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Kaati, Lisa
Publications (10 of 31) Show all publications
Shrestha, A., Kaati, L. & Cohen, K. (2017). A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities. In: Tjoa, AM Wagner, RR (Ed.), 2017 28th International Workshop on Database and Expert Systems Applications (DEXA): . Paper presented at 28th International Workshop on Database and Expert Systems Applications (DEXA), AUG 28-31, 2017, Lyon3 Univ, Lyon, FRANCE (pp. 1-5). IEEE
Open this publication in new window or tab >>A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities
2017 (English)In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) / [ed] Tjoa, AM Wagner, RR, IEEE, 2017, p. 1-5Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Workshop on Database and Expert Systems Applications-DEXA, ISSN 1529-4188
Keywords
Discussion forums, Support vector machines, Pragmatics, Manuals, Radio frequency, Electronic mail, Social network services
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-351187 (URN)10.1109/DEXA.2017.17 (DOI)000426078300001 ()978-1-5386-1051-0 (ISBN)
Conference
28th International Workshop on Database and Expert Systems Applications (DEXA), AUG 28-31, 2017, Lyon3 Univ, Lyon, FRANCE
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Isbister, T., Kaati, L. & Cohen, K. (2017). Gender Classification with Data Independent Features in Multiple Languages. In: Brynielsson, J (Ed.), 2017 European Intelligence and Security Informatics Conference (EISIC): . Paper presented at European Intelligence and Security Informatics Conference (EISIC), SEP 11-13, 2017, Athens, GREECE (pp. 54-60). IEEE
Open this publication in new window or tab >>Gender Classification with Data Independent Features in Multiple Languages
2017 (English)In: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, p. 54-60Conference paper, Published paper (Refereed)
Abstract [en]

Gender classification is a well-researched problem, and state-of-the-art implementations achieve an accuracy of over 85%. However, most previous work has focused on gender classification of texts written in the English language, and in many cases, the results cannot be transferred to different datasets since the features used to train the machine learning models are dependent on the data. In this work, we investigate the possibilities to classify the gender of an author on five different languages: English, Swedish, French, Spanish, and Russian. We use features of the word counting program Linguistic Inquiry and Word Count (LIWC) with the benefit that these features are independent of the dataset. Our results show that by using machine learning with features from LIWC, we can obtain an accuracy of 79% and 73% depending on the language. We also, show some interesting differences between the uses of certain categories among the genders in different languages.

Place, publisher, year, edition, pages
IEEE, 2017
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Keywords
Blogs, Pragmatics, Psychology, Social network services, Internet, Dictionaries, Machine learning
National Category
General Language Studies and Linguistics
Identifiers
urn:nbn:se:uu:diva-351175 (URN)10.1109/EISIC.2017.16 (DOI)000425928200007 ()978-1-5386-2385-5 (ISBN)
Conference
European Intelligence and Security Informatics Conference (EISIC), SEP 11-13, 2017, Athens, GREECE
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-24Bibliographically approved
Fernquist, J., Fängström, T. & Kaati, L. (2017). IoT Data Profiles: The Routines of Your Life Reveals Who You Are. In: Brynielsson, J (Ed.), 2017 European Intelligence and Security Informatics Conference (EISIC): . Paper presented at European Intelligence and Security Informatics Conference (EISIC), SEP 11-13, 2017, Athens, GREECE (pp. 61-67). IEEE
Open this publication in new window or tab >>IoT Data Profiles: The Routines of Your Life Reveals Who You Are
2017 (English)In: 2017 European Intelligence and Security Informatics Conference (EISIC) / [ed] Brynielsson, J, IEEE, 2017, p. 61-67Conference paper, Published paper (Refereed)
Abstract [en]

Preserving privacy is getting more and more important. The new EU general data protection regulation (GDPR) which will apply from May 2018 will introduce developments to some areas of EU data protection law and increase the privacy and personal integrity by strengthen and unify data protection for all individuals in EU. GDPR will most likely have an impact on many organizations and put pressure on many organizations that handle data. In this work, we investigate to what extent data profiles consisting of data from connected things can be used to identify a user. We use time and event profiles that can be created based on when, where and how a user communicates and uses digital devices. Our results show that such data profiles can be used to identify individuals and that collecting and creating data profiles of users can be seen as a serious threat towards privacy and personal integrity.

Place, publisher, year, edition, pages
IEEE, 2017
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Keywords
Social network services, Bluetooth, Cellular phones, Europe, Data protection, Automobiles
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-351176 (URN)10.1109/EISIC.2017.17 (DOI)000425928200008 ()978-1-5386-2385-5 (ISBN)
Conference
European Intelligence and Security Informatics Conference (EISIC), SEP 11-13, 2017, Athens, GREECE
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-24Bibliographically approved
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, p. 121-126Conference 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, p. 266-279Chapter 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, p. 1053-1060Conference 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, p. 1053-1060Conference 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, p. 85-90Conference 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, p. 77-84Conference 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
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