Integrating and Mining Virtual Communities across Multiple Online Social Networks: Concepts, Approaches and Challenges Wu-Chen Su National Cheng Kung University Tainan, Taiwan email:
[email protected] Abstract—Nowadays, virtual communities gather and organize via different means on the Internet. For example, Online Social Networks (OSNs) are providing a new way of doing business for enterprises with their customers and business partners. They can interact with each other on many OSNs building closer relationships than before. In addition, organizations do have more choices to use and infer useful knowledge from these valuable data sources favoring organizational growth. In this regard, management challenges among multiple online social networks must be revealed to provide insights of strategic thinking for organizations. This study shows that current research of multiple online social networks are shifting from Homogeneous OSNs to Heterogeneous OSNs and Social Internetworking Scenarios (SISs) and producing data interoperability, data privacy and security issues among these virtual organizations. An exploration of current applications and unresolved challenges is helpful for the future organizational development of online societies. Keywords—Heterogeneous Social Networks; Multiple Social Networks; Social Internetworking Scenarios; Review
I.
INTRODUCTION
According to the definitions offered by Kim et al. [1] and Kizza [2], Online Social Network (OSN) is a platform for connecting implicitly or explicitly people who form relations that in turn form communities among which information acquired and shared. OSNs can be categorized as traditional OSNs (e.g., Facebook and MySpace) and Mobile OSNs (mOSNs). The definition of mOSN is that people can use their mobile devices to access these OSNs in a mobile context. Furthermore, OSNs also can be seen as public or private information networks. In some circumstances, there may exist some private networks within public networks, for instance, a private company’s Facebook page for their members and loyal customers. To date, a vast number of different OSNs have been created to fulfill different needs in this modern society. The challenge for users and organizations is to select and combine the information sought for. Some researchers argue that the creditably of information regarding these OSNs is untenable due to much data of unknown authorship or which is based on insufficient knowledge[3]. Therefore, an effective strategy is needed to deal with these issues accordingly. In this study, an understanding of the current research, practical applications and theories of multiple OSNs from organizational perspectives is sought and an exploration of challenges facing these virtual communities in this rapidly developing
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environment is made. Then, suggestions are offered for future research in academia and industry based on the findings of this study. The remainder of this essay is organized as follows: “Overview” section is a brief introduction of the current multiple OSNs. Subsequently, new emerging research is highlighted through a review of pertinent research literature based on the research purpose and/or subject of those studies. Also, datasets and the latest development trends in knowledge discovery methods and relevant applications respectively are reviewed. Lastly, future challenges and current findings for integration of these virtual communities across multiple OSNs and discovering information are offered along with suggestions for future research in this new emerging domain. II.
OVERVIEW
A.
Categorization of Multiple Online Social Networks In Fig. 1, one can see the current research categories of multiple OSNs and their approaches in different communities. In this study, the focus is on new emerging trends－Social Internetworking Scenarios (SISs) [4] Heterogeneous OSNs [5] and relevant applications to investigate their positive impact on organizations. These studies do not just discuss only a single type of subject utilizing networks (e.g., people) in the real world situations, rather they are dealing with multiple roles and relations in varied information networks.
Fig. 1. Overview of Different Multiple Online Social Networks
B.
Social Internetworking Scenarios In this digital age, people tend to join multiple OSNs with respect to their own interests and different backgrounds. However, these OSNs have their own design strategies and features to attract online customers from all around the world to become members, thus, how to manage these OSNs in SISs is becoming a challenging issue and a new emerging research trend [4]. However, the definitions of SISs under this research
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direction are still unclear. The reasons are that researchers usually define their research from different theoretical perspectives, confusing subsequent researchers who follow their steps into this research domain. In Fig. 2, one can see a demonstrated example of SISs. Users join SISs and serve as bridges to connect each OSN. In each OSN, users can also join multiple sub OSNs (multi-relations).
OSNs. Thus, there are three different subjects and multiple relationships extant within information networks. For example, it can be authors, venue and paper in academic OSNs. Han [5] claims that if you just consider a single type of subject in this kind of information network, you will lose some useful information.
Fig. 4. Heterogeneous OSNs
III.
Fig. 2. Social Internetworking Scenarios
C.
Homogeneous OSNs In Fig. 3, one can see there are two small social networks (A-1 an A-2). Each rectangle represents a single user and the edge between each user is their relations via online communities. Thus, a user can join multiple small social networks in one OSN according to their interests and backgrounds. For example, users can have their own favorite tastes for different sporting groups. Therefore, one can distinguish this kind of network type through user roles in the networks. The network type of all social networks in Fig. 3 is the same and can be regarded as a Homogeneous network.
