【摘要】：微博信息轉(zhuǎn)發(fā)作為信息傳播研究的關(guān)鍵問題之一。預(yù)測信息的轉(zhuǎn)發(fā)概率及傳播趨勢在信息傳播、輿情監(jiān)控、產(chǎn)品推薦等方面具有重要的應(yīng)用價值�，F(xiàn)有研究主要基于網(wǎng)絡(luò)結(jié)構(gòu)及信息的歷史傳播規(guī)律預(yù)測信息的未來傳播趨勢,大多忽略了用戶間的個體差異。在基于用戶行為的轉(zhuǎn)發(fā)預(yù)測中還主要是站在信息發(fā)布者的角度研究信息的被轉(zhuǎn)發(fā)因素,較少研究用戶轉(zhuǎn)發(fā)信息的影響因素。本文主要以用戶為中心,站在信息接收者的角度,通過挖掘影響用戶轉(zhuǎn)發(fā)的主要因素并結(jié)合機器學(xué)習(xí)中分類算法進(jìn)行預(yù)測,主要工作如下:首先,根據(jù)實際問題需求通過微博平臺提供的API抓取研究所需要的數(shù)據(jù)集,包括用戶信息、微博信息、用戶關(guān)系信息和轉(zhuǎn)發(fā)關(guān)系信息等,并對數(shù)據(jù)集的特征及完整性進(jìn)行分析和描述,并結(jié)合實際特征的影響情況進(jìn)行選取。。然后,挖掘影響用戶轉(zhuǎn)發(fā)行為的重要因素,包括信息發(fā)布者特征、信息接收者特征及用戶間交互特征,通過挖掘特征與轉(zhuǎn)發(fā)之間的關(guān)系圖分析所選特征的特點及影響。最后,使用支持向量回歸、樸素貝葉斯及隨機森林三個分類算法并結(jié)合信息轉(zhuǎn)發(fā)的影響因素,對用戶是否轉(zhuǎn)發(fā)信息進(jìn)行預(yù)測,通過實驗對比結(jié)果選取最適合模擬網(wǎng)絡(luò)中真實轉(zhuǎn)發(fā)過程的分類算法。通過模型分析證實了挖掘用戶特征對信息轉(zhuǎn)發(fā)行為預(yù)測研究的必要性,運用誤分率得出不同因素影響信息轉(zhuǎn)發(fā)行為的重要程度。
[Abstract]:Weibo message forwarding is one of the key issues in the research of information dissemination. Forecasting the probability and trend of information forwarding has important application value in information dissemination, public opinion monitoring, product recommendation and so on. The existing research mainly based on the network structure and the information history dissemination law predicts the information future dissemination tendency, mostly ignores the individual difference between the user. In user behavior based forwarding prediction, we mainly study the factors of information being forwarded from the point of view of information publisher, and less study the influencing factors of user forwarding information. In this paper, user-centered, from the perspective of information receiver, by mining the main factors that affect the user forwarding and combining with the classification algorithm in machine learning, the main work is as follows: first, According to the demand of practical problems, the data set of the research is captured by API provided by Weibo platform, including user information, Weibo information, user relationship information and forwarding relation information, and the characteristics and integrality of the data set are analyzed and described. And combined with the actual characteristics of the impact of the selection. Then, the important factors that affect the user's forwarding behavior are mined, including the information publisher feature, the information receiver feature and the user interaction feature, and the characteristics and effects of the selected features are analyzed by mining the relationship graph between the features and the forwarding. Finally, support vector regression, naive Bayes and random forest classification algorithms are used to predict whether the information is forwarded or not. By comparing the experimental results, the classification algorithm which is most suitable for simulating the real forwarding process in the network is selected. The necessity of researching the prediction of information forwarding behavior by mining user features is confirmed by model analysis, and the importance of different factors affecting information forwarding behavior is obtained by using the error rate.
【學(xué)位授予單位】：首都經(jīng)濟貿(mào)易大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G206

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