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Anomaly detection on social media using ARIMA models
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis explores whether it is possible to capture communication patterns from web-forums and detect anomalous user behaviour. Data from individuals on web-forums can be downloaded using web-crawlers, and tools as LIWC can make the data meaningful. If user data can be distinguished from white noise, statistical models such as ARIMA can be parametrized to identify the underlying structure and forecast data. It turned out that if enough data is captured, ARIMA models could suggest underlying patterns, therefore anomalous data can be identified. The anomalous data might suggest a change in the users' behaviour.

Place, publisher, year, edition, pages
2015. , 38 p.
Series
IT, 15077
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-269189OAI: oai:DiVA.org:uu-269189DiVA: diva2:882392
Supervisors
Examiners
Available from: 2015-12-14 Created: 2015-12-14 Last updated: 2015-12-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
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Output format
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