Capstone Thesis

Interpreting and Defining Measures of Community Success

Chuck Cage, EMAC/UTD


A national survey conducted in 2012 reported that 60% of American adults use social networking sites like Facebook or Twitter and 21% of those social networking site users belong to one or more defined “groups” within the SNSs. (“Overview | Pew Internet & American Life Project,” n.d.). As online communities increase in popularity, so increases our need to understand what makes some communities succeed fantastically while many fail miserably (Fichter, 2005). More significantly, as “community building” becomes a key part of marketing, activism, and even online participation in general, both community managers and community scholars require a set of metrics to quantify the success of communities. The identification of these metrics is complicated by confusing multiple definitions of community and the lack of a unifying framework in which to examine the significance of community metrics.

This paper starts by tracing the evolution of community definitions toward a single dimension definition that separates communities from other non-community interactive groups, operationalizing this definition and identifying the significance of selecting the correct point of view from which to choose key community success factors. It then gathers metrics from current literature into two large categories—size and participation—and presents methods in which these categories of metrics can be used to estimate community health within the framework of relationships and sustainability. Finally, it presents two potential social network analysis methods which future research could leverage to integrate people-related size metrics and communication behavior-related participation metrics into single metrics which directly measure sustainability and relationship health.


The core concept of “community” is difficult to define, as the concept sees application in many different areas of study and popular definitions of the term change over time due to advancements in transportation and communication technologies. Early studies, however, define community geographically (Preece, 2001). Colloquial definitions, like those found via free online dictionaries, center around place, or shared geographic location; in common speech the word “community” often replaces the word “neighborhood,” as in “apartment community” or a housing development’s (for resident use only) “community pool.” Online, some communities remain geographically bounded, such as college- or workplace-related social forums (Steinfield, DiMicco, Ellison, & Lampe, 2009; Wellman et al., 1996) and computer networking systems operated in support of cities and neighborhoods.

Even in communities not strictly delimited by geography, place may play a key role in determining with whom we choose to communicate and how often we communicate with them. A study of the online social networking activity of emerging adults (i.e., young people) found a significant overlap in online and face-to-face friends—a roughly 50% overlap via both instant messaging and social networking site friendships (SNS)—with most respondents further reporting “that they would only add people they had met in person onto their network on social networking sites” (Subrahmanyam, Reich, Waechter, & Espinoza, 2008). Another study of friendship links among users of the online blogging platform LiveJournal discovered that approximately 70% of friendships among LiveJournal bloggers “are derived from geographical processes” and found evidence that “the probability that a person befriends a certain candidate” is related to “the inverse of the number of closer candidates” (Liben-Nowell, Novak, Kumar, Raghavan, & Tomkins, 2005). While the internet enables us to meet and become friends with people from around the world, we’re more likely to select from local friend candidates first.

Online communities ranging from message boards to massively multiplayer online games (MMOs) provide an effective set of tools for structurally recreating Oldenburg’s “third places” (Rheingold, 1993; Steinkuehler & Williams, 2006)—places which “host the regular, voluntary, informal, and happily anticipated gatherings of individuals beyond the realms of home and work” (Oldenburg, 1999). While taking “third place” social capital-building interactions online expands the potential geographic reach of such communication networks, it also takes those interactions into the physical locations where participants’ “non-work” computers reside: their homes. This reinforces a “stay-at-home, place-to-place connectivity” which in turn drives “extensive global and local interaction” (“glocalization”) where people maintain both face-to-face-originated friendships and potentially globe-spanning online ties (Wellman, 2005).

In both cases, rather than transitioning us to location-free global social networks, internet communication produces locally-clustered yet globally-interlinked social networks—“small world” networks—in which network structure and the flow of information through it are affected by place.

Some argue, however, that the reduced geographical restrictions for participation offered by computer mediated communication (CMC) has contributed to “a long term shift to communities organized by shared interests rather than by shared place (neighborhood or village) or shared ancestry” (Wellman & Gulia, 1999). Additionally, since mobile phones and data devices are more “person-based” than “place-based,” recent heavy public adoption of mobile technology may have shifted the nature of social networks from “place-to-place” to “person-to-person,” creating networks defined “socially and not spatially” (Wellman, 2005). Thus any shared attribute can function as a community boundary, and some definitions of community rely on these boundaries.

