【摘要】：微博作為社交媒體的一種形式,具有信息傳播快、來源廣和多角度等特征,已經(jīng)成為人們?nèi)粘Ｐ畔⒔涣骱头窒淼闹饕?吸引了國內(nèi)外學者的廣泛關(guān)注。研究用戶影響力對于微博的用戶推薦、信息擴散、輿情監(jiān)測和定向營銷等具有重要的意義。首先,通過分析微博消息傳播機制,構(gòu)建了微博網(wǎng)絡(luò)模型,并把微博網(wǎng)絡(luò)細分為兩個網(wǎng)絡(luò):用戶關(guān)系網(wǎng)絡(luò)和博文傳播網(wǎng)絡(luò)。然后,結(jié)合新浪微博的特征,為了有效地避免“僵尸粉”的影響,防止用戶采用對博文的自我轉(zhuǎn)發(fā)、自我評論等操作來惡意提升自己的影響力,從用戶關(guān)系網(wǎng)絡(luò)和博文傳播網(wǎng)絡(luò)兩個角度,定義了四個評價用戶影響力的指標:LeaderRank影響力、博文平均被轉(zhuǎn)發(fā)數(shù)、博文平均被評論數(shù)和博文平均被贊數(shù)。在此基礎(chǔ)上,為避免給不同指標確定合適的權(quán)重參數(shù),引入了多目標決策中經(jīng)典的Skyline計算方法,提出了WeiboLeaderRank影響力評價算法,并分析了該算法的特點。為了驗證算法的有效性,使用網(wǎng)絡(luò)爬蟲技術(shù),設(shè)計并實現(xiàn)了新浪微博數(shù)據(jù)采集系統(tǒng),建立了包含125207個用戶的微博研究數(shù)據(jù)集。由于微博服務(wù)器檢測到異常的訪問請求時,會采取重定向訪問請求或禁止用戶訪問等措施,這會嚴重影響采集的速度。為解決這一問題,采用了多賬號模擬登陸,一個賬號開啟一個線程,多線程同時采集的方法。線程使用匿名代理服務(wù)器請求數(shù)據(jù),并動態(tài)改變請求HTTP頭部信息,同時加入異常檢測模塊,及時發(fā)現(xiàn)異常情況并采取相應(yīng)的操作,盡量模仿正常的用戶訪問行為,提高采集效率。最后在采集的數(shù)據(jù)集上進行實驗,驗證了四個影響力評價指標的有效性,并把WeiboLeaderRank算法和其他常用的用戶影響力算法進行比較,結(jié)果表明WeiboLeaderRank算法評價效果更好,并且計算時間是隨著數(shù)據(jù)量地增長而線性增加的,算法能適應(yīng)超大規(guī)模的真實微博環(huán)境,同時具有較好的實時性。
[Abstract]:As a form of social media, Weibo has the characteristics of fast information dissemination, wide sources and multiple angles. It has become the main channel for people to exchange and share information on a daily basis, and has attracted wide attention of scholars at home and abroad. The study of user influence is of great significance for Weibo user recommendation, information diffusion, public opinion monitoring and targeted marketing. Firstly, by analyzing the mechanism of Weibo message propagation, the Weibo network model is constructed, and the Weibo network is subdivided into two networks: the user relationship network and the blog transmission network. Then, according to the features of Sina Weibo, in order to effectively avoid the influence of "zombie powder" and prevent users from using self-forwarding, self-comment and other operations to increase their influence maliciously. From the perspective of user relationship network and blog post communication network, this paper defines four indexes to evaluate the influence of users: LeaderRank influence, the average number of posts being forwarded, the average number of comments and the average number of likes of blog posts. On this basis, in order to avoid determining appropriate weight parameters for different indexes, the classical Skyline calculation method in multi-objective decision making is introduced, and Weibo LeaderRank influence evaluation algorithm is proposed, and the characteristics of the algorithm are analyzed. In order to verify the validity of the algorithm, a Sina Weibo data acquisition system is designed and implemented by using web crawler technology, and a Weibo research data set including 125,207 users is established. When the Weibo server detects an abnormal access request, it will take measures such as redirecting the access request or prohibiting the user from accessing the request, which will seriously affect the speed of the acquisition. In order to solve this problem, multi-account simulation login, one account opened a thread, multi-thread at the same time. The thread uses anonymous proxy server to request data and dynamically changes the request HTTP header information. At the same time, the thread adds anomaly detection module, finds the abnormal situation in time and takes appropriate actions to imitate the normal user access behavior as far as possible. Improve the efficiency of collection. Finally, experiments are carried out on the collected data sets to verify the effectiveness of the four impact evaluation indexes. The Weibo LeaderRank algorithm is compared with other commonly used user influence algorithms. The results show that the Weibo LeaderRank algorithm is more effective than the Weibo LeaderRank algorithm. The computation time increases linearly with the increase of data volume. The algorithm can adapt to the large scale real Weibo environment and has good real-time performance.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP393.09

【引證文獻】

相關(guān)碩士學位論文 前1條

1 趙倩;基于社區(qū)結(jié)構(gòu)的Top-K影響力節(jié)點發(fā)現(xiàn)算法研究[D];華中科技大學;2015年

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