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Automatic Detection of Cyberbullying on Social Media
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Bullying on social media is a dire problem for many youths, leading to severe health problems. In this thesis we describe the construction of a software prototype capable of automatically identifying bullying comments on the social media platform ASKfm using Natural Language Processing (NLP) and Machine Learning (ML) techniques. State of the art NLP and ML algorithms from previous research are studied and evaluated for the task of identifying bullying comments in a data set from ASKfm. The best performing classier acts as the core component in the detection software prototype. The resulting prototype can monitor selected proles on ASKfm in real time and display identied bullying comments connected to these proles on a web page.

Place, publisher, year, edition, pages
2016. , 63 p.
Series
UMNAD, ISSN 1081
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-128575OAI: oai:DiVA.org:umu-128575DiVA: diva2:1052691
External cooperation
Dohi
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2016-12-07 Created: 2016-12-07 Last updated: 2016-12-07Bibliographically approved

Open Access in DiVA

fulltext(548 kB)152 downloads
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File name FULLTEXT01.pdfFile size 548 kBChecksum SHA-512
48b99feecd8d97efb9b8a229d87566c003a31f724886dd56658ef7cd35a4a372fb0ed8f37b2548a0e02bffe14d1b2e8db91f977ae9375e2bb02c9e0c1aedddc3
Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • 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