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A Machine Learning Approach Towards Detecting Extreme Adopters in Digital Communities
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. FOI, Stockholm, Sweden..
FOI, Stockholm, Sweden..
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. s. 1-5
Serie
International Workshop on Database and Expert Systems Applications-DEXA, ISSN 1529-4188
Emneord [en]
Discussion forums, Support vector machines, Pragmatics, Manuals, Radio frequency, Electronic mail, Social network services
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-351187DOI: 10.1109/DEXA.2017.17ISI: 000426078300001ISBN: 978-1-5386-1051-0 (digital)OAI: oai:DiVA.org:uu-351187DiVA, id: diva2:1209677
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
Inngår i avhandling
1. Techniques for analyzing digital environments from a security perspective
Åpne denne publikasjonen i ny fane eller vindu >>Techniques for analyzing digital environments from a security perspective
2019 (engelsk)Doktoravhandling, 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.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 64
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1786
Emneord
digital communities, machine learning, text analysis, linguistic features, linguistic analysis, warning behaviors, Internet, social media, extremism, terrorism, psychological state, author identification, alias matching
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:uu:diva-379605 (URN)978-91-513-0605-6 (ISBN)
Disputas
2019-05-17, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-04-24 Laget: 2019-03-22 Sist oppdatert: 2019-06-18

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