本文關(guān)鍵詞：面向社會(huì)媒體的用戶在線社交圈識(shí)別與分析　出處：《哈爾濱工業(yè)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 用戶社交網(wǎng)絡(luò)分析 用戶強(qiáng)關(guān)系分析 用戶社交圈識(shí)別 用戶社交圈分析 用戶個(gè)人資料補(bǔ)全

【摘要】：社會(huì)媒體已經(jīng)成為人們?cè)诨ヂ?lián)網(wǎng)中信息傳播的重要平臺(tái),用戶在社會(huì)媒體中關(guān)注各種信息源并與好友進(jìn)行互動(dòng)。這些行為導(dǎo)致了社會(huì)媒體中形成了龐大的用戶社交網(wǎng)絡(luò),同時(shí),用戶線下大量的真實(shí)社會(huì)關(guān)系也存在于這種在線社交網(wǎng)絡(luò)中。在以往的社會(huì)科學(xué)中,有限的調(diào)研形式和稀缺的數(shù)據(jù)給人與人社會(huì)關(guān)系的研究造成了很大的困難。社會(huì)媒體易于獲取的數(shù)據(jù)使人與人之間社交強(qiáng)關(guān)系和社交圈的研究變成可能,這種線上熟人社交圈中的用戶具有非常高的同質(zhì)性,同一社交圈中的用戶會(huì)很大程度的影響彼此在社交網(wǎng)絡(luò)中的行為。因此,用戶在線社交圈的研究是社會(huì)媒體中用戶分析相關(guān)研究的重要基礎(chǔ)。每個(gè)用戶都在社會(huì)媒體中擁有大量的好友,用戶和這些好友之間都社交關(guān)系強(qiáng)度各不相同,并且很難加以區(qū)分。同時(shí),每個(gè)用戶在社會(huì)媒體中大多擁有不同的社交圈,這些社交圈互相獨(dú)立,代表用戶不同的社會(huì)關(guān)系,例如高中同學(xué)、大學(xué)同學(xué)等。用戶社交圈是用戶社會(huì)強(qiáng)關(guān)系的一種典型的表現(xiàn)形式,識(shí)別并分析這些社交圈可以反映用戶不同的社會(huì)維度,然而,社交圈具有很強(qiáng)的主觀性和私密性,每個(gè)用戶只能了解自己社交圈的好友構(gòu)成和社交圈的意義,因此研究人員很難在社會(huì)媒體中直接獲取一個(gè)用戶的社交圈好友和社交圈意義等相關(guān)數(shù)據(jù)。為了解決以上問題,本文從以下方面開展了對(duì)用戶在線社交圈識(shí)別和分析的相關(guān)研究。1、基于用戶關(guān)注關(guān)系的在線社交圈識(shí)別。每個(gè)社交圈都是用戶的社會(huì)強(qiáng)關(guān)系,因此每個(gè)社交圈內(nèi)部的成員之間都彼此連接緊密。根據(jù)這個(gè)原理,本文提出了一種基于在線凝聚聚類的用戶社交圈識(shí)別算法,并在用戶相似度計(jì)算中引入了用戶之間的社交屬性,可以準(zhǔn)確的識(shí)別用戶在社會(huì)媒體中的多個(gè)不同的社交圈。為了解決主觀性的社交圈數(shù)據(jù)難以獲取的問題,本文建立了激勵(lì)用戶標(biāo)注自己的社交圈成員的眾包平臺(tái),該方法有效的獲取了可供用戶社交圈相關(guān)研究的真實(shí)數(shù)據(jù)。2、基于用戶多維特征的在線社交圈識(shí)別。社交圈內(nèi)的成員不僅在網(wǎng)絡(luò)結(jié)構(gòu)上連接緊密,而且在個(gè)人資料、興趣愛好等方面具有很強(qiáng)的同質(zhì)性。現(xiàn)有的社交圈識(shí)別方法利用網(wǎng)絡(luò)結(jié)構(gòu)特征和個(gè)人資料特征都分別取得了很好的識(shí)別效果,然而這些方法很難把用戶在不同維度的特征結(jié)合起來。本文提出了基于矩陣分解的潛在因子聯(lián)合模型,模型可以通過學(xué)習(xí)用戶不同的維度特征得到特征融合后的用戶向量,實(shí)驗(yàn)證明,與利用用戶單一維度的特征模型相比,該模型通過融合多種用戶特征有效的提高了用戶社交圈識(shí)別的準(zhǔn)確率。3、基于多元線性回歸的用戶在線社交圈標(biāo)簽挖掘。作為用戶的社交強(qiáng)關(guān)系,每個(gè)社交圈都有各自不同的社會(huì)意義。每個(gè)社交圈成員在社會(huì)媒體中都有自己的標(biāo)簽,同一社交圈內(nèi)成員的一些共同標(biāo)簽可以代表這個(gè)社交圈的意義,然而社會(huì)媒體中用戶標(biāo)簽的稀少甚至缺失造成了用戶標(biāo)簽數(shù)據(jù)的缺失,進(jìn)一步給社交圈的標(biāo)簽挖掘帶來困難。本文提出了一種基于多元線性回歸的社交圈標(biāo)簽識(shí)別方法,同時(shí)融合和用戶標(biāo)簽本身的特征和社交圈內(nèi)的網(wǎng)絡(luò)結(jié)構(gòu)特征,為每個(gè)標(biāo)簽在社交圈內(nèi)計(jì)算一個(gè)權(quán)重,權(quán)重大的標(biāo)簽更可能作為社交圈的代表性標(biāo)簽。與相關(guān)方法相比,該方法解決了標(biāo)簽數(shù)據(jù)的稀疏問題,提升了社交圈標(biāo)簽的識(shí)別效果。4、基于用戶在線社交圈的用戶個(gè)人資料補(bǔ)全。用戶個(gè)人資料是用戶在社會(huì)媒體中的重要特征,大量的用戶個(gè)人資料缺失使用戶資料補(bǔ)全成為近年來的熱點(diǎn)研究方向。已有的用戶資料補(bǔ)全方法大多基于用戶文本,文本特性的變化和噪聲給這類方法帶來很大干擾。用戶社交圈是用戶社交強(qiáng)關(guān)系的體現(xiàn),不同的社交圈代表了用戶不同的社會(huì)維度�；谶@個(gè)原理,本文提出了基于非負(fù)矩陣分解模型的用戶個(gè)人資料補(bǔ)全方法,方法通過用戶的不同社交圈補(bǔ)全用戶不同社會(huì)維度的個(gè)人資料,保證了用戶個(gè)人資料的多樣性,相比已有方法提高了用戶個(gè)人資料補(bǔ)全的性能。綜上所述,本文開展了用戶在線社交圈識(shí)別與分析的一些相關(guān)研究工作。相關(guān)的技術(shù)適用于主流社會(huì)媒體的用戶社交強(qiáng)關(guān)系和社交圈的分析。在研究中取得了一些初步的結(jié)論和成果,希望能對(duì)社會(huì)媒體中用戶分析的相關(guān)工作有所裨益。
[Abstract]:Social media has become an important platform for people to communicate information in the Internet. Users pay attention to various sources of information in social media and interact with friends. These behaviors lead to the formation of huge user social networks in social media, and at the same time, a large number of real social relationships on the user line also exist in this online social network. In the past social sciences, limited research forms and scarce data have caused great difficulties in the study of human and human relations. Easy access to social media data in the study of social relations and strong social circle between people becomes possible, the online social circle of acquaintances in the user has very high homogeneity, will greatly influence the behavior of each other in a social network with a social circle of users. Therefore, the research of user online social circles is an important basis for the research of user analysis in social media. Every user has a large number of friends in the social media, and the intensity of social relationships between users and these friends is different and difficult to distinguish. At the same time, most users have different social circles in social media. These social circles are independent of each other, representing different social relationships of users, such as high school classmates, college students, etc. Users social circle is a typical form of user strong social performance, recognition and analysis of the social circle can reflect the different users of the social dimension, however, social circle is very subjective and privacy, each user can only understand their own social circle of friends and social circle, so it is difficult for researchers to in social media in direct access to a user's social circle of friends and social circle significance and related data. In order to solve the above problems, this paper has carried out the following research on the identification and analysis of user online social circles. 1. Online social circle recognition based on user concerns. Each social circle is a strong social relationship of the user, so the members of each social circle are connected to each other. According to this principle, a user social circle recognition algorithm based on online agglomerative clustering is proposed in this paper, and the social attributes between users are introduced into user similarity computation, which can accurately identify users in different social circles in social media. In order to solve the problem that the social circle data is difficult to obtain, a crowdsourcing platform is established to encourage users to annotate their social circle members. This method effectively gets the real data that can be related to users' social circles. 2. Online social circle recognition based on user multidimensional features. Members of the social circle are not only closely connected to the network structure, but also have strong homogeneity in personal data, interests, and so on. The existing social circle recognition methods have achieved good recognition results by using network structure and personal data characteristics. However, these methods are difficult to integrate users' characteristics in different dimensions. This paper presents a joint model of latent factors based on matrix decomposition, the model can be obtained by learning the characteristics of the user different user dimension vector, after feature fusion experiments show that compared with the characteristics of user model using single dimension, the model through the integration of a variety of user features can improve the accuracy of user circle recognition. 3. User online social circle label mining based on multiple linear regression. As a user's social relationship, each social circle has its own social significance. Each circle members have their own label in social media, some common label the same social circle members can represent the social circle, but the user tags in social media are even caused by the lack of lack of user tag data, and further to the social circle label mining difficult. This paper presents a circle label recognition method based on multiple linear regression, network structure and user and fusion tags itself characteristics and social circle, for each label to calculate a weight in the social circle, the right major label is more likely as the representative of social circle label. Compared with the related methods, this method solves the sparse problem of the label data and improves the recognition effect of the social ring label. 4, based on online social circle user personal data complement. User profile is an important feature in the social media users in the user profile is missing a number of user data completion has become a hot research direction in recent years. Most of the existing user data imputation method based on user text, change and noise characteristic of the text brings great interference to this kind of method. The social circle of the user is the embodiment of the strong social relationship of the user, and the different social circles represent the different social dimensions of the user. Based on this principle, this paper proposes customer personal data imputation method of non negative matrix factorization based methods by different users with different social circles complete social dimension of personal information users, to ensure the diversity of users' personal data, compared with the existing methods to improve the performance of user profile completion. To sum up, some related research work on the identification and analysis of user online social circles is carried out in this paper. The related technology is applicable to the analysis of social relations and social circles of users in the mainstream social media. Some preliminary conclusions and results have been obtained in the study, and it is hoped that it will be beneficial to the relevant work of user analysis in social media.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：C912.3;TP301.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 陳海強(qiáng);程學(xué)旗;劉悅;;基于用戶興趣的尋找虛擬社區(qū)核心成員的方法[J];中文信息學(xué)報(bào);2009年02期

，

本文編號(hào)：1342319