本文選題：興趣 + 影響力��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：近年來(lái),互聯(lián)網(wǎng)的普及帶來(lái)了社交網(wǎng)絡(luò)應(yīng)用的蓬勃發(fā)展,在社交網(wǎng)絡(luò)平臺(tái)上,用戶可以采用多種方式進(jìn)行信息交互,隨時(shí)隨地了解最新信息,并參與到感興趣事件的討論中。這種新的信息交互模式極大地縮短了事件擴(kuò)散的時(shí)間,因此在擁有海量信息內(nèi)容和龐大的用戶群體的社交平臺(tái)上,對(duì)事件的轉(zhuǎn)發(fā)進(jìn)行預(yù)測(cè),具有重大實(shí)際意義。本文以新浪微博為研究對(duì)象,從內(nèi)容興趣特征和用戶影響力兩個(gè)方面對(duì)微博事件的轉(zhuǎn)發(fā)預(yù)測(cè)進(jìn)行了系統(tǒng)研究,其中的研究工作得到了國(guó)家自然科學(xué)基金項(xiàng)目(61172072)和北京市教育委員會(huì)研究生學(xué)科建設(shè)項(xiàng)目的支持,論文的主要內(nèi)容如下:研究了微博內(nèi)容興趣特征提取方法。本文通過(guò)新浪API采集微博數(shù)據(jù),針對(duì)微博短文本的特性,提出了兩種微博短文本興趣特征提取方案。方案一通過(guò)構(gòu)造LDA模型,進(jìn)行吉布森抽樣得到每條微博的興趣特征的分布概率;方案二通過(guò)改進(jìn)的短文本TF-IDF方法,根據(jù)詞權(quán)重得出每條微博的興趣特征。通過(guò)Perplexity指標(biāo)對(duì)兩種方案進(jìn)行比較分析,最后選用LDA模型進(jìn)行興趣特征提取。建立了基于興趣的微博用戶影響力計(jì)算模型。本文針對(duì)微博社交網(wǎng)絡(luò)的多樣性特點(diǎn),提出基于興趣的用戶影響力算法模型。該算法采用LDA模型提取微博內(nèi)容興趣特征,構(gòu)建特定興趣下用戶關(guān)系網(wǎng)絡(luò);在用戶興趣下進(jìn)行影響力計(jì)算時(shí),首次引入微博用戶流行率的概念,并把用戶間興趣相似度作為轉(zhuǎn)移概率,從而提高了影響力值計(jì)算精度。在實(shí)驗(yàn)階段,本文將模型與經(jīng)典微博用戶PageRank影響力算法進(jìn)行對(duì)比實(shí)驗(yàn)。通過(guò)比較Spearman等級(jí)相關(guān)系數(shù),論證了基于興趣的微博用戶影響力模型算法具有更高的準(zhǔn)確性。實(shí)現(xiàn)了基于微博內(nèi)容興趣特征和用戶影響力的微博轉(zhuǎn)發(fā)預(yù)測(cè)方法。本文對(duì)微博事件轉(zhuǎn)發(fā)預(yù)測(cè)原理進(jìn)行了研究,建立了 BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型,并對(duì)模型進(jìn)行了仿真實(shí)驗(yàn)。在采用50000條實(shí)驗(yàn)數(shù)據(jù)對(duì)仿真系統(tǒng)完成訓(xùn)練后,模型的實(shí)驗(yàn)預(yù)測(cè)結(jié)果準(zhǔn)確率可達(dá)85%。本文還通過(guò)引入兩個(gè)模型進(jìn)行對(duì)比實(shí)驗(yàn),借助ROC評(píng)價(jià)結(jié)果得到基于興趣特征和用戶影響力模型,能夠?qū)ξ⒉┺D(zhuǎn)發(fā)情況進(jìn)行有效預(yù)測(cè)的結(jié)論。
[Abstract]:In recent years, the popularity of the Internet has brought the vigorous development of social network applications. On the social network platform, users can exchange information in a variety of ways, learn the latest information anytime, anywhere, and participate in the discussion of interesting events.This new mode of information interaction greatly shortens the time of event diffusion, so it is of great practical significance to predict the event forwarding on the social platform with massive information content and huge user group.Taking Weibo of Sina as the research object, this paper makes a systematic study on the forwarding prediction of Weibo event from two aspects of content interest characteristics and user influence.The research work is supported by the National Natural Science Foundation of China 61172072) and the postgraduate subject construction project of Beijing Education Commission. The main contents of this paper are as follows: the extraction method of Weibo's content interest feature is studied.In this paper, Weibo data are collected by Sina API, and two kinds of interesting feature extraction schemes are put forward according to the characteristics of the short text of Weibo.Scheme 1, by constructing LDA model, carries out Gibson sampling to get the distribution probability of each Weibo's interest feature, and scheme 2 obtains the interest feature of each Weibo according to the word weight according to the improved short text TF-IDF method.The two schemes are compared and analyzed by Perplexity index. Finally, LDA model is used to extract the feature of interest.An interest-based model for calculating Weibo's user influence is established.According to the diversity of Weibo social network, this paper proposes an interest-based user influence algorithm model.The algorithm uses LDA model to extract the features of Weibo's content interest, constructs the user relationship network under specific interest, and introduces the concept of user popularity of Weibo for the first time when calculating the influence of user interest.The similarity of interest among users is taken as the transfer probability, which improves the accuracy of calculating the influence value.In the experiment stage, the model is compared with the classical Weibo user PageRank influence algorithm.By comparing the Spearman rank correlation coefficient, it is proved that Weibo user influence model algorithm based on interest has higher accuracy.Based on Weibo's content interest feature and user's influence, this paper realizes the Weibo forwarding prediction method.In this paper, the principle of Weibo event forwarding prediction is studied, the BP neural network prediction model is established, and the simulation experiment of the model is carried out.After training the simulation system with 50000 experimental data, the prediction accuracy of the model can reach 85%.This paper also introduces two models to carry on the contrast experiment, obtains based on the interest characteristic and the user influence model by the ROC appraisal result, can carry on the effective forecast to the Weibo forwarding situation.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.092

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