TY - JOUR
T1 - Exploring Trends and Patterns of Popularity Stage Evolution in Social Media
AU - Kong, Qingchao
AU - Mao, Wenji
AU - Chen, Guandan
AU - Zeng, Daniel
N1 - Funding Information:
Manuscript received April 1, 2018; accepted June 28, 2018. Date of publication August 7, 2018; date of current version September 16, 2020. This work was supported in part by the Ministry of Science and Technology of China under Grant 2016QY02D0305, in part by the National Natural Science Foundation of China under Grant 71702181 and Grant 71621002, and in part by the Key Program of the Chinese Academy of Sciences under Grant ZDRW-XH-2017-3. This paper was recommended by Associate Editor E. Chen. (Corresponding author: Wenji Mao.) Q. Kong and G. Chen are with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (e-mail: qingchao.kong@ia.ac.cn; chenguandan2014@ia.ac.cn).
PY - 2020/10
Y1 - 2020/10
N2 - The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.
AB - The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.
KW - Online contents
KW - popularity evolution
KW - popularity stage prediction (PSP)
KW - social media analytics
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U2 - 10.1109/TSMC.2018.2855806
DO - 10.1109/TSMC.2018.2855806
M3 - Article
AN - SCOPUS:85051363504
VL - 50
SP - 3817
EP - 3827
JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
SN - 1083-4427
IS - 10
M1 - 8428538
ER -