Web Mining Techniques for On-line Social Networks Analysis: An Overview

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1 Web Mining Techniques for On-line Social Networks Analysis: An Overview I-Hsien Ting, Hui-Ju Wu 1 Department of Information Management, National University of Kaohsiung No. 700, Kaohsiung University Road, Kaohsiung City, 811 Taiwan iting@nuk.edu.tw 2 Institute of Human Resource Management, National Changhua University of Education d @mail.ncue.edu.tw Abstract. On-line social networking has become a very popular application of Web 2.0 ages. This chapter provides a study about the issues of using web mining techniques for on-line social networks analysis. Techniques and concepts of web mining and social networks analysis will be introduced and reviewed in this chapter as well as a discussion about how to use web mining techniques for on-line social networks analysis. Moreover, in this chapter, a process to use web mining for on-line social networks analysis is proposed, which can be treated as a general process in this research area. Discussions of the challenges and future research are also included in this chapter. Keywords: Web Mining, Social Networking, Social Networks Analysis, Association Rule, Visualization 1. Introduction Social networks analysis is an interesting research direction to analyze these structures and relationships of social networks, such as the analyses of density, centrality and cliques of social network structure [32]. A social network is usually formed and constructed by daily and continuously communications of people and it therefore includes different relationships, such as the positions, betweeness and closeness among individuals or groups [21]. In order to understand the social structure, social relationships and social behaviors, social networks analysis therefore is an essential and important technique that has to be studied.

2 2 I-Hsien Ting, Hui-Ju Wu In recent years, on-line social networking is a very hot and popular application in the age of web 2.0 [16], which allows user to communicate, interact and share in the World Wide Web [1]. Some on-line social networking websites now even become the most popular sites on the web [29][11][22]. For example, Flickr for online album sharing, Youtube for online video sharing, Linkdin for connecting from college and employee, some portal-based on-line social networking websites are multi-functions integrated, such as Facebook, MySpace, etc. In addition, the rapid growths of Blog systems also provide good platforms for users to communicate and share. Thus, on-line social networking now is part of our human s life huge resources of communication contents, relationships and behaviors for us to do on-line social networks analysis are produced by the incredible developments of on-line social networking websites and applications [9]. The history of social networks analysis is older than everybody how is reading this chapter. The history of social networks analysis is more than hundred years from around 1900 s, and mostly in the research areas of sociology [32]. During this period, the studies of social networks analysis were focusing on small groups and small social networks. However, it becomes harder and harder to analyze manually for those broad social networks, such as the World Wide Web. Therefore, the strong computer ability and information technologies has became very important tool for social networks analysis and the search direction is therefore now moving from sociology to computer science. For on-line social networks analysis, the analysis targets are mainly focused on resources from the web, such as the contents of the web, the structures of the web and the usage behaviors of users in the web. Among the information techniques that can be used for the analysis of on-line social networks, web mining is claimed to be the most suitable one [7]. Web mining is an application of Data Mining and the analysis targets of web mining are mainly from the World Wide Web, such as web content mining, web structure mining and web usage mining [5]. Therefore, it is more than suitable to use the web mining techniques for on-line social networks analysis, and it is also the focus of this chapter. The chapter is structured as follows: section 1 is the background and introduction of this chapter, and the literature reviews about social network analysis and web mining are provided in section 2. In section 3, a study of how web mining techniques can be used for on-line social networks analysis will be included and a general process for applying the web mining techniques for on-line social networks analysis will be proposed in section 4. In section 5, a discussion of the challenge of using web mining for on-line social networks analysis will be provided and the future research directions will be included in this section as well.2. Background 2. Literature Review In this section, related literatures about social networks analysis and web min-

3 Web Mining Techniques for On-line Social Networks Analysis: An Overview 3 ing will be reviewed, in order to present a broad view about these two topics for readers. 2.1 Social Networks Analysis In the research area of social networks analysis, it is usually the main task about how to extract social networks from different communication resources [21] [26]. The data that used for building social networks is relational data [32], which can be obtained and transferred from different resources including the web, communication, internet relay chats, telephone communications, organization and business events, etc [4]. For example, the communication is a rich source for extracting and constructing social networks. In the issue of social networks extraction, the relationship between senders and receivers can be transformed by measuring the frequency of communication with take the communication behavior (such as reply, forward, etc.) into account [8]. The transformed relational data can then be used for social networks construction. In the past three decades, social network analysis has developed a range of concepts and methods for detecting structural patterns, identifying patterns of different types of relationship interrelate, analyzing the implications that structural patterns for the behavior of network members, studying the impact on social structures of network members and their social relationships [37][ 32] [35]. Types of Social Network Analysis A social network has a set of relations of ties, which can be viewed in two different ways. One approach focus on an individual, called ego-centered network, and put it at the centers of the network. Members of the network are defined by the relations with the ego. Ego-centered network analysis can show the range and breadth of connectivity for individuals and identify those who have access to diverse pools of information and resources. The ego-centered approach is useful when the population is large, or the boundaries of the population are hard to define [23] [38].The second approach considers the whole network based on some specific criterion of population boundaries such as a formal organization, department, club or kinship group. Whole network analysis can identify those members of the network who emerge as central figures or who act as bridges between different groups. This approach requires responses from all members on their relations with all others in the same environment, such as the extent of and video communication in a workgroup [18].

