The Transfer Model and Guidance Strategy of Netizens' Emotions

In the context of the COVID-19 pandemic, a large amount of information is gathered on Internet platforms, through which people express their opinions and vent their emotions. Emotional guidance of netizens has become an important part of social governance during turbulences caused by a so-called “infodemic”. This study focuses on the evolution and interaction of netizens' emotions after the occurrence of network public opinion events. First, the transfer model of netizens' emotions is constructed, and the significance of each parameter in the model is studied through simulation. Then, based on the model, we put forward the optimization method and quantitative method of guidance strategy of netizens' emotions. Finally, the empirical study proves the effectiveness of the model, which can provide a theoretical basis for the emotional guidance strategies after the outbreak of network public opinion events.

[1]  Allison S. Gabriel,et al.  Getting worse or getting better? Understanding the antecedents and consequences of emotion profile transitions during COVID-19-induced organizational crisis. , 2021, The Journal of applied psychology.

[2]  Srinivas Banothu,et al.  Emotion Extraction and Classification from Twitter Text , 2021, SSRN Electronic Journal.

[3]  Junwu Xu,et al.  Sentiment analysis of Chinese text based on Elmo-RNN model , 2021 .

[4]  Attila Kiss,et al.  Social media sentiment analysis based on COVID-19 , 2020, J. Inf. Telecommun..

[5]  Xin Lu,et al.  An Analysis of the Emotional Evolution of Large-Scale Internet Public Opinion Events Based on the BERT-LDA Hybrid Model , 2021, IEEE Access.

[6]  Michael G. Wheaton,et al.  Is Fear of COVID-19 Contagious? The Effects of Emotion Contagion and Social Media Use on Anxiety in Response to the Coronavirus Pandemic , 2021, Frontiers in Psychology.

[7]  J. Mélendez,et al.  Emotion recognition changes in a confinement situation due to COVID-19 , 2020, Journal of Environmental Psychology.

[8]  Robert J. Boncella,et al.  Emotional communication analysis of emergency microblog based on the evolution life cycle of public opinion , 2020 .

[9]  Xueyan Liu,et al.  Public Environment Emotion Prediction Model Using LSTM Network , 2020, Sustainability.

[10]  G. Panayiotou,et al.  Coping with the invisible enemy: The role of emotion regulation and awareness in quality of life during the COVID-19 pandemic , 2020 .

[11]  Liu Yang,et al.  Social Network Public Opinion Research Based on S-SEIR Epidemic Model , 2019, 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[12]  Peng Zhang,et al.  Research on netizens’ emotion evolution in emergency based on machine learning , 2019, Journal of Physics: Conference Series.

[13]  Runxi Zeng,et al.  A model and simulation of the emotional contagion of netizens in the process of rumor refutation , 2019, Scientific Reports.

[14]  Yanhong Meng,et al.  The Impact of Emotion on the Information Diffusion Model in Online Social Network , 2019, IOP Conference Series: Earth and Environmental Science.

[15]  Xin Jin,et al.  Research on Evolution Mechanism and Sentiment Analysis of Emergency Network Public Opinion , 2018 .

[16]  Zhigeng Pan,et al.  Using SIR Model to Simulate Emotion Contagion in Dynamic Crowd Aggregation Process , 2018 .

[17]  Chengzhi Zhang,et al.  Emotion evolutions of sub-topics about popular events on microblogs , 2017, Electron. Libr..

[18]  Pan Zhigeng,et al.  Dynamic Crowd Aggregation Simulation Using SIR Model Based Emotion Contagion , 2017, 2017 International Conference on Virtual Reality and Visualization (ICVRV).

[19]  Emilio Ferrara,et al.  Measuring Emotional Contagion in Social Media , 2015, PloS one.

[20]  Qiujun Lan,et al.  An Extended SISa Model for Sentiment Contagion , 2014 .

[21]  Jeffrey T. Hancock,et al.  Experimental evidence of massive-scale emotional contagion through social networks , 2014, Proceedings of the National Academy of Sciences.

[22]  Adam D. I. Kramer,et al.  Detecting Emotional Contagion in Massive Social Networks , 2014, PloS one.

[23]  Rada Mihalcea,et al.  Sentiment Analysis , 2014, Encyclopedia of Social Network Analysis and Mining.

[24]  K KirangeD.,et al.  EMOTION CLASSIFICATION OF NEWS HEADLINES USING SVM , 2013 .

[25]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[26]  David G. Rand,et al.  the high Himalayas and Antarctica Phylogeography of microbial phototrophs in the dry valleys of Supplementary data tml , 2010 .

[27]  Sigal G. Barsade.  The Ripple Effect: Emotional Contagion and its Influence on Group Behavior , 2002 .