Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns
Social media has become one of the most popular marketing channels for many companies, which aims at maximizing their influence by various marketing campaigns conducted from their official accounts on social networks. However, most of these marketing accounts merely focus on the contents of their tweets. Less effort has been made on understanding tweeting time, which is a major contributing factor in terms of attracting customers' attention and maximizing the influence of a social marketing campaign. To that end, in this paper, we provide a focused study of temporal retweeting patterns and their influence on social media marketing campaigns. Specifically, we investigate the users' retweeting patterns by modeling their retweeting behaviors as a generative process, which considers temporal, social, and topical factors. Moreover, we validate the predictive power of the model on the dataset collected from Sina Weibo, the most popular micro blog platform in China. By discovering the temporal retweeting patterns, we analyze the temporal popular topics and recommend tweets to users in a time-aware manner. Finally, experimental results show that the proposed algorithm outperforms other baseline methods. This model is applicable for companies to conduct their marketing campaigns at the right time on social media.
G. Liu et al., "Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns," Proceedings of the 2014 IEEE International Conference on Data Mining (2014, Shenzhen, China), pp. 905 - 910, Institute of Electrical and Electronics Engineers (IEEE), Dec 2014.
The definitive version is available at https://doi.org/10.1109/ICDM.2014.48
2014 IEEE International Conference on Data Mining, ICDM 2014 (2014: Dec. 14-17, Shenzhen, China)
Keywords and Phrases
Commerce; Data mining; Economic and social effects; Social networking (online); Baseline methods; Contributing factor; Generative process; Marketing campaign; Marketing channels; Predictive power; Social marketings; Social media marketings; Marketing
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2014
The work was partly supported by the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities(12JJD630001), the National Natural Science Foundation of China (71110107027, 71028002), and Tsinghua University Initiative Scientific Research Program (20101081741).