But while place and personal attributes serve as valuable tools to divide people into related groups—“virtual settlements,” or “cyber-places in which virtual communit[ies] operate”— not all virtual settlements are recognizable as “communities” (Jones, 1997). Students participating in a class-related online forum, for example, can interact daily in order to fulfill course requirements, yet in some cases never form close bonds with their fellow students. Online marketers can draw people to a promotional website and even convince those people to contribute their own content, but such contributions do not assure the emergence of community. What distinguishes cyber-places from virtual communities is a “sense of community” (SOC), defined as “a characteristic of successful communities distinguished by members’ helping behaviors and members’ emotional attachment to the community and other members” (A.L. Blanchard & Markus, 2004).

Conversely, “sense of community” may form in widely heterogenic groupings, as in the case of the Whole Earth ‘Lectronic Link (the “WELL”), a long-standing discussion forum which bills itself as “the birthplace of the online community movement” and which draws discussion on a wide range of topics from a geographically diverse user base. Howard Rheingold, an early participant in and advocate for the WELL, wrote:

A virtual community as they exist today is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. In cyberspace, we chat and argue, engage in intellectual intercourse, perform acts of commerce, exchange knowledge, share emotional support, make plans, brainstorm, gossip, feud, fall in love, find friends and lose them, play games and metagames, flirt, create a little high art and a lot of idle talk. We do everything people do when people get together, but we do it with words on computer screens, leaving our bodies behind. Millions of us have already built communities where our identities commingle and interact electronically, independent of local time or location. The way a few of us live now might be the way a larger population will live, decades hence. (Rheingold, 1996, p. 414)

Not surprisingly, he defined “virtual communities” as “social aggregations that emerge from the Net when enough people carry on those public discussions long enough, with sufficient human feeling, to form webs of personal relationships in cyberspace” (Rheingold, 1993), calling out relationships as a key factor separating communities from other communication behavior.

Studies of offline and online communities (Agarwal & Liu, 2008; A. Blanchard, 2004; Anita L. Blanchard, 2007; Gruzd, Wellman, & Takhteyev, 2011; McMillan & Chavis, 1986) have operationalized sense of community in terms of relationships as well, indicating that sense of community requires feelings of membership (participation in creating boundaries and group symbols which represent group membership), feelings of influence (influencing and being influenced by the community, often through the enforcement and public challenge of group norms), feelings of integration and fulfillment of needs (satisfaction obtained through reinforcement of ideas or receipt or giving of status), and shared emotional connections. A 2004 survey study of an online community of athletes and sports enthusiasts found sense of community present, further defining the dimensions of SOC, reporting that community members found feelings of membership through recognizing (and being recognized by) other members, integration and fulfillment of their needs through exchange of support, shared emotional connections via attachment obligation (a need to return to the site due to a perceived dependence of others), identity creation and consumption, and value of their relationships with other specific community members (A.L. Blanchard & Markus, 2004).[1]  Therefore, this paper adopts this relationship-based definition and operationalization of community because as it offers significant advantages over previous definitions, specifically a separation of the “engine” of community from the various habitats in which it can exist.

[1] It’s important to distinguish relationships from engagement, a term found in brand marketing literature and more recently applied to communities. Though definitions vary, engagement generally refers to community participants’ commitment to the community—a combination of satisfaction, loyalty, and willingness to participate (Brodie, Ilic, Juric, & Hollebeek, 2011). While engagement equates to some components of the operationalization of sense of community, engagement primarily represents a product of community instead of a predecessor of it.


Selecting and Evaluating Metrics

But the above definition of community provides only part of the framework in which to interpret community metrics. Studies and literature examined here found that different types of communities require different metrics for success. For example, a use study of an IBM community tool that included successful communities spanning a variety of place (global), cyber-place (forums, blogs, wikis, etc.), people, and purpose (communities of practice, teams, technical support groups, idea labs in which groups brainstormed for innovation, and recreational communities) found significant differences in number of participants, level of participation, resource production per participant, and thread depth among communities, suggesting that success may be defined and/or operationalized differently for various community types.

