countering lone actor terrorism: Weak signals and online activities. In: Understanding Lone Actor Terrorism: Past experience, future outlook, and response strategies (pp. 266-279). Abingdon, UK: Routledge.
Open this publication in new window or tab >>Countering lone actor terrorism: Weak signals and online activities
2016 (English)In: Understanding Lone Actor Terrorism: Past experience, future outlook, and response strategies, Abingdon, UK: Routledge, 2016, 266-279 p.Chapter in book (Refereed)
Place, publisher, year, edition, pages
Abingdon, UK: Routledge, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-310298 (URN)10.4324/9781315657622 (DOI)2-s2.0-84966971035 (Scopus ID)978-1-138-10051-0 (ISBN)
Available from: 2016-02-05 Created: 2016-12-13 Last updated: 2018-01-13Bibliographically approved
Kaati, L., Shrestha, A. & Sardella, T. (2016). Identifying Warning Behaviors of Violent Lone Offenders in Written Communication. In: Domeniconi, C Gullo, F Bonchi, F DomingoFerrer, J BaezaYates, R Zhou, ZH Wu, X (Ed.), 2016 IEEE 16Th International Conference On Data Mining Workshops (ICDMW): . Paper presented at 16th IEEE International Conference on Data Mining (ICDM), DEC 12-15, 2016, Barcelona, SPAIN (pp. 1053-1060). New York: IEEE.
Open this publication in new window or tab >>Identifying Warning Behaviors of Violent Lone Offenders in Written Communication
2016 (English)In: 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, 1053-1060 p.Conference paper, Published 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.

Place, publisher, year, edition, pages
New York: IEEE, 2016
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-332915 (URN)10.1109/ICDMW.2016.0152 (DOI)000401906900144 ()978-1-5090-5910-2 (ISBN)
Conference
16th IEEE International Conference on Data Mining (ICDM), DEC 12-15, 2016, Barcelona, SPAIN
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2018-01-13Bibliographically approved
Kaati, L., Shrestha, A. & Sardella, T. (2016). Identifying warning behaviors of violent lone offenders in written communication. In: Proc. 16th ICDM Workshops: . Paper presented at ICDM Workshop on Social Media and Risk, SOMERIS 2016, December 12, Barcelona, Spain (pp. 1053-1060). IEEE Computer Society.
Open this publication in new window or tab >>Identifying warning behaviors of violent lone offenders in written communication
2016 (English)In: Proc. 16th ICDM Workshops, IEEE Computer Society, 2016, 1053-1060 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-306943 (URN)10.1109/ICDMW.2016.0152 (DOI)978-1-5090-5910-2 (ISBN)
Conference
ICDM Workshop on Social Media and Risk, SOMERIS 2016, December 12, Barcelona, Spain
Available from: 2017-02-02 Created: 2016-11-07 Last updated: 2018-01-13Bibliographically approved
Kaati, L., Shrestha, A. & Cohen, K. (2016). Linguistic analysis of lone offender manifestos. In: Proc. 4th International Conference on Cybercrime and Computer Forensics: . Paper presented at ICCCF 2016, June 12–14, Vancouver, Canada. IEEE.
Open this publication in new window or tab >>Linguistic analysis of lone offender manifestos
2016 (English)In: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-306941 (URN)10.1109/ICCCF.2016.7740427 (DOI)000390123800007 ()978-1-5090-6096-2 (ISBN)
Conference
ICCCF 2016, June 12–14, Vancouver, Canada
Available from: 2016-11-17 Created: 2016-11-07 Last updated: 2018-01-13Bibliographically approved
Figea, L., Kaati, L. & Scrivens, R. (2016). Measuring online affects in a white supremacy forum. In: Proc. 14th International Conference on Intelligence and Security Informatics: . Paper presented at ISI 2016, September 28–30, Tucson, AZ (pp. 85-90). IEEE.
