發(fā)布時(shí)間：2018-06-03 07:24

  本文選題：社會(huì)網(wǎng)絡(luò) + 數(shù)據(jù)采集��； 參考：《福州大學(xué)》2014年碩士論文

【摘要】：近年來,隨著WEB技術(shù)和移動(dòng)互聯(lián)網(wǎng)技術(shù)的發(fā)展,以微博為代表的在線社交網(wǎng)站成為了人們?nèi)粘＝涣�、娛樂的重要工具。在線社交網(wǎng)站作為社交媒體,不僅具有新聞傳播特性,也具有社交網(wǎng)絡(luò)特性,可以從新聞話題傳播、社會(huì)網(wǎng)絡(luò)結(jié)構(gòu)等多角度進(jìn)行分析,具有很好的研究價(jià)值。同時(shí),這些在線社交網(wǎng)站上用戶的互動(dòng)與交流,構(gòu)建出結(jié)構(gòu)復(fù)雜、數(shù)據(jù)規(guī)模龐大的社會(huì)網(wǎng)絡(luò),這給社會(huì)網(wǎng)絡(luò)數(shù)據(jù)分析和研究帶來了極大的挑戰(zhàn)。為了更好地進(jìn)行社會(huì)網(wǎng)絡(luò)數(shù)據(jù)研究,本文針對基于WEB的在線社交網(wǎng)站,構(gòu)建出社會(huì)網(wǎng)絡(luò)數(shù)據(jù)采集及分析平臺(tái),并通過該平臺(tái),以微博社會(huì)網(wǎng)絡(luò)為例,進(jìn)行微博傳播分析和微博用戶關(guān)系分析。本文的研究內(nèi)容主要有以下三個(gè)方面：首先,本文構(gòu)建出社會(huì)網(wǎng)絡(luò)數(shù)據(jù)采集及分析平臺(tái),提供在線社會(huì)網(wǎng)絡(luò)數(shù)據(jù)的采集及分析功能,為大規(guī)模社會(huì)網(wǎng)絡(luò)研究提供基礎(chǔ)支持。平臺(tái)整體采用分層的軟件架構(gòu)思想,保障系統(tǒng)的可擴(kuò)展性。在數(shù)據(jù)采集方面,平臺(tái)整合了不同的數(shù)據(jù)采集方式,同時(shí)針對不同數(shù)據(jù)類型分別進(jìn)行采集和數(shù)據(jù)存儲(chǔ)；在數(shù)據(jù)分析方面,平臺(tái)對不同社會(huì)網(wǎng)絡(luò)分析算法和可視化布局算法等進(jìn)行封裝,提供社會(huì)網(wǎng)絡(luò)數(shù)據(jù)的可視化分析功能。接著,本文運(yùn)用平臺(tái)的數(shù)據(jù)采集和可視化分析功能,進(jìn)行可視化微博傳播分析。首先本文對微博社會(huì)網(wǎng)絡(luò)中微博消息的傳播特點(diǎn)進(jìn)行分析,并利用平臺(tái)采集不同數(shù)據(jù)樣本進(jìn)行對比實(shí)驗(yàn),再運(yùn)用可視化分析技術(shù)對樣本中的微博消息傳播網(wǎng)絡(luò)進(jìn)行分析,并獲得微博在傳播過程中的各項(xiàng)特征的統(tǒng)計(jì)數(shù)據(jù),發(fā)現(xiàn)關(guān)鍵傳播帳號在微博傳播過程中起到的巨大作用。最后,基于平臺(tái)對大規(guī)模社會(huì)網(wǎng)絡(luò)數(shù)據(jù)的處理能力,本文對影響微博社會(huì)網(wǎng)絡(luò)用戶關(guān)系的相關(guān)特征進(jìn)行分析。首先本文分析了網(wǎng)絡(luò)結(jié)構(gòu)特征在微博社會(huì)網(wǎng)絡(luò)中的作用,同時(shí)引入微博屬性特征,構(gòu)造出基于隨機(jī)森林的鏈路預(yù)測模型。運(yùn)用該模型對平臺(tái)所采集的數(shù)據(jù)集進(jìn)行訓(xùn)練預(yù)測,通過比較引入微博屬性特征前后的預(yù)測性能以及特征的重要性分布,分析了各類特征對微博用戶關(guān)系形成的影響,揭示了除傳統(tǒng)的網(wǎng)絡(luò)結(jié)構(gòu)特征外,微博屬性特征對微博用戶關(guān)系的形成具有重要的影響力。
[Abstract]:In recent years, with the development of WEB technology and mobile Internet technology, the online social network represented by Weibo has become an important tool for people's daily communication and entertainment. As social media, online social network not only has the characteristics of news dissemination, but also has the characteristics of social network. It can be analyzed from news topic communication, social network structure and so on, which has good research value. At the same time, the interaction and exchange of users on these online social networking sites have created a complex social network with large data scale, which brings great challenges to the analysis and research of social network data. In order to study social network data better, this paper constructs a social network data collection and analysis platform for online social network based on WEB, and takes Weibo social network as an example. Weibo propagation analysis and Weibo user relationship analysis. The main contents of this paper are as follows: firstly, this paper constructs a social network data acquisition and analysis platform to provide online social network data collection and analysis function, providing basic support for large-scale social network research. The whole platform adopts the idea of layered software architecture to ensure the expansibility of the system. In the aspect of data acquisition, the platform integrates different ways of data acquisition, at the same time, it collects and stores data according to different data types, and in the aspect of data analysis, The platform encapsulates different social network analysis algorithms and visual layout algorithms to provide visual analysis function of social network data. Then, this paper uses the function of data acquisition and visual analysis of the platform to analyze the visual Weibo propagation. Firstly, this paper analyzes the characteristics of Weibo message transmission in Weibo social network, and uses the platform to collect different data samples to carry on the contrast experiment, and then uses the visual analysis technology to analyze the Weibo message dissemination network in the sample. The statistical data of the characteristics of Weibo in the process of communication are obtained, and the key communication account plays an important role in the process of Weibo propagation. Finally, based on the platform's ability to deal with large-scale social network data, this paper analyzes the related features that affect the user relationship of Weibo social network. Firstly, the function of network structure in Weibo social network is analyzed, and the Weibo attribute feature is introduced to construct a link prediction model based on stochastic forest. The model is used to train and predict the data set collected by the platform. By comparing the prediction performance before and after the introduction of the Weibo attribute feature and the importance distribution of the feature, the influence of various features on the formation of Weibo user relationship is analyzed. In addition to the traditional network structure features, the Weibo attribute features play an important role in the formation of Weibo user relationships.
【學(xué)位授予單位】：福州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP274.2;TP393.09

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