微博影響力傳播模型的改進(jìn)與驗(yàn)證
發(fā)布時(shí)間：2019-01-30 09:29
【摘要】：微博,作為交流最方便、信息傳播最快捷的社交網(wǎng)絡(luò)平臺,已經(jīng)深入人們的日常生活中。普通網(wǎng)民、公眾人物、媒體、政府機(jī)構(gòu)、企業(yè)等各個(gè)領(lǐng)域的人們越來越習(xí)慣通過微博來發(fā)布或傳播最新消息。隨著社交網(wǎng)絡(luò)在研究領(lǐng)域的飛速發(fā)展,微博作為典型的社交網(wǎng)絡(luò),同樣引起了學(xué)者們的廣泛關(guān)注。本文從影響力傳播入手,重點(diǎn)研究了微博中的影響力傳播模型。 微博中影響力傳播主要通過信息傳播來體現(xiàn),因此分析信息傳播的影響因素對改進(jìn)影響力傳播模型具有重要意義。信息傳播的影響因素可以看作影響力傳播模型中節(jié)點(diǎn)激活概率的影響因素。通過對現(xiàn)有模型的深入學(xué)習(xí)和對社交網(wǎng)絡(luò)中信息傳播的影響因素的分析,本文提出了三個(gè)微博中的節(jié)點(diǎn)激活概率影響因素——博文特征、用戶間關(guān)系特征和用戶特征,并給出了相應(yīng)的計(jì)算方法,該方法稱為TRU (Tweets, Relationships and Users)節(jié)點(diǎn)激活概率計(jì)算方法。其中博文特征主要度量純文本、僅含URL鏈接的文本、僅含多媒體的文本和同時(shí)含URL、多媒體的文本四種形式的微博在傳播中的影響程度。用戶間關(guān)系特征從用戶間行為慣性和用戶間興趣相似度展開,分析轉(zhuǎn)發(fā)頻度和興趣相似度對信息傳播的影響。用戶特征主要通過用戶影響力度量該用戶在影響力傳播中的作用。在獨(dú)立級聯(lián)模型中引入TRU節(jié)點(diǎn)激活概率計(jì)算方法并改進(jìn)節(jié)點(diǎn)激活閾值計(jì)算方法的基礎(chǔ)上,本文提出適用于微博的ICTRU (TRU measurement based on Independent Cascade model)影響力傳播模型。 通過新浪的微博開放平臺提供的公開API,本文抓取了超過450萬用戶和其對應(yīng)的微博信息來驗(yàn)證ICTRU模型的有效性。通過對比ICTRU模型的模擬傳播圖和真實(shí)微博的傳播效果圖,同時(shí)分析兩者對應(yīng)的轉(zhuǎn)發(fā)樹數(shù)據(jù),證實(shí)了ICTRU模型可以有效的模擬微博中的影響力傳播情況。同時(shí)對比ICTRU模型和原始的獨(dú)立級聯(lián)模型的傳播情況,得出ICTRU模型可以更好的適用于微博中的影響力傳播。
[Abstract]:Weibo, as the most convenient social network platform for communication and information dissemination, has penetrated into people's daily life. Ordinary Internet users, public figures, the media, government agencies, enterprises and other areas of people more and more accustomed to Weibo to publish or disseminate the latest news. With the rapid development of social networks in the field of research, Weibo, as a typical social network, has attracted extensive attention of scholars. This article starts with the influence dissemination, and focuses on Weibo's influence communication model. Weibo's influence communication is mainly reflected by information communication, so it is important to analyze the influencing factors of information communication to improve the influence communication model. The influencing factors of information transmission can be regarded as the influence factors of node activation probability in the influence propagation model. Based on the in-depth study of the existing models and the analysis of the influencing factors of information transmission in social networks, this paper puts forward three influential factors of the activation probability of nodes in Weibo, namely, the features of blog posts, the features of the relationship between users and the characteristics of users. The corresponding calculation method is given, which is called TRU (Tweets, Relationships and Users) node activation probability calculation method. The features of blog articles mainly measure the influence of Weibo in the transmission of pure text, including only the text of URL link, the text of multimedia and the text of URL, multimedia at the same time. The relationship between users is based on the behavior inertia of users and the interest similarity between users. The influence of forwarding frequency and interest similarity on information transmission is analyzed. User characteristics measure the role of the user in the dissemination of influence through the influence of the user. Based on the introduction of TRU node activation probability calculation method and the improvement of node activation threshold calculation method in the independent cascade model, a ICTRU (TRU measurement based on Independent Cascade model) influence propagation model for Weibo is proposed in this paper. Through the open API, provided by Weibo of Sina this paper fetches more than 4.5 million users and its corresponding Weibo information to verify the validity of the ICTRU model. By comparing the simulated propagation map of ICTRU model with that of real Weibo, and analyzing the corresponding forwarding tree data, it is proved that the ICTRU model can effectively simulate the influence transmission in Weibo. At the same time, by comparing the ICTRU model with the original independent cascade model, it is concluded that the ICTRU model is more suitable for the influence transmission in Weibo.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092
[Abstract]:Weibo, as the most convenient social network platform for communication and information dissemination, has penetrated into people's daily life. Ordinary Internet users, public figures, the media, government agencies, enterprises and other areas of people more and more accustomed to Weibo to publish or disseminate the latest news. With the rapid development of social networks in the field of research, Weibo, as a typical social network, has attracted extensive attention of scholars. This article starts with the influence dissemination, and focuses on Weibo's influence communication model. Weibo's influence communication is mainly reflected by information communication, so it is important to analyze the influencing factors of information communication to improve the influence communication model. The influencing factors of information transmission can be regarded as the influence factors of node activation probability in the influence propagation model. Based on the in-depth study of the existing models and the analysis of the influencing factors of information transmission in social networks, this paper puts forward three influential factors of the activation probability of nodes in Weibo, namely, the features of blog posts, the features of the relationship between users and the characteristics of users. The corresponding calculation method is given, which is called TRU (Tweets, Relationships and Users) node activation probability calculation method. The features of blog articles mainly measure the influence of Weibo in the transmission of pure text, including only the text of URL link, the text of multimedia and the text of URL, multimedia at the same time. The relationship between users is based on the behavior inertia of users and the interest similarity between users. The influence of forwarding frequency and interest similarity on information transmission is analyzed. User characteristics measure the role of the user in the dissemination of influence through the influence of the user. Based on the introduction of TRU node activation probability calculation method and the improvement of node activation threshold calculation method in the independent cascade model, a ICTRU (TRU measurement based on Independent Cascade model) influence propagation model for Weibo is proposed in this paper. Through the open API, provided by Weibo of Sina this paper fetches more than 4.5 million users and its corresponding Weibo information to verify the validity of the ICTRU model. By comparing the simulated propagation map of ICTRU model with that of real Weibo, and analyzing the corresponding forwarding tree data, it is proved that the ICTRU model can effectively simulate the influence transmission in Weibo. At the same time, by comparing the ICTRU model with the original independent cascade model, it is concluded that the ICTRU model is more suitable for the influence transmission in Weibo.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092
【參考文獻(xiàn)】
相關(guān)期刊論文 前6條
1 張晨逸;孫建伶;丁軼群;;基于MB-LDA模型的微博主題挖掘[J];計(jì)算機(jī)研究與發(fā)展;2011年10期
2 冀進(jìn)朝;韓笑;王U，
本文編號：2417996
本文鏈接：http://sikaile.net/guanlilunwen/ydhl/2417996.html
最近更新
教材專著
