| 基于BP神经网络的微博转发量的预测 |
| 邓青1, 马晔风1, 刘艺2, 张辉1 |
| 1. 清华大学 工程物理系, 公共安全研究中心, 北京 100084; 2. 中国人民公安大学 治安学院, 北京 100038 |
| 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 |
摘要:
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| 摘要微博已经成为人们传播和获取信息的重要途径。突发事件相关微博的传播范围对事件的影响起着重要的作用,微博转发是扩大微博传播范围的一种重要方式。该文以城管与民众冲突事件(俗称“城管事件”)为例,将BP(back propagation)神经网络应用到该类事件相关微博的转发行为研究中,以实现该类突发事件下微博转发行为的影响因素分析和转发量的预测。该文先从发帖人和微博内容2个角度分析了突发事件下微博转发行为的影响因素, 结果表明: 1) 微博发帖人的活跃度、微博涉及主题标签、包含视频等可视化信息、提及其他用户及微博的发表时间段均对该微博的转发量有较大影响; 2) 与发帖人相比, 微博内容对其转发量的影响更大。在影响因素分析的基础上, 该文通过将预测问题转化为模式分类问题, 基于BP(back propagation)神经网络对突发事件下的微博转发量进行了预测, 并通过改变样本数对预测结果的稳定性进行了测试, 得到了有一定参考价值的预测结果。 | |||
| 关键词 ：微博,转发,BP(back propagation)神经网络,预测,影响因素,权重分析,应急响应 | |||
| 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. | |||
| Key words：Twitterretweetsback propagation (BP) neural networkpredictionfactorsweighted analysisemergency responses | |||
| 收稿日期: 2014-09-04 出版日期: 2016-01-12 | |||
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| 通讯作者:张辉,教授,E-mail:zhhui@mail.tsinghua.edu.cnE-mail: zhhui@mail.tsinghua.edu.cn | |||
| 引用本文: |
| 邓青, 马晔风, 刘艺, 张辉. 基于BP神经网络的微博转发量的预测[J]. 清华大学学报（自然科学版）, 2015, 55(12): 1342-1347. DENG Qing, MA Yefeng, LIU Yi, ZHANG Hui. Prediction of retweet counts by a back propagation neural network. Journal of Tsinghua University(Science and Technology), 2015, 55(12): 1342-1347. |
| 链接本文: |
| http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2015.24.012或 http://jst.tsinghuajournals.com/CN/Y2015/V55/I12/1342 |
图表:
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| 表1 影响因素及因变量信息表 |
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| 表2 经过处理后的微博数据的分类 |
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| 图1 BP神经网络结构图 |
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| 图2 各指标的权重分布 |
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| 表3 不同样本数的预测结果 |
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| 表4 不同样本数实验的平均值 |
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