Additionally, community participants may have different perceptions of success than community operators. Communities tend to follow a life cycle divided into the broad categories of inception, creation, growth, maturity, and death. In order to avoid death, communities must continue to produce quality content, attract new users to replace members lost to attrition, and generate trust and interaction among their membership (Leimeister, Sidiras, & Krcmar, 2004). However, these sustainability requirements may conflict with the needs of individual community members who experience a reduced motivation to participate (or interact with new potential relationship partners) upon reaching cognitive constraints (Dunbar, 2008) or satisfying their needs.

Individual participants and operators also interpret success differently. Preece writes:

A manager of an e-commerce site will judge success of an online community in terms of how many people are drawn to the site, how long they stay, how often they come back, and ultimately how much they spend on goods or services. A teacher will judge success of a learning community by how well students perform their work, the quality of their projects and what students say about their learning community. Sick or unhappy people will judge a support community by the help and empathy they receive from others and the information they get that enables them to deal with their predicament. (Preece, 2001, p. 12)

Therefore, when selecting and interpreting community metrics, care should be taken to consider a) the metrics’ measure of (or effect on) relationships and sustainability, b) the type of community in which the metrics will be applied (including the community’s specific technical and social affordances), and also c) the success factor perspective of the target metric audience. These factors affect not only which metrics may be important for a given community, but may also indicate the necessity of establishing baselines from which to gauge the significance of metric values.


In examining a body of literature encompassing approximately 85 academic papers, books, and commercial white papers related to the study of functioning and success of communities, this paper extracts 64 unique[1] metrics which are included in Table 1. Duplicate metrics were removed or consolidated, and in cases of functionally similar metrics and definitions priority was given to sources containing more individually unique metrics.

However, none of these metrics was found to offer direct measures of sustainability or relationship health, the key factors to community identified here. Instead, existing metrics help predict the change of other metrics. For example, number of participants might predict a dimension of participation or a dimension of participation might offer insight into future participation, but both participation and number of participants affect sustainability. Therefore, to facilitate analysis within the established conceptual framework, these metrics were categorized into two main categories—size (metrics which pertain to the number of people involved in the community) and participation (metrics which pertain to the volume or quality of communication activity in the community).

Additionally, two much smaller groups of metrics were delineated as well: relationships (metrics which comprise components of social network analysis, but which don’t provide direct measurement of relationship health) and composite (measures which combine other measures to form indices). These two groups are addressed separately; SNA measures receive their own section below related to future research directions, and composite measures are addressed in the paper’s conclusion.


Whether physical or virtual, communities require resources to provide benefits to members, and people often represent a primary source of those resources (Butler, 2001). Therefore, larger size would seem to predict larger community benefits. However, benefits do not always scale linearly or relate proportionally to size.

In online discussion communities, members create a public good—either information or the opportunity to interact—through their contributions. Due to the digital nature of these contributions, each member’s contribution to the public good costs the same regardless of the number of “free riders” who consume the information without contributing. Therefore, the number of contributors required to achieve the “core” of content necessary to attract new members is are often referred to as the “critical mass” of active community membership, because once a community passes this threshold of participation (and created content) it can then continue to attract others at no additional cost. In such instances, larger groups are more likely to contain a subset of willing-to-participate individuals large enough to constitute a critical mass. Critical mass may represent a lower constraint on sustainable interactive discourse, representing the minimum community population required to produce a public good (Wasko, Teigland, & Faraj, 2009). Critical mass may also represent the minimum population required to achieve “positive network externalities,” the effect seen when “a product becomes more valuable as more users adopt the product” (Jones & Rafaeli, 2000).

Larger groups also provide increased opportunity for interactions as well as a larger “audience resource”—the perceived group of people who could potentially consume created content—both of which increase the value of the community to potential members (Butler, 2001). Increased ability to attract new members supports sustainability through replacing members lost to attrition.

