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An Application of Dimension Reduction for Intention Groups in Reddit
Linnaeus University, Faculty of Technology, Department of Computer Science.
Linnaeus University, Faculty of Technology, Department of Computer Science.
2016 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Reddit (www.reddit.com) is a social news platform for information sharing and exchanging. The amount of data, in terms of both observations and dimensions is enormous because a large number of users express all aspects of knowledge in their own lives by publishing the comments. While it’s easy for a human being to understand the Reddit comments on an individual basis, it is a tremendous challenge to use a computer and extract insights from it. In this thesis, we seek one algorithmic driven approach to analyze both the unique Reddit data structure and the relations inside owners of comments by their similar features. We explore the various types of communications between two people with common characteristics and build a special communication model that characterizes the potential relationship between two users via their communication messages. We then seek a dimensionality reduction methodology that can merge users with similar behavior into same groups. Along the process, we develop computer program to collect data, define attributes based on the communication model and apply a rule-based group merging algorithm. We then evaluate the results to show the effectiveness of this methodology. Our results show reasonable success in producing user groups that have recognizable group characteristics and share similar intentions.

Place, publisher, year, edition, pages
2016. , 41 p.
Keyword [en]
Reddit, communication model, dimension reduction, similarity metric
National Category
Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-56500OAI: oai:DiVA.org:lnu-56500DiVA: diva2:970602
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 60 credits
Supervisors
Examiners
Available from: 2016-09-19 Created: 2016-09-13 Last updated: 2016-09-19Bibliographically approved

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Master Thesis Report - Xuebo Sun and Yudan Wang(1291 kB)4 downloads
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File name FULLTEXT01.pdfFile size 1291 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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