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Cluster Computing
Erschienen in: Cluster Computing 4/2016

01.12.2016

Object-based dynamic influence measurement model (DIMM) using social data (on facebook)

verfasst von: Seoung-hyun Koh, Yen-yoo You, Do-sung Na

Erschienen in: Cluster Computing | Ausgabe 4/2016

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Abstract

Due to the explosive growth of social network service resulting from the popularity of smart devices, online relations and activities are now affecting the behaviors of many people. On that account, the interest and importance of social network activities on Internet continue to grow. This study defines the social data with the following four factors: object, user, direction and distance. Moreover, this study quantifies the structured data such as number of responses and number of friends and the unstructured data such as difference between cause time and response time, preference and response type in relation to the object based activities of social network service (SNS) users in terms of time axis. This study then proposes the model to measure the influence direction and influence strength (or distance). In addition, this study models and explains the process regarding the system to collect and analyze the data for influence measurement and also the influence measurement technique using the sample data collected on facebook.
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Metadaten
Titel
Object-based dynamic influence measurement model (DIMM) using social data (on facebook)
verfasst von
Seoung-hyun Koh
Yen-yoo You
Do-sung Na
Publikationsdatum
01.12.2016
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 4/2016
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-016-0668-4

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