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Detecting multipliers of jihadism on twitter
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. (Security)
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. (Security)
ICSR, London, England.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. (Security)
2015 (Engelska)Ingår i: Proc. 15th ICDM Workshops, IEEE Computer Society, 2015, s. 954-960Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2015. s. 954-960
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:uu:diva-272243DOI: 10.1109/ICDMW.2015.9ISI: 000380556700127ISBN: 9781467384926 (tryckt)OAI: oai:DiVA.org:uu-272243DiVA, id: diva2:893574
Konferens
ICDM Workshop on Intelligence and Security Informatics, ISI-ICDM 2015, November 14, Atlantic City, NJ
Tillgänglig från: 2015-11-14 Skapad: 2016-01-12 Senast uppdaterad: 2019-03-22Bibliografiskt granskad
Ingår i avhandling
1. Techniques for analyzing digital environments from a security perspective
Öppna denna publikation i ny flik eller fönster >>Techniques for analyzing digital environments from a security perspective
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
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
Nyckelord
digital communities, machine learning, text analysis, linguistic features, linguistic analysis, warning behaviors, Internet, social media, extremism, terrorism, psychological state, author identification, alias matching
Nationell ämneskategori
Teknik och teknologier
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:uu:diva-379605 (URN)978-91-513-0605-6 (ISBN)
Disputation
2019-05-17, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-04-24 Skapad: 2019-03-22 Senast uppdaterad: 2019-06-18

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