Social media brand engagement as a proxy for E-commerce activities: a case study of Sina Weibo and JD

Lin, Weiqiang, Saleiro, Pedro, Milic-Frayling, Natasa and Ch'ng, Eugene (2019) Social media brand engagement as a proxy for E-commerce activities: a case study of Sina Weibo and JD. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 3-6 Dec. 2018, Santiago, Chile.

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E-commerce platforms facilitate sales of products while product vendors engage in Social Media Activities (SMA) to drive E-commerce Platform Activities (EPA) of consumers, enticing them to search, browse and buy products. The frequency and timing of SMA are expected to affect levels of EPA, increasing the number of brand related queries, clickthrough, and purchase orders. This paper applies cross-sectional data analysis to explore such beliefs and demonstrates weak-to-moderate correlations between daily SMA and EPA volumes. Further correlation analysis, using 30-day rolling windows, shows a high variability in correlation of SMA-EPA pairs and calls into question the predictive potential of SMA in relation to EPA. Considering the moderate correlation of selected SMA and EPA pairs (e.g., Post-Orders), we investigate whether SMA features can predict changes in the EPA levels, instead of precise EPA daily volumes. We define such levels in terms of EPA distribution quantiles (2, 3, and 5 levels) over training data. We formulate the EPA quantile predictions as a multi-class categorization problem. The experiments with Random Forest and Logistic Regression show a varied success, performing better than random for the top quantiles of purchase orders and for the lowest quantile of search and clickthrough activities. Similar results are obtained when predicting multi-day cumulative EPA levels (1, 3, and 7 days). Our results have considerable practical implications but, most importantly, urge the common beliefs to be re-examined, seeking a stronger evidence of SMA effects on EPA.

Item Type: Conference or Workshop Item (Paper)
Keywords: Social Media Activities; E-commerce Platform Activities; Cross-sectional Data Analysis; Time Series; Multi-class Categorization; Quantile Level Prediction
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham Ningbo China > Faculty of Humanities and Social Sciences > School of International Communications
University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Wu, Cocoa
Date Deposited: 26 Feb 2019 02:44
Last Modified: 26 Feb 2019 02:44

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