【摘要】：微博是由Web2.0技術(shù)發(fā)展而來的新媒體產(chǎn)物,它是一類信息傳播與共享的社交網(wǎng)絡(luò)平臺。在日常生活中,話題的傳播、熱點事件的討論和網(wǎng)絡(luò)產(chǎn)品銷售越來越離不開微博了。近年來,國內(nèi)外的學(xué)者們開始研究微博,發(fā)表的文獻數(shù)量急劇增長,同時也吸引了很多其他領(lǐng)域的研究者加入進來。但是,現(xiàn)階段微博的研究還在發(fā)展階段,研究方法和內(nèi)容尚未成熟。由于微博的數(shù)據(jù)量日益增多和信息量急速膨脹,人們對微博信息的辨識能力也減弱。微博作為虛擬社區(qū)的代表,用戶之間的交流形成一個節(jié)點眾多和結(jié)構(gòu)復(fù)雜的復(fù)雜系統(tǒng)網(wǎng)絡(luò)。一般的網(wǎng)絡(luò)不能夠完全表示用戶間、話題間的關(guān)系。因此,需要建立超網(wǎng)絡(luò)來解決這種多種拓撲性質(zhì)的問題。這樣,既能完全刻畫出微博中兩類不同質(zhì)的點,又能直接美觀地呈現(xiàn)出來。粉絲作為一個特殊群體,常常瘋狂地?zé)釔勰硞�事物。而微博粉絲是網(wǎng)絡(luò)粉絲中的一種,如果他“關(guān)注”了這個微博主,就成為它的粉絲。一個微博主的粉絲越多,它發(fā)表的微博信息有可能被更多的人看到,即它的影響力越大。我們對粉絲的行為進行研究,可以提高企業(yè)的品牌形象和微博營銷,還可以讓企業(yè)知道用戶的產(chǎn)品體驗情況。目前,我國對粉絲行為的研究偏少,而對微博粉絲的研究就更少了。首先,本文對微博超網(wǎng)絡(luò)的研究現(xiàn)狀進行分析,并借鑒現(xiàn)有的超網(wǎng)絡(luò)模型提出了微博話題內(nèi)容子網(wǎng)絡(luò)、粉絲子網(wǎng)絡(luò)和面向粉絲興趣的企業(yè)微博超網(wǎng)絡(luò)模型三種網(wǎng)絡(luò)結(jié)構(gòu)。然后,對微博話題進行切詞分詞,每條微博信息提取出5個關(guān)鍵詞,將微博話題與關(guān)鍵詞建立邊的聯(lián)系。粉絲通過轉(zhuǎn)發(fā)或評論參與微博話題的討論,間接地與關(guān)鍵詞建立關(guān)系。最后,本文使用C++語言,搭建一個平臺框架來抓取新浪微博數(shù)據(jù)。數(shù)據(jù)選取的是中國移動官方微博的數(shù)據(jù)。同時,通過網(wǎng)絡(luò)模型構(gòu)建關(guān)鍵詞關(guān)系網(wǎng),并進行詞頻分析、中心性分析和凝聚子群分析來挖掘粉絲感興趣的核心內(nèi)容,驗證了本模型的有效性。
[Abstract]:Weibo is a new media product developed from Web2.0 technology. It is a kind of social network platform for information dissemination and sharing. In daily life, the spread of topics, hot issues and network product sales more and more inseparable from Weibo. In recent years, scholars at home and abroad began to study Weibo, published a rapid increase in the number of documents, but also attracted many other fields of researchers to join. However, at present, Weibo's research is still in the stage of development, research methods and content is not yet mature. Due to Weibo's increasing amount of data and rapid expansion of information, the ability of identifying Weibo information is also weakened. Weibo is the representative of virtual community, and the communication between users forms a complex system network with many nodes and complicated structure. General network can not completely express the relationship between users and topics. Therefore, it is necessary to establish a supernetwork to solve this problem of various topological properties. In this way, can completely depict two kinds of different points in Weibo, but also directly and aesthetically present. Fans, as a special group, often madly love something. Weibo fans are one of the online fans, if he "pay attention" to the Weibo Lord, become its fans. The more followers a Weibo has, the more likely it will be to see its message, the more influential it is, the more likely it is to be seen. We study the behavior of fans, which can improve the brand image of enterprises and Weibo marketing, but also let enterprises know the user's product experience. At present, there are few researches on fan behavior in China, and even less on Weibo fans. Firstly, this paper analyzes the current research situation of Weibo supernetwork, and puts forward three kinds of network structure, such as the topic content subnet of Weibo, the fan subnetwork and the enterprise Weibo supernetwork model oriented to fans' interest, using the existing supernetwork model for reference. Then, the topic of Weibo is divided into words, and five keywords are extracted from each piece of Weibo information, and then the topic is connected with the keyword. Fans through retweets or comments to participate in the discussion of Weibo topics, indirectly to establish a relationship with key words. Finally, this paper uses C language, build a platform framework to capture Sina Weibo data. The data is selected by China Mobile official Weibo data. At the same time, the keyword relationship network is constructed through the network model, and word frequency analysis, centrality analysis and condensed subgroup analysis are carried out to mine the core content of fans' interest, which verifies the validity of this model.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：G206;F713.55

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