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dc.contributor.advisorHayes, Conor
dc.contributor.authorAumayr, Erik
dc.description.abstractIn a world where online users form communities for various purposes and around many different topics of interest, it has become of social and economic importance for owners, providers and managers of online communities to assess and possibly improve community success. However, the first problem arises when trying to measure success. Success means different things for different people, and moreover, the different purposes of communities determine the criteria under which success must be defined. For example, for assessing the success of a questions and answers (Q&A) community like Stack Overflow, one has to take into account how effectively questions are being solved. On the other hand, social connections among the participants are far less important in a Q&A site than for a social media site like Facebook. The first core contribution of this thesis is to define concrete success criteria for a number of communities. In particular, we focus on Q&A, Life & Health, and Knowledge Creation communities, which are distinct types of communities, each with their own purposes and goals. In order to achieve community success, it is vital to understand which user behaviour is indicative of, or contributes to, success. In the literature, many community features have been proposed as indicators for success, including aspects of user activity, participation, loyalty, and interconnectedness among the community members. However, these indicators have not been evaluated against tangible success criteria. Our second core contribution is to put together a collection of proposed community features, and to evaluate them against the success criteria we defined for the three types of communities in our study. We find that there are no universal success indicators, as the goals and purposes of the communities vary widely, and that it is therefore important to identify the appropriate user behaviour that maximizes success for different types of communities. Lastly, the third core contribution of this thesis is to identify successful user behaviour by studying the relation between user behaviour and community success, utilising prediction and simulation approaches. The prediction allows us to determine the best combinations of user behaviour for each community type, and the simulation enables us to represent user behaviour in a computational model in order to study community success in a controllable environment. This way, we are in control of the various conditions that a community can be subjected to, and observe how these change the community’s ability to fulfil its purpose beyond what is captured in recorded data, and without interfering with live communities. The practical application of our approach allows owners and managers of online communities to gauge the success of their communities, and identify the weak spots and potential remedies to increase success.en_IE
dc.publisherNUI Galway
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectonline communitiesen_IE
dc.subjectuser behaviouren_IE
dc.subjectmachine learningen_IE
dc.subjectagent-based simulationen_IE
dc.subjectData analyticsen_IE
dc.subjectInsight Centreen_IE
dc.subjectEngineering and Informaticsen_IE
dc.titleOnline community success - A study of success criteria and user behaviour in online communitiesen_IE
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderFP7 Information and Communication Technologiesen_IE
dc.local.noteThis work is a study of success of online communities. In this study, we propose an approach to define "success" based on the purpose of the community, and we use simulation and automatic detection of important user behaviour in order to find out which user behaviour is related to various definitions of success for different online communities. Our findings show that different user behaviour is important for the success of different types of online communities, there is no one-fits-all solution. Therefore, the appropriate definition of success and the corresponding user behaviour have to be investigated for each online community, using the methodology proposed in this thesis.en_IE
dcterms.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::ICT/257859/EU/Risk and Opportunity management of huge-scale BUSiness communiTy cooperation./ROBUSTen_IE
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Strategic Research Cluster/08/SRC/I1407/IE/SRC Clique: Graph & Network Analysis Cluster/

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland