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Trolldetektering: En undersökning i lämpligheten att använda ämnesmodellering och klustring för trolldetektion
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Troll Detection (English)
Abstract [sv]

Denna rapport syftar till att undersöka om ämnesmodellering och klustring kan användas till eller underlätta arbetet med trolldetektering.

De två ämnesmodellerna Latent Semantic Indexing (LSI) och Latent Dirichlet Allocation (LDA) används samt klustringsmetoden K-means. En grupp om tio användare som bedöms som troll undersöks följt av en undersökning av tio liknande användare för vart och en av de tio trollen.

Resultatet visar på att en relativt god mängd av av de relaterade användarna också kunde bedömas som troll. Klustringen kunde också avslöja en del mindre grupper varav några bestod av bottar.

Slutsatsen som dras är att ämnesmodellering och klustring tycks vara en god väg att gå men att ytterligare studier behövs.

Abstract [en]

This report aims to investigate if topic modeling and clustering can be used for or ease the work with troll detection.

The two topic models Latent Semantic Indexing (LSI) och Latent Dirichlet Allocation (LDA) as well as the clustering algorithm K-means are selected for this investigation. For each model, each troll in a group of ten trolls has ten related users extracted and then judged on whether they are trolls or not.

The results show that a relatively large amount of the related users also were judged as likely to be trolls. The clustering revealed a couple of small groups which seem to consist of a small network of bots.

The drawn conclusion is that topic modeling and clustering are seemingly a good choice for aiding in the detection of trolls but further studies are required.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186430OAI: oai:DiVA.org:kth-186430DiVA: diva2:927294
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2016-05-18 Created: 2016-05-11 Last updated: 2016-05-18Bibliographically approved

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fulltext(1135 kB)67 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf