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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (2): 32-42    DOI: 10.11925/infotech.2096-3467.2020.1027
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Identifying Leaders and Dissemination Paths of Public Opinion
Xu Yabin1,2(),Sun Qiutian2
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China
2School of Computer, Beijing Information Science and Technology University, Beijing 100101, China
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[Objective] This study proposes new method to monitor social media, aiming to limit or guide the spread of public opinion. [Methods] First, we constructed an OLMT model to identify opinion leaders based on the dissemination force and topological potential. Then, we modified the Transformer model to build a social media behavior prediction model (MF-Transformer) with high parallelism and attention mechanism. [Results] The proposed models identified opinion leaders and their retweeting behaviors, as well as the main dissemination paths of online public opinion. The recall and accuracy of the predicted results were 92.17% and 99.07%, respectively, which were higher than those of the existing methods. [Limitations] We only examined our new models with data from Sina Weibo. [Conclusions] The proposed models could effectively identify online opinion leaders, as well as predict the dissemination paths of their comments and retweets.

Key wordsPublic Opinion      Opinion Leader      Social Network Behavior Prediction      Dissemination Paths Identification     
Received: 21 October 2020      Published: 24 November 2020
ZTFLH:  TP393  
Fund:National Natural Science Foundation of China(61672101);Open Project of Beijing Key Laboratory of Network Culture and Digital Communication(ICDDXN004);Open Project of Key Laboratory of Information Network Security of Ministry of Public Security(C18601)
Corresponding Authors: Xu Yabin ORCID:0000-0003-2727-3773     E-mail:

Cite this article:

Xu Yabin, Sun Qiutian. Identifying Leaders and Dissemination Paths of Public Opinion. Data Analysis and Knowledge Discovery, 2021, 5(2): 32-42.

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Framework of Propagation Behavior Prediction Model Based on MF-Transformer
Implementation Mechanism of Self-Attention
名称 含义 获取方式
userfols 用户粉丝数 网络爬虫技术爬取用户的基本资料
userinfl 用户影响力 通过意见领袖挖掘算法OLMT
usercati 用户活跃度 原创微博数、转发微博数、评论微博数加权求和
proauth 上游用户的认证情况 爬取用户的认证情况,认证用户记做1,非认证用户记做0
averretw 上游用户的平均被转发次数 上游用户历史微博被转发次数之和与历史微博条数的比值
commmoti 用户的传播积极性 用户最近转发的微博数量与发布微博的总数量的比值
textleng 文本长度 计算需预测被转发与否的微博文本长度
whetherpict 是否包含图片 通过微博文本判断是否包含图片,包含记做1,不包含记做0
whetherurl 是否包含URL 通过微博文本判断是否包含URL,包含记做1,不包含记做0
publishtime 文本发布时间段 爬取文本发布的时间,如5:23记做5
publishacti 发布时间段用户的活跃度 发布时间段内的原创微博数、转发微博数、评论微博数加权求和
textinte 用户对微博文本的兴趣度 计算方法见4.2节
forwardfreq 对上游用户的转发频率 用户转发上游用户的微博数与用户转发的微博总数的比值
transpower 传播源的传播力 计算方法见公式(2)
intersimi 与上游用户的兴趣相似度 计算方法见4.2节
Transmission Behavior Characteristics and Acquisition Methods
Accuracy of Different Mining Algorithms
Coverage of Different Mining Algorithms
Performance of Propagation Prediction Models
The Relationship Between Path Length and Path Number with Covering Ability
Forecasting Result of Public Opinion Communication
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