4 4 I-Hsien Ting, Hui-Ju Wu Key Concepts of Social Network Analysis Network analysis provides a rich and systematic means of assessing such network by mapping and analyzing relationships among people, teams, departments or even the entire organization [25]. A network is composed of three elements (1) actors (2) relations between actors, and (3) the linkages among actors. Actors and their actions are viewed as interdependent rather than independent, autonomous units. Actors can be persons, organizations, or groups, or any other set of related entities. Relations between actors are depicted as links between the corresponding nodes [39]. A tie connects a pair of actors by one or more relations. Pairs may maintain a tie based on one relation only or a multiplex tie based on many relations. Thus, ties also have characteristics like content, direction and strength, but they are often referred to as weak or strong. Social network analysts have found that multiplex ties are more intimate, voluntary, supportive and durable [36]. In addition, the linkages among actors have several characteristics, which are direction, degree, and content. The direction of linkages covers symmetrical and asymmetrical relations; the degree of linkages means the strength of relations, and the content of linkages includes friendship, information, power, and influence, etc. Owing to complex properties of nodes, relations, and linkages, scholars utilizing the concept of network in their studies have different definitions of network [13]. SNA Techniques Visualization is also a hot topic of social network analysis, and it is a suitable technique in this area. Through the visualization of social networks, the characters of social networks can be understood easily, such as the structure of networks, the distribution of nodes, the links (relationships) between nodes and the clusters and groups in the social networks[19][35]. In additional to social network extraction and visualization, there are other measurements that can be used for social network analysis as well [35]. For example, centrality degree of a social network is a measurement that is used to measure the betweenness and closeness of the social network [34]. Betweenness centrality indicates the extent to which a node lies on the shortest path between every other pair of nodes. Closeness centrality analyzes centrality structure of a network based on geodesic distances among nodes in a social network [6]. Cluster coefficient is a measurement to discover the clusters in a social network and to measure the coefficient of the clusters. The density measurement can be used to analyze the connectivity and the degree of nodes and links in a social network [24]. The measurements path length and reachability can be used to analyze how to reach a node from another node in the social networks. Structural hole is also a measurement of social network analysis, which can be used to discover the holes in a social network and by this to fill the hole and expand the social network [10]. These sparse regions are structural holes that prove opportunities for brokering information flows among actors. Thus, maximizing the structural holes spanned or

5 Web Mining Techniques for On-line Social Networks Analysis: An Overview 5 minimizing redundancy between actors is an important aspect of constructing an efficient, information-rich network [3]. 2.2 Web Mining In the introduction section of this chapter, a brief explanation of web mining has been provided. It is an application of data mining and data mining is a technique to discover and extract useful information from large data sets or databases [17]. For web mining, the definition therefore can be explained as to discover or extract useful information from the web [5]. Different Types of Web Mining According to different analysis targets and resources, the web mining techniques can be divided into three different types, which are Web Content Mining, Web Structure Mining and Web Usage Mining. Web content mining is a web mining technique to analyze the contents in the web, such as texts, graphs, graphics, etc [2]. Recently, most of web content mining researches are focused on the text data processing and few are focused on other multimedia data. Natural language process is therefore the main technology that used in this area. The concept and techniques of Semantic Web and Ontology also have to be studied [14][27]. Web structure mining is a technique that can be used to analyze the links and structure of websites. Graph theory is usually the main concept and theory for web structure mining to analyze and explain the structure of websites. In addition, the extraction of the structure of websites is always essential in this research area [10]. Therefore, it s usually the concern about how to design and implement a crawler (or spider, bots) to extract and construct the structure of websites, such as the research topic of Deep-web. Web usage mining is a web mining technique that can be used to analyze how the websites have been used, such as the navigation behavior of users. The serverside Clickstream data (logs file) is the main sources that used for web usage mining. Client-side data (such as client-side logs file, cookies) is sometimes to be used due to some research concerns, such as in order to record more complete behavior of users. Different web usage mining analyses include basic statistical analysis of the navigation behavior of users in a website, such as how many times the website has been browsed, where the users comes from, etc. Furthermore, advanced web usage mining analyses can also be provided, such as more complex analysis for understand the navigation history of users in a website or cross-website analysis [31].

