Using single source data to better understand User-generated Content (UGC) behavior

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages790-795
ISBN (Print)9781479958771
Publication statusPublished - 10 Oct 2014

Conference

Title2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
PlaceChina
CityBeijing
Period17 - 20 August 2014

Abstract

Single source refers to the unified measurement of different aspects of the same individual based on data from multiple sources. In the context of UGC, single source data can be used to study at least two important but as yet insufficiently investigated theoretical issues. First, single source data are ideal sources for studying inter-platform dynamics such as user migration across UGC platforms. Second, single source data can help to link individual self-reported cognitive factors with web crawled individual behavior logs, to achieve better understanding of individual behavior. In this paper, we select a random sample of Sina Blog users and collect their behavior information on both Sina Blog and Sina Weibo platforms; we also conduct an online survey to collect information about their cognitive factors. Merging all data together, we observe and quantify different behavior patterns of the same people across Blog and Weibo; we also identify alternative attractiveness and perceived popularity as significant drivers of one of the most important inter-platform dynamics - switching behavior.

Research Area(s)

  • inter-platform dynamics, single source data, UGC behavior, user motivations

Citation Format(s)

Using single source data to better understand User-generated Content (UGC) behavior. / Lu, Heng; Zhu, Jonathan J.H.

ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Institute of Electrical and Electronics Engineers Inc., 2014. p. 790-795 6921676.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review