Emotions and Message Sharing on the Chinese Microblog
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33104
Emotions and Message Sharing on the Chinese Microblog

Authors: Yungeng Xie, Cong Liu, Yi Liu, Xuanao Wan

Abstract:

The study aims to explore microblog users’ emotion expression and sharing behaviors on the Chinese microblog (Weibo). The first theme of study analyzed whether microblog emotions impact readers’ message sharing behaviors, specifically, how the strength of emotion (positive and negative) in microblog messages facilitate/inhibit readers’ sharing behaviors. The second theme compared the differences among the three types of microblog users (i.e., verified enterprise users, verified individual users and unverified users) in terms of their profiles and microblog behaviors. A total of 7114 microblog messages about 24 hot public events in China were sampled from Sina Weibo. The first study results show that strength of negative emotions that microblog messages carry significantly increase the possibility of the message being shared. The second study results indicate that there are significant differences across the three types of users in terms of their emotion expression and its influence on microblog behaviors.

Keywords: Microblog, emotion expression, information diffusion.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130383

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 813

References:


[1] Bollen, J., Mao, H., & Zeng, X. (2010). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1-8.
[2] Bollen, J., Pepe, A., & Mao, H. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Proceedings of the Fifth International AAAI Conference on Weblogs and Social MediaPaper of AAAI Conference, 450-453.
[3] Cheung, S. Y., & Lam, E. T. (2005). An innovative shortened bilingual version of the Profile of Mood States (POMS-SBV). School Psychology International, 26, 121-128.
[4] Berger, J. (2011). Arousal increases social transmission of information. Psychological Science, 22, 891–893.
[5] Berger, J., & Milkman, K. (2012). What makes online content viral? Journal of Marketing Research, 49, 192–205.
[6] Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). An investigation of influentials and the role of sentiment in political communication on Twitter during election periods. Information, Communication, and Society, 16, 795-825.
[7] Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29, 217-247.
[8] Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter Events. Journal of the American Society for Information Science and Technology, 62, 406-418.
[9] Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of LREC Conference, 1320-1326.
[10] Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of Conference on Empirical Methods in Natural Language, 79-86.
[11] Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37, 267-307.
[12] Wilson, T., Wiebe, J., & Hoffman, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis, Computational Linguistics, 35, 399-433.