| PHYSICS AND ENGINEERING PHYSICS |
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Prediction of retweet counts by a back propagation neural network |
| DENG Qing1, MA Yefeng1, LIU Yi2, ZHANG Hui1 |
1. Center for Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Pubic Order School, People's Public Security University of China, Beijing 100038, China |
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Abstract Twitter has become a major platform for expressing and gathering information to change people's opinions and lives. Retweets are a key mechanism for information diffusion. The retweet mechanism can be a useful method to guide public opinion and contribute to emergency responses. This paper considers a case study of the conflicts between urban management officials (known as Chengguan in China) and the public. This study focused on factor analysis and prediction of a tweet's popularity based on a back propagation (BP) neural network during a crisis. The weighted analysis of various factors from the perspectives of the posters and the content of the microblog messages shows how some factors, including the user's activity, hashtag, visual information, mentioning others and posting time, influences a message's popularity. The results show that followers are more attracted by a tweet's content rather than its poster. The prediction problem is changed into a pattern classification problem to predict the retweet count using a back propagation (BP) neural network. The stability of the results was tested by changing the number of samples.
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| Keywords
Twitter
retweets
back propagation (BP) neural network
prediction
factors
weighted analysis
emergency responses
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Issue Date: 15 December 2015
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2015,
Vol. 55