In some instances, however, increased size can prove detrimental. For example, the exponential increase in possible interaction partners that comes with increased community size makes it harder for individuals to know the rest of the group’s members, which may reduce social support—a key operationalization of sense of community—and make it harder for individuals to locate information within the community (Butler, 2001). The increased volume of communication that accompanies larger communities may also lead to information overload, an “acute sensation of being swamped by unwanted information” which can lead users to either leave the community or modify their communication behavior to reduce the flow of information to a more manageable level (Jones & Rafaeli, 2000).

In large communities, increased group size—especially size beyond the critical mass threshold—may lead community members to perceive that their contributions are not required, increasing the instances of “social loafing” where members come to expect that others within the community will provide the content and resources required for sustainability and experience reduced motivation to contribute (Butler, 2001). This threatens sustainability by reducing the percentage of actively contributing users in the community and may also affect sense of community by impeding attachment obligation.

It’s clear that size can both positively and negatively affect ongoing participation and sustainability and has mixed effects on individual participants. Current research suggests that there may exist a window of size, bounded on the low end by critical mass and constrained on the high end by communication self-reduction effects such as information overload and social loafing, in which communities thrive and are most sustainable in terms of both content creation and maintenance of sense of community.


Community size represents only a potential value to communities. In order to express that value, community members must also participate. Participation is important to both sustainability (Iriberri & Leroy, 2009) and to sense of community (McMillan & Chavis, 1986).

Participation can take both active and passive forms. Active participation is most commonly recognized as beneficial in the literature examined here and forms the basis for common participation metrics such as messages per user, number of messages per unit time, and combination participation/size metrics such as active members (Preece, 2001). However, some scholars recognize passive participation in the form of reading behavior (Panciera, Priedhorsky, Erickson, & Terveen, 2010; Rafaeli, Ravid, & Soroka, 2004; Soroka & Rafaeli, 2006). Reading content created in the past by other community members helps to create “cultural capital,” defined as “knowledge that enables an individual to interpret various cultural codes” (Rafaeli et al., 2004). Such knowledge supports sense of community through the consumption of identity and by providing the tools for recognizing signs of community boundaries. Furthermore, a combination of active and passive participation can encourage additional participation and help manage information overload by balancing contribution and consumption (Rafaeli et al., 2004). Though it can be difficult to count lurkers, who often leave no visible traces of their activity (Soroka & Rafaeli, 2006), it is possible in some CMC infrastructures to identify and quantify lurking behavior through metrics such as logins, visit frequency, or contribution object view counts.

Not all participation offers equal benefit to communities. Participation perceived as reciprocity, for example, may offer benefits above and beyond non-reciprocal participation by positively influencing members’ integration into the community and increasing community satisfaction, which also positively influences intention to participate in the future and may help retain and integrate community newcomers (Casaló, Flavián, & Guinalíu, 2011). Reciprocity has also been shown to increase the creation of new conversations (Huffaker, 2010). Metrics contributing to detecting and understanding reciprocity include number of answered threads (Durant, McCray, & Safran, 2010; Matthews et al., 2013), average time between content creation and response, responsiveness (a function which factors the previous two metrics together and aggregates them across an entire community over time) (Durant et al., 2010), and number of replies per thread/total number of replies (Durant et al., 2010; Matthews et al., 2013).

A further subset of reciprocity, interactivity, or “the extent to which messages in a sequence relate to each other, and especially the extent to which later messages recount the relatedness of earlier messages,” may provide additional benefits to communities.[2] One study’s content analysis of newsgroup activity revealed that interactive communication contained “more statement of opinion in general, and specifically more expression of agreement” in interactive messages as well as “more self-disclosure and more than twice as much use of the first-person plural pronouns,” both of which associate interactivity “with a sense of involvement and belonging” (Rafaeli & Sudweeks, 1997). Expressions of identity and feelings of belonging are both factors of SOC and suggest that interactivity may be key to establishing a sense of community.

As with size, the interpretation of participation metrics in terms of success varies across communities (Preece, 2001), so it may be necessary to examine additional community factors to establish participation scales.