6 6 I-Hsien Ting, Hui-Ju Wu Web Mining Techniques Traditional data mining techniques can also be provided for web mining, such as classification, clustering, association rule mining, and visualization. In web mining, the classification algorithms can be used to classify users into different classes according to their browsing behavior. For example, a classification application classifies their users according to their browsing time. After classification, a useful classification rule like 30% of users browse product/food during the hours 8:00-10:00 PM can be discovered. The difference between classification and clustering is that the classes in classification are predefined (supervised), but in clustering are not predefined (unsupervised). The criterion by which items are assigned to different clusters is the degree of similarity among them. The main purpose of Clustering is to maximize both the similarity of the items in a cluster and the difference between clusters [20]. The association rule mining technique can be used to indicate pages that are most often referenced together and to discover the direct or indirect relationships between web pages in users browsing behavior [31]. For example, an association rule mining in the web usage mining area should take the form the people who view web page index.htm and also view product.htm the support=50% and the confidence=60%. Visualization is a special analysis technique in web mining that allows data and information to be understood or recognized by human eyes. Graphical and visualized means are used for this kind of technique to represent data, information and analysis results [19]. In web structure mining, it usually plays an important role to illustrate the structure of hypertexts and links in a websites or the linking relationship between websites. For other two types of web mining techniques, visualization is also an ideal tool to model the data or information. For example, a graph (or map) can be used for web usage mining to present the traversal paths of users or a statistic graph to show the information of web usage. This approach enables the analyzer to understand and to interpret the analysis results of web usage mining very efficiency. 3. Web Mining Techniques for On-line Social Networks Analysis In this section, the three different types of web mining and the techniques of web mining that introduced in section 2 will be used for discussion to show how these techniques can be used for on-line social networks analysis.

7 Web Mining Techniques for On-line Social Networks Analysis: An Overview The Three Web Mining Types for On-line Social Networks Analysis Web content mining, text mining or natural language processing are very useful techniques that can be used for on-line social network analysis. For example, web content mining can be used to categorize or classify the documents of on-line social networking website, especially for blog or text forum analysis to categorize or classify the articles of blogs. The article categorization is usually the first task for many on-line social networks analyses or applications. Furthermore, web content mining can also be used for on-line social networks analysis to analyze users reading interests, such as the favorite contents of users. However, for most on-line social networks analysis tasks, it is usually necessary to work with other types of web mining and techniques collaboratively. For example, the social networks analysis likes this case is necessary to use the concept and techniques of web content mining and web usage mining. Web usage mining plays an important role in on-line social networks analysis as well. It is useful for the on-line social network analysis of social networks extraction that discussed in section 2 of this chapter. The usage data and users communication in on-line social networking website can be transformed to relational data for social-networks construction [24]. In addition, web usage mining is also a tool to measure the centrality degree. For example, the closeness of blog users can be measured by: Closeness = ( f *( w* b)) + ( f *( w* r)) + ( f *( w* i)) (1) In the equation above, the f denotes the frequency of a blog behavior, and w is the weight of closeness for each blog behavior. The three blog behaviors are b=browsing, r=reading and i=interaction. This is just a simple case of web usage mining, and the techniques of web usage mining allow many possible means of on-line social networks analysis. Web structure mining is the third kind of web mining and it is also useful for extracting and constructing on-line social networks to extract the links from WWW, or other sources. Web structure mining also can be used to analyze the path length, reachability or to find the structural holes, which are very basic and traditional social networks analyses. Web structure mining usually provides graph and visualized to represent the data and information of social networks, which enables the analyzer to understand and to analyze social networks easily [15]. For most on-line social networks analyses, the three types of web mining can t work alone by just using one of them, it is not similar to other web usage mining applications. Sometimes, the three different types of web mining maybe used for just one particular on-line social network analysis.

