Welcome to Francis Academic Press

Academic Journal of Humanities & Social Sciences, 2021, 4(7); doi: 10.25236/AJHSS.2021.040712.

Research on the Dissemination Process of Hot Words — Focus on the Connection between Netizens’ Emotions and the Search Volume of Hot Words


Fei Wang, Hiroshi Yokoi

Corresponding Author:
Fei Wang

Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan


The era of the popularization of the Internet has heralded an explosion of information. Various hot words appear frequently online, arousing public attention. The purpose of this study is to clarify the connection between netizens’ emotions and the search volume of hot words to explore their dissemination process. We randomly selected 10 hot words that were released by famous journals and events in China, and examined the dynamics of these words from January 1 to April 30, 2021. We obtained the search volume data of hot words from the data-sharing platform “Baidu Index,” and classified the search trend types into “segmented,” “steady,” and “bursty.” We collected the text information submitted by netizens related to these 10 words from the online social platform “Weibo” for emotion analysis, and compared the proportion of emotions with the search volume of hot words to analyze the correlation. This study concludes that there is a connection between netizens’ emotions and the search volume of hot words, and it is easier to detect prominent emotions in search trends with obvious fluctuations. This result helps us grasp the dissemination process of hot words, and simultaneously understand public opinion, which will help stabilize social development.


hot words, search trend types, emotions, dissemination process of hot words

Cite This Paper

Fei Wang, Hiroshi Yokoi. Research on the Dissemination Process of Hot Words — Focus on the Connection between Netizens’ Emotions and the Search Volume of Hot Words. Academic Journal of Humanities & Social Sciences (2021) Vol. 4, Issue 7: 61-69. https://doi.org/10.25236/AJHSS.2021.040712.


[1] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts (2011). Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11, p. 65. 

[2] H. Huang and X. Yuan (2018). Research on the communication mode of memetic semantic generalization of Internet hot words: take “cheer up someone (打’call’)” as an example. Public Communication of Science and Technology, no. 9, pp. 115 - 123. (in Chinese)

[3] Z. Wang and H. Yin (2014). The reasons and effects for the dissemination of Internet hot words. Chinese & Foreign Entrepreneurs, no. 30, p. 251. (in Chinese)

[4] F. Wang, W. Li, and H. Yokoi (2020). Research on the Connection Between Emotional Factors and the Dissemination Process of Hot Words. In: 2nd  International Conference on Arts, Humanity and Economics, Management, pp. 518 - 524.

[5] H. Cui and J. Kertész (2020). Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic. EPJ Data Sci., vol. 10, no. 1, p. 8.

[6] N. Naveed, T. Gottron, J. Kunegis, and A. C. Alhadi (2011). Bad news travel fast: a content-based analysis of interestingness on Twitter. In: Proceedings of the 3rd International Web Science Conference on - WebSci ’11, pp. 1 - 7. 

[7] K. Lee (2015). The dissemination of news on Twitter: what kind of news and who retweets this news. Media Communication, no. 65, pp. 63 - 75. (in Japanese)

[8] Sina Weibo, https://weibo.com/

[9] Baidu Index, https://index.baidu.com/

[10] Y. Zong (2014). The reason of the network hot words’ “hot” and its psychological attribution. Blooming Season, no. 14, p. 139. (in Chinese)

[11] S. Tsugawa and H. Ohsaki (2017). On the relation between message sentiment and its virality on social media. Soc. Netw. Anal. Min., vol. 7, no. 1, p. 19.

[12] Baidu, https://www.baidu.com/

[13] C. Zhao, Y. Yang, S. Wu, W. Wu, H. Xue, K. An, and Q. Zhen (2020). Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China. Sci Rep, vol. 10, no. 1, p. 5896.

[14] K. Zhang (2016). Big data analysis of scientific journals’ influence based on the Baidu index: taking National Medical Journal of China as an example. Chinese Journal of Scientific and Technical Periodicals, vol. 27, no. 7, pp. 779 - 784. (in Chinese)

[15] H. Adachi and M. Toda (2015). Study of emotional interactions among comments in SNS. Information Processing Society of Japan, vol. 2015 - NL - 220, no. 11, pp. 1 - 3. (in Japanese)

[16] R. Plutchik (1980). A general psychoevolutionary theory of emotion. Theories of emotion, pp. 3 - 33.

[17] P. Ekman (1992). An argument for basic emotions. Cognition and Emotion, vol. 6, no. 3 - 4, pp. 169 - 200.

[18] L. Xu, H. Lin, Y. Pan, H. Ren, and J. Chen (2008). Constructing the affective lexicon ontology. Journal of the China Society for Scientific and Technical Information, vol. 27, no.2, pp. 180 - 185. (in Chinese)