Social Network Analysis

While measurements of size and participation, especially when combined, may provide predictive indications of success in terms of community building and sustainability, they do so by summarizing and aggregating individual interactions that correlate with success. They fail to take into account the effect these interactions have on one another and the significance of the position one interaction—or one participant—may occupy within a network of all participants and interactions. Social network analysis (SNA) fills this gap. Barry Wellman describes social network analysis simply:

Social network analysis conceives of social structure as the patterned organization of these network members and their relationships. Social network analysts work at describing underlying patterns of social structure, explaining the impact of such social structures on other variables, and accounting for change in social structures. Social network analysis has developed procedures for detective structural patterns, seeing how patterns of different types of relationships interrelate, analyzing the implications that structural patterns have for the behavior of network members and their social relationships. (Wellman, 1997, p. 1)

As it offers the opportunity to uncover insights into relationships—the key to sense of community—and to quantitatively link people and communications in order to further explain relationships between size and participation, social network analysis may offer additional metrics to more directly measure community success. Two such potential metrics are included below and represent directions for future research.

Clustering coefficient (C) represents the degree of local redundancy in a social network using the following formula (Panzarasa, Opsahl, & Carley, 2009):[3]

 Screen Shot 2013-05-01 at 3.49.21 PM

From a social capital perspective, “local redundancy and social cohesion promote a sense of belonging, foster trust, facilitate the enforcement of social norms and the transfer of tacit and complex information, and enable the creation of a common culture” (Panzarasa et al., 2009).[4] All of these results correlate with the previous operationalization of sense of community, suggesting that clustering coefficient may serve as a good predictor of sense of community.

However, local redundancy comes at the cost of structural holes, or links between otherwise unconnected sub-groups within the community. In other words, “if all your friends’ friends are also your friends, none of your friends can introduce you to new social circles where you may meet someone you have never met before and who knows something you do not know already”(Panzarasa et al., 2009, p. 925). Exposure to different and dissenting opinions and dissimilar potential friend candidates both positively affect participation (Ludford, Cosley, Frankowski, & Terveen, 2004). Therefore, increased redundancy may negatively affect community sustainability. Research also indicates that the closure associated with increased redundancy increases norm reinforcement, placing constraints on the evolution of social norms and, when coupled with socially undesirable behavior, can produce and sustain harmful norms (Prell, 2009).

Small world structures may provide the key to balancing the competing benefits of local redundancy and structural holes—balancing openness and closure in social networks. Small world networks exhibit both a high local clustering of elements as well as a small number of “shortcuts,” or structural hole bridges which connect otherwise disconnected clusters (Humphries & Gurney, 2008; Prell, 2009). More specifically, small world networks are networks which reside in the regime where average path length is less than the average path length in an equivalent random network and the network’s clustering coefficient is also greater than the clustering coefficient of an equivalent random network (Humphries & Gurney, 2008; Watts & Strogatz, 1998). Thus, such networks deliver the most possible local clustering with a minimal cost in terms of brokerage (Prell, 2009). One potential operationalization of small world network structure is Humphries and Gurney’s “small-world-ness” metric, which, in general terms, “defines a precise measure of ‘small-world-ness’ S based on the trade off between high local clustering and short path length”  (Humphries & Gurney, 2008).[5]

This linkage between social capital theory and small-world theory requires additional research, but it may help define boundaries of healthy aggregated brokerage and closure behavior within a network, providing community managers indication of excessive “cliquishness” or poor social bonding among community members.[6] If so, the small-world-ness metric could offer direct indication of a community’s balance of these relationship components and therefore the community’s relationship health.

Social network analysis may also offer tools for evaluating and managing community sustainability. Social network analysts define a “giant component” as “a large proportion of nodes that are reachable from each other” (Panzarasa et al., 2009). Emergence of a giant component represents a significant stage of community development as the existence of a giant component facilitates cross-network communication (Panzarasa et al., 2009), facilitating collective action such as adoption of innovations (Toole, Cha, & González, 2012). This suggests that measuring the existence and size (or relative size) of a community’s giant component may offer indication of a community’s position within the community life cycle. Additionally, modeling and simulation using maps of existing social networks could help quantify sustainability by identifying the number (and identity) of key nodes which, if removed from the network, could reduce the size of the giant component enough to compromise the network’s connectivity (Panzarasa et al., 2009) and lead to the demise of community social structure.


