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Classification of Quantitative Methods for Analyzing Unobtrusive Data in Online Community Research

Student: Aleksandra Briukhno

Supervisor: Aigul M. Klimova

Faculty: Faculty of Social Sciences

Educational Programme: Complex Social Analysis (Master)

Year of Graduation: 2019

The Internet provides numerous opportunities for its users to form groups of interest: social networks, forums, chat rooms — so-called virtual (a.k.a. online) communities. Most of the interactions in online communities are either automatically documented as “digital traces”, such as likes and reposts, or these interactions themselves are publicly available written messages. Thus, almost every interaction on the Internet is recorded and can be extracted for research purposes, opening up a wide range of possibilities for the use of various quantitative methods. The purpose of this work is to develop a classification of quantitative methods for the analysis of unobtrusive data in online community research. The paper is structured as follows: the first chapter discusses the main conceptualizations of the term “online community”, and a working definition of this term is proposed; the second chapter provides a classification of quantitative methods for the analysis of unobtrusive data; in the third chapter, the practical application of these methods is considered and the local online community is analyzed. Social network analysis and content analysis are used.

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