Open this publication in new window or tab >>Measuring online affects in a white supremacy forum
2016 (English)In: Proc. 14th International Conference on Intelligence and Security Informatics, IEEE, 2016, 85-90 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-306904 (URN)10.1109/ISI.2016.7745448 (DOI)000390129600015 ()978-1-5090-3865-7 (ISBN)
Conference
ISI 2016, September 28–30, Tucson, AZ
Available from: 2016-11-17 Created: 2016-11-04 Last updated: 2018-01-13Bibliographically approved
Ashcroft, M., Johansson, F., Kaati, L. & Shrestha, A. (2016). Multi-domain alias matching using machine learning. In: Proc. 3rd European Network Intelligence Conference: . Paper presented at ENIC 2016, September 5–7, Wroclaw, Poland (pp. 77-84). IEEE.
Open this publication in new window or tab >>Multi-domain alias matching using machine learning
2016 (English)In: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, 77-84 p.Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-306944 (URN)10.1109/ENIC.2016.019 (DOI)000399097600011 ()9781509034550 (ISBN)
Conference
ENIC 2016, September 5–7, Wroclaw, Poland
Available from: 2017-02-02 Created: 2016-11-07 Last updated: 2018-01-13Bibliographically approved
Kaati, L., Johansson, F. & Forsman, E. (2016). Semantic technologies for detecting names of new drugs on darknets. In: Proc. 4th International Conference on Cybercrime and Computer Forensics: . Paper presented at ICCCF 2016, June 12–14, Vancouver, Canada. IEEE.
Open this publication in new window or tab >>Semantic technologies for detecting names of new drugs on darknets
2016 (English)In: Proc. 4th International Conference on Cybercrime and Computer Forensics, IEEE, 2016Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-314826 (URN)10.1109/ICCCF.2016.7740426 (DOI)000390123800006 ()978-1-5090-6096-2 (ISBN)
Conference
ICCCF 2016, June 12–14, Vancouver, Canada
Available from: 2016-11-17 Created: 2017-02-06 Last updated: 2018-01-13Bibliographically approved
Ashcroft, M., Kaati, L. & Meyer, M. (2015). A step towards detecting online grooming: Identifying adults pretending to be children. In: Proc. 5th European Intelligence and Security Informatics Conference: . Paper presented at EISIC 2015, September 7–9, Manchester, UK (pp. 98-104). IEEE Computer Society.
Open this publication in new window or tab >>A step towards detecting online grooming: Identifying adults pretending to be children
2015 (English)In: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, 98-104 p.Conference paper, Published paper (Refereed)
Abstract [en]

Online grooming is a major problem in todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this paper we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. The approach involves a two step process wherein authors are first classified as being children or adults, and then each child is being examined and false children distinguished from genuine children. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy. In this paper we will discuss the accuracy of the methods proposed, as well as the features that were important in their success. We believe that this work is an important step towards automated analysis of chat room conversation to detect and possible attempts of grooming. Our approach where we use text analysis to distinguish adults who are pretending to be children from actual children could be used to inform children about the true age of the person that they are communicating. This would be a step towards making the Internet more secure for young children and eliminate grooming.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-272202 (URN)10.1109/EISIC.2015.41 (DOI)000380550100014 ()9781479986514 (ISBN)
External cooperation:
Conference
EISIC 2015, September 7–9, Manchester, UK
Available from: 2015-09-09 Created: 2016-01-12 Last updated: 2018-01-10Bibliographically approved
Ashcroft, M., Kaati, L. & Meyer, M. (2015). Are You Really a Child?: A Machine Learning Approach To Protect Children from Online Grooming. In: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015. Paper presented at TAMSEC 2015, November 24–25, Kista, Sweden. .
Open this publication in new window or tab >>Are You Really a Child?: A Machine Learning Approach To Protect Children from Online Grooming
2015 (English)In: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015, 2015Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Online grooming and sexual abuse of children is a major threat towards the security of todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this work we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-272244 (URN)
Conference
TAMSEC 2015, November 24–25, Kista, Sweden
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2018-01-10Bibliographically approved
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