uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Machine Learning for Affect Analysis on White Supremacy Forum
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired webforums. Here, one of their goals has been to detect and measure users' affects that are expressed in the forums as well as identify how users' affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this thesis, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manually annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to  analyse and measure the presence of different affects on hate forums.

Place, publisher, year, edition, pages
2016. , 32 p.
IT, 16058
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-301983OAI: oai:DiVA.org:uu-301983DiVA: diva2:955841
Educational program
Master Programme in Computer Science
Available from: 2016-08-26 Created: 2016-08-26 Last updated: 2016-08-26Bibliographically approved

Open Access in DiVA

fulltext(1689 kB)44 downloads
File information
File name FULLTEXT01.pdfFile size 1689 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 44 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 906 hits
ReferencesLink to record
Permanent link

Direct link