RESEARCH METHOD
In order to have a clear overview of this emerging research trend, this researcher collected data from four major IS/CS academic publication websites－IEEE Xplore, ScienceDirect, ACM Digital library and SpringerLink in June, 2013 and used the following selection criteria in this survey: 1.
The keywords in this study are “Social Internetworking”, “Heterogeneous Social networks” and “Multiple Social networks.” According to Kamal et al. [6], the term “Social Network” was used by sociologists and psychologists first. Thus, it will lead us to get many irrelevant results. Thus, in some databases, this researcher usually combines “Internet” or the research theme of “Computer Science” to filter out irrelevant datasets.
2.
In addition, studies not in the English language and abstract-only articles via host institution’s e-library system have been excluded, as were some technical-oriented studies which did not include any managerial or relevant data regarding organizations.
The author and collaborative experts screened the titles and abstracts of all articles found and eliminated studies which did not meet the aforementioned criteria first. The final selection of studies were decided through a full reading of their text and discussed when necessary. IV. Fig. 3. Multi-relational and Single Subject (Homogeneous) Networks
D.
Heterogeneous OSNs In real world situations, the multiple relations carried out via OSNs can allow those OSNs to not only be seen as Homogeneous OSNs, but also can be Heterogeneous OSNs. In Fig. 4, one can see different shapes; rectangle, triangle and circle nodes which represent different roles across multiple
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RESULTS
After reviewing these articles and filtering out irrelevant research, we found there are 25 articles that fall within the scope of this research from the defined dataset. The results are analyzed by their approaches (purposes and study subjects), study case(s), application network type and data structure of OSN. Furthermore, emerging knowledge discovery methods and varied applications across multiple OSNs are also presented to demonstrate their impact upon organizations.
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Table I. Research comparisons Reference
Approach
Study Case(s)
Application Network Type
Data Structure of OSN
Buccafurri et al. [4]
Find bridges across multiple OSNs
Anonymous groups
SISs
Nocera et al. [7] Buccafurri et al. [8]
Find HUBs of multiple OSNs (users) to favor their growths Find missing connections(me) across multiple OSNs through their neighbors
Students of 4 regional universities Anonymous groups
SISs
Different (use user accounts and their social relations to connect each OSN) Similar
Buccafurri et al. [9] Shen et al. [10] Buccafurri et al. [11]
Find bridge and starter
N/A
N/A
Find seed users who have the same interests to propagate information Discuss new crawling strategies in SISs
Anonymous groups
SISs
Anonymous groups
SISs
F. Buccafurri et al. [12] Rizzo et al. [13]
A framework of finding stereotypical maps of people (starters, spammers, hub, etc.) Design a social media crawler and a common data schema to collect media items
N/A
N/A
Media item groups
SISs
Tang et al. [14]
Consider privacy preservation when analyzing terrorists SNs
Terrorist groups
Irani et al. [15]
Crawl user footprints (profiles) across multiple OSNs and store them in a common schema for analyzing leakage attributes Botnets infection of multiple OSNs Social identity management (a purposed global model can be adjusted to any particular site) Propose a middleware for privacy protection across multiple OSNs
Anonymous groups
Homogeneous OSNs (data of 4 organizations was converted into one SN) SISs
N/A N/A
N/A (simulation) N/A
N/A
SISs
Profile management (concepts are similar to [17]). However, it just focuses on some particular OSNs) A crawler system for collecting data from SISs
N/A
SISs
Anonymous groups
SISs
Recommendation systems in SISs
University student groups
SISs
Different (they focus on crawling performances by node numbers ) Similar
Explore factors will impact trust relations across multiple OSNs
Online game communities and IBM internal communications Online game communities
Homogeneous OSNs
Similar
Homogeneous OSNs
Similar
Academic groups
Homogeneous OSNs
Similar
Groups for open source projects (e.g., developer groups, user groups, etc.) Criminal groups
Homogeneous OSNs
Similar (discussions, bugs, source code and mailing list boards)
Homogeneous OSNs
Criminal groups
Homogeneous OSNs
Similar (use datasets of professors and students to do experiment simulations) Similar
Academic groups
SISs
Academic groups
SISs
Varied (Academic networks, movie information networks, etc.)
