TY - GEN
T1 - Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns
AU - Liu, Guannan
AU - Fu, Yanjie
AU - Xu, Tong
AU - Xiong, Hui
AU - Chen, Guoqing
N1 - Publisher Copyright: © 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84936952276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936952276&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2014.48
DO - 10.1109/ICDM.2014.48
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 905
EP - 910
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
Y2 - 14 December 2014 through 17 December 2014
ER -