8 8 I-Hsien Ting, Hui-Ju Wu 3.2 Web Mining Techniques for On-line Social Networks Analysis There are many different kinds of web mining techniques, such as those discussed in section 2 of this chapter. In this section, two of them will be used as examples to explain how web mining techniques can be used for on-line social networks analysis. The two techniques are clustering and association rule mining. Clustering is an important web mining techniques for on-line social networks analysis. In social networks analysis, finding a group of closet people in a network or cross networks are usually the main tasks. Normally, this task is achieved by using visualization technique in a small social network. However, only few groups in a social network are expected to be discovered by using this approach, and further analyses are hard to be taken. Thus, the clustering technique can of helping for a large social network to identify more groups and clusters. Moreover, it can provide many detail information than just using visualization [33]. They include the closeness of a group, the detail information of members in a group and the relationship between groups in a social network. Association rule mining is another web mining technique that is popular to be used in traditional data mining application, such as marketing analysis, and it is therefore also called market-basket analysis. In social network analysis, the association rule mining can help us to discover the hidden relationships between nodes in a social network or even cross networks. For example, an association rule for on-line social networks analysis maybe the person A who know person B and also know person C, the support is 0.9 and the confidence is 0.5 or the person who read person A s blog article and also read person B s blog article, the support is 0.9 and the confidence is 0.5. The association rule mining therefore can provide different analysis and to transform more relational data and to identify more nodes and relationships in social networks. In addition, the association rule mining is helpful for the application after social networks analysis, such as recommendation systems or information filtering systems [28]. 4. The Process to Use Web Mining for On-line Social Networks Analysis In this section, a general process to use web mining for on-line social networks analysis will be proposed, and the details of each step in the process will be discussed as well. Figure 1 presents the general process to use web mining for on-line social networks analysis. The steps in the process including selection of analysis targets, selection of on-line social networks analysis, data preparation, web mining techniques selection, results presentation and interpretation, recommendation and action.

9 Web Mining Techniques for On-line Social Networks Analysis: An Overview 9 Fig.. 4. The general process to use web mining for on-line social networks analysis The first step in the process is the selection of analysis targets. In this step the analysis targets will be selected, such as web, , telephone communication, etc. Sometimes, there may not only one target will be selected due to some analyses may focus on the analysis of multi-targets. After this step, then we can select what kind on-line social networks analysis will be proceeded. Once the analysis targets and on-line social networks analysis methodology have been selected, the next step is data preparation. In this step, related data will be collected in this stage for analysis and the data will be cleaned and formed as the final format to store in database. Then the next step in the process is web mining techniques selection and to proceed the selected web mining techniques. As discussed in the previous sections of this chapter, there may not only one web mining techniques will be selected and sometimes the collaboration of different types of web mining technique is necessary. After selecting suitable techniques for on-line social networks analysis, the selected techniques then will be used to analyze the data that collected and prepared in the third step of the process. The analysis results after web mining then will be presented and the results will be interpreted either manually or automatically. Visualization technique sometime is used to assist the presentation of analysis results, such as the extracted social networks. The last step of the general process to use web mining for on-line social networks analysis is recommendation and action. This is an optional step in the

10 10 I-Hsien Ting, Hui-Ju Wu process, and the process may be terminated after the analysis results have been generated. The recommendation and action step is the step to deal with the analysis results that generated in the previous step. For example, if the structure holes in a social network has been discovered by on-line social networks analysis, the recommendations about how to fill the hole will be generated (by manually or automatically) and then to take appropriate actions for the generated recommendations. The general process to use web mining for on-line social networks could be a continuous process. In some research projects, the process will be started again after recommendations have been generated and actions have been taken. The process will be started again in order to perform the action performance evaluation or to hold a new social networks analysis. The process that proposed in this section is a general process and it doesn t mean any case that uses web mining techniques for on-line social networks analysis will follow the process. In some cases, the modifications of this process are necessary to suit the requirements of different on-line social networks analysis projects. For example, the recommendation and action step in the process may not necessary for some cases and can be removed from the general process. 5. Discussion In this chapter, a study of applying the concept and techniques of web mining for on-line social networks has been provided, and related literatures of web mining and social networks analysis have been reviewed. Moreover, how to use web mining and a general process of using web mining for on-line social networks analysis have also been proposed in this chapter. Web mining based techniques are proving to be useful for analysis of on-line social network data, especially for large datasets that cannot be analyze by traditional methods. This chapter will help researchers by providing a review on the research, enable the understanding of how web mining can be useful for on-line social network analysis and motivate them to pursue new research in different field. It is an interesting topic about how to use the techniques of web mining for online social networks analysis. However, there are several challenges in this research area to be overcome. For example, data sampling is a big issue when using web mining for on-line social networks analysis. In other web mining applications, data sampling is a simple task to reduce the amounts of data size. However, in online social networks analysis, it becomes a hard task to select suitable samples which can represent the real social networks. Furthermore, how to collaborative different types of web mining techniques for a particular on-line social networks analysis is usually issue. The approach and process that proposed in this chapter is of helping to deal with some cases of on-line social networks analysis, but tunings of the process are sometimes necessary. It will be useful for practitioners from internet world to understand how web mining based techniques can help them han-

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