Heterogeneous OSNs
Li et al. [16] Riesner et al. [17] Wu et al. [18] Jian et al. [19] F. Buccafurri et al. [20] De Meo et al. [21] Borbora et al. [22] Ahmad et al. [23] Huo et al. [24] de Sousa et al. [25] Fard et al. [26] Xufeng et al. [27] Zeng et al. [28] Zeng et al. [29] Han [5]
Resolve link prediction problems across multiple OSNs Find out top-k query results from multiple OSNs An approach to see the evolution of open source projects Use Data Mining, statistical methods, game theory and multi-agent techniques to find hidden knowledge in criminal networks Find criminals’ relationships, criminal leader and suspects of a conspiracy which based on ranking of indicators A recommendation system which based on importance of data and ranking strategies The concept is similar to [28]. However, this approach selects top-k interests which based on frequency of interested term. Mining heterogeneous information networks
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SISs
Different (use user accounts and their social relations to connect each OSN) N/A Different (datasets of Twitter and Foursquare OSNs) Different (datasets of Twitter, LiveJournal, YouTube and Flickr) N/A Different media items on different OSNs (e.g., Youtube, Flicker, etc.) Similar
Different user profile structures on different OSNs N/A (simulation) N/A (no implementation due to data integration difficulties, e.g., privacy of attributes) Different (common Ontology structures for storing private policies) Different (common structures for storing profiles)
Different (Semantic Web Dog Food and Twitter datasets) Different (Semantic Web Dog Food dataset) Similar (DBLP and IMDB networks)
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A.
Study Purpose The one category of research tries to find evidence and the core elements across multiple OSNs. Normally, these key elements are responsible for information propagation across multiple OSNs [4,7-8,10,12,25]. In the meantime, they may be named as bridge, broker, power user, hub, proxy, etc. The second category of research collects datasets across multiple OSNs, e.g. research of effective strategies and algorithms for crawlers to search and obtain information [11,13]. The most numerous research focuses on privacy and security issues in online environments [14-19], for example, prevention of network attacks [16] and user profile management [19]. In addition, research of recommendations [20-21], trust analysis [22], link prediction [23] and query results ranking of datasets [24] is extended from single OSN to multiple OSNs, and there is also research assisting criminal investigations of suspects and [26-27] for analyzing criminal group networks. In addition, the study cases are diverse among many sectors, for example, academic, business, criminal, open source project stakeholders. B.
Study Subject and Application Network Type Comparisons carried out for this research between these approaches find that research subject are ‘People’ (79.2%), ‘Ontology’ (8.3%), ‘People and Ontology’ (4.2%) and others (8.3%). However, there are some points about the bridge (connecting media) between each OSN that deserve being highlighted. In some cases, people may have some difficulties acting as the main bridges across multiple OSNs. However, some research assumes that people will do so based on their interests to collect information from varied sources. However, users may not be aware of any particular new OSN or even are inexperienced in searching for such information sources. In that case, they may not act as a potential bridge across multiple OSNs. Therefore, further research should consider different bridge actors. Additionally, the relationship between each OSN (network type) is also an important factor. If it is an intra-network type (i.e., Homogeneous OSNs), it means the data structures of OSNs will be more similar. Otherwise, future research must focus on data pre-processing if their research is using a data-driven approach (e.g., Data Mining application). As stated in the prior section of this essay, privacy and security issues are primary concerns for major research. Thus, future research also should not assume people will be delighted to share personal information during the setup of experimental studies. For example, in the medical domain, patients normally do not want their illnesses to be discussed in public. Before doing relevant studies, the feasibility of such research must be considered prior to beginning the research. C.
Data Set In previous discussion herein, it is mentioned that researchers have three major ways to store and access their datasets. Based on the analysis done for the current study, it can be said that these datasets for inter-network cases are mainly gathered from various theoretical approaches and application domains. Recent studies [4,8,11,13,15,20-21] use their own crawler systems to obtain data, usually
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incorporating OSNs (Twitter, YouTube, MySpace, LiveJournal and Google+) which have announced APIs (Application programming interfaces) for developers who have interest in customizing their programs to work smoothly with their business services. Thus, researchers can utilize user accounts to connect each OSN and get their desired datasets. Some studies [4,13] share datasets with the public to reduce efforts in collecting data. However, some attributes given in these datasets could not be obtained due to privacy concerns. Thus, De Meo et al. [21] in a study done created their own OSNs in a university domain and recruited students who study different subjects to participate in that research. In studies [28-29], the authors manually select a research subject as a main bridge and found their relationships with different OSNs to initiate that research. However, in a large data set, the explicit relationships are not observed easily. Thus, how to select wisely (e.g., influential person) among these valuable data sources for facilitating knowledge dissemination is also another problem for organizations. In addition, the different design strategies of OSNs can produce varied data structures. It is not easy to integrate these in a common data structure. Research should consider more flexible approaches to deal with data integration issues among these virtual organizations, for example, Ontology [18]; common schema for different OSNs are needed [19]. Considering these factors, there is some research using existing organized data [26-27] and simulations [16] to discuss research questions. Thus, this researcher believes, it is not easy to find a suitable dataset in this new research topic. D.
