智慧型手機使用者社群資訊行為探勘

Exploring Smartphone Users' Social Information Behavior

楊絜雲(JIE-YUN YANG)

指導教授 ： 蔡坤宏 ； 江義平

國立臺北大學/商學院/資訊管理研究所/碩士(2016年)

摘要

近年來上網已成為現代人不可或缺的行為，而社群網路服務的興起，使得社群網站瀏覽行為也成為人們重要的資訊行為之一。因此本研究以社群網路服務網站做為研究核心，透過蒐集連續3個月智慧型手機使用者的點擊流資料(clickstream data)來進行網路使用者社群資訊行為探勘研究，目的為探討社群類別與各主要類別(包括:新聞、購物、論壇、線上視頻、部落格)網站之間的關聯性。資料分析以取得之點擊流資料後，以使用者為中心(user-centric)以及網站為中心(site-centric)兩種取向進行分析，首先user-centric以相關分析確定與社群類別App/Web有所相關之其他類別App/Web，再以其結果透過固定效果模式探討24小時中性別、年齡以及週間/末對於瀏覽量之影響；site-centric以對應分析顯示各類別App/Web之間的相對分佈狀況，接著利用關聯規則以及Jaccard Index計算出各類別App/Web與社群類別App/Web同時出現之機率。最後透過分析結果針對各社群經營者、App/Web經營者、行銷人員、開發商以及研究者提出建議與後續研究之參考。

關鍵字

網路探勘 ； 社群資訊行為 ； 智慧型手機 ； 點擊流資料 ； 行動商務

並列摘要

Surfing the web has become most important and indispensable behavior recently. With the surge of social network services (SNSs), browsing the SNSs has become one of the main sources of people’s information behavior. This study will use social network sites as the core sites to analyze smartphone users’ social information behavior, by collecting three-month period of users’ clickstream data, in order to find out users’ online behavior between SNSs and other sites include news, shopping, search, forum, video and blog. First, user-centric approach will be conducted for correlation analysis to quantify the association between SNSs and other categories, and then use the results as the standard to conduct fixed effect model by exploring the duration of gender, age and weekdays/weekend at 24hrs. Second, site-centric approach will conduct correspondence analysis and association rules by using users' session to gather all categories in the graph and explore the relationship between the SNSs and the other categories. Finally, based on the findings, this study to provide a stepping stone for marketers and researchers to understand web users’ social information behavior.

並列關鍵字

Clickstream Data ； Web Usage Mining ； Social Information Behavior ； Smartphone ； Mobile Commerce

參考文獻

Huang, C. Y., Shen, Y. C., Chiang, I., & Lin, C. S. (2007). Characterizing Web users' online information behavior. Journal of the American society for information science and technology, 58(13), 1988-1997.

Bucklin, R. E., & Sismeiro, C. (2009). Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing. Journal of Interactive Marketing, 23(1), 35-48.

Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories. Decision support systems, 42(3), 1872-1888.

Chou, P. H., Li, P. H., Chen, K. K., & Wu, M. J. (2010). Integrating web mining and neural network for personalized e-commerce automatic service. Expert Systems with Applications, 37(4), 2898-2910.

Clausen, S.E. (1998). Applied correspondence analysis: An Introduction.(1st ed.). New York, USA: Sage.