Knowledge Discovery Methods of Multiple Online Social Networks To date, there is no existing research using traditional Data Mining methods to discover knowledge from SISs [4]. Discovering information from SISs focus on finding influential elements (e.g., hub and bridge) and some interesting facts about whether there exists any “backbone” among these influential elements [4,7]. In order to favor the growth of potential hubs, it is also proposed that some strategies to train them as real hubs. For example, rewards could be provided for users to stay longer or participate in activities more in SISs. In addition, recommendations of new resources, opinions, potential friends who have similar interests according to hub’s profile could be elicited. In addition to all of these actions, in the future, they also hope to find “Spammers” and slack their “growths” across multiple OSNs. Thus, this researcher believes if organizations use similar ways to find the right person at the right time in the right place, it is possible to invest in them to promote organizational growth. Although current developments of SISs are still at an early stage, future research can focuses on developing new, or modifying existing, data mining algorithms to help organizations to carry out big data analyses across multiple OSNs and find useful information for making decisions. Additionally, Han [5] and relevant research groups have proposed serial mining algorithms to infer useful knowledge from Heterogeneous OSNs. For example, in Table II, one
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can see there are many interesting algorithms that can be used to project different dimensions of academic network and be extended to different information networks in our daily life. Table II. Revised Data Mining examples of [5] Knowledge hidden in DBLP Network What are CS/IS research area structures? Who are the leading researchers on Software Engineering? What are the most essential terms, venues, authors in Health Informatics? Who are the peer researchers of me? Who will collaborate with me in the future? Which types of relationship are most influential for an author to decide his/her topics? How was the field of Data Mining emerged or evolving? Which authors are rather different from his/her peers in field of E-learning?
Mining Functions Clustering Ranking Classification and Ranking Similarity Search Relationship Prediction Relation Strength Learning Network Evolution Outlier/anomaly detection
E.
Application of Multiple Online Social Networks In Table I, researchers show the effectiveness and applicability of applying concepts of multiple OSNs in their studies. Instead of finding useful information from a particular organization, the authors of these studies are keen to do surveys among multiple organizations and their datasets. These datasets have their linkages with heterogeneous study subjects for different purposes and usages. Mining these valuable concepts is very useful for organizations and relevant stakeholders. For example, in the work of Chen et al. [30], it is proposed that algorithms are used to elicit most effective treatments from medical research treatises and compare these with clinical evaluations made by medical professionals. In the end, they prove these mining information is very helpful for organizations. As one can see, this medical information network consists of different actors and components, e.g., medical staff, treatments, symptoms, author(s) of articles, etc. In addition, it represents different forms across online and real social networks. Therefore, considering these heterogeneous relationships among different organizations is important while conducting a practical research. After reviewing these interesting applications, we can see concepts of multiple OSNs have broadened possibilities of management of organizations’ knowledge in different domains from different perspectives. V.
CONCLUSIONS AND FUTURE RESEARCH SUGGESTIONS
To sum up, current research trends for integrations of virtual communities are shifting from Homogeneous OSNs to Heterogeneous OSNs and SISs. This study has revealed current issues of data interoperability, data privacy and security concerns across multiple OSNs. For data interoperability problems, there are several studies using a common structure for storing and accessing their organizational data and consideration of semantic integration simultaneously. In addition, the protection of personal privacy and security concerns are also important to
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organizations. A concept of a global model was developed to deal with issues of integration of privacy and security settings across multiple OSNs. However, not all OSNs have provided APIs for organizations to manage their data (e.g., health OSNs). Thus, how to effectively store and access individual information from these OSNs is needed for further discussions. In addition, this study also pointed out that different linkages, roles and subjects (e.g., people) are having interactions with each other through different ways in our daily life. Thus, how to find these implicit and explicit relationships from Heterogeneous OSNs and use this powerful information to promote growth of an organization is also another emerging problem. As mentioned previously, Data Mining application across multiple OSNs is still an emerging field, especially in SISs. If organizations want to do large scale analyses of their data to benefit organization growth, they still need some powerful tools to help them to achieve those goals. This review has summarized the state of current developments in the field of multiple OSNs and highlighted challenges which are still waiting for researchers to address in the future. Resolving these issues will be a great help for organizations enhancing their competitiveness in modern online societies. REFERENCES [1]
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