The effect of globally-accessible CMC on social groups confounds the definition of community and presents scholars and practitioners with a confusing array of community definitions based on groups bounded by various combinations of place, cyber-place, and personal attributes. More recent research, however, separates such groupings from “community” by defining community in terms of the sense of community felt among community members. This definition can be operationalized in terms of relationships, which, when combined with a prioritization of specific parties’ key success factors, yields a framework within which one can determine the significance of individual community metrics.

This paper examined a variety of metrics related to place and participation, but it’s important to note that very few metrics, if any, provide a single measure of “community health” or success. Outside academia, some companies have attempted to create such a measure by combining multiple metrics together into composite metrics. One example of this is the “community health index” or “CHI,” whose creator, the marketing company Lithium (“Online Community Management & CRM Solutions – Lithium,” n.d.), promotes in a white paper as being “like the FICO score, body-mass index (BMI), or standardized test scores…that [allow] communities to gauge their health in absolute objective terms.” While Lithium’s CHI takes into account a range of member, content, traffic, responsiveness, interaction, and liveliness-related metrics, it suffers from “corrections,” “smoothing,” and “normalization” via ratios with undocumented, consultant-generated constants, making it difficult to determine what (if anything) it measures “purely objectively.” “Fudge factor”-based metrics like CHI may prove useful for community managers who are primarily interested in the relative “health” exposed once Lithium dials in the constants for their community, but these metrics offer little to scholars seeking the absolute measurements of community health required to compare dissimilar communities or establish causal connections.

Future research should include additional identification, classification, and analysis of combination metrics, but more importantly operationalization and testing of social network metrics such as small-world-ness as insight into “cliquishness” and group bonding would provide a significant advantage for community managers seeking to maintain community sustainability.

[1] Though duplicate metrics have been removed, some very similar metrics remain as they represent potential different dimensions of or applications of measurement.

[2] Rafaeli & Sudweeks go on to further differentiate interactivity from “reactivity” in that reactive communications simply respond to other contributions where interactive communication “cites” past communication, carrying previous content forward in the discussion. Some advocate simpler measurements of interactivity like thread depth (Preece, 2001).

[3] Clustering coefficients are normalized to the clustering coefficient of a similar-sized random graph.

[4] The metric of clustering coefficient is remarkably similar to Burt’s composite constraint measure (Burt, 2005), different primarily in that composite constraint takes into account the weight of the ties and is applied to individual ego networks. Burt ties constraint to closure and therefore to bonding social capital, resolving similar results in terms of effect on the individual: belonging, trust, and facilitation of social norms.

[5] The mathematic specifics of the small-world-ness metric are beyond the scope of this paper.

[6] Some scholars dissent, suggesting that the importance of brokering behavior and structural holes may be mitigated in public forums “because all information is transparent and accessible” (Huffaker, 2010). However, this does not explain the enhanced connections observed between individual actors in public networks, like, for example, the observed propensity for increased participation among participants in interactive message chains (Rafaeli & Sudweeks, 1997).


Agarwal, N., & Liu, H. (2008). Blogosphere: research issues, tools, and applications. SIGKDD Explor. Newsl., 10(1), 18–31. doi:10.1145/1412734.1412737

Blanchard, A. (2004). Blogs as virtual communities: Identifying a sense of community in the Julie/Julia project. Into the blogosphere, 30. Retrieved from

Blanchard, A.L., & Markus, M. L. (2004). The experienced sense of a virtual community: Characteristics and processes. ACM SIGMIS Database, 35(1), 64–79.

Blanchard, Anita L. (2007). Developing a sense of virtual community measure. CyberPsychology & Behavior, 10(6), 827–830.

Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2011). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research. Retrieved from

Burt, R. S. (2005). Brokerage and Closure: An Introduction to Social Capital: An Introduction to Social Capital. OUP Oxford. Retrieved from

Butler, B. S. (2001). Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information systems research, 12(4), 346–362.

Casaló, L. V., Flavián, C., & Guinalíu, M. (2011). New members’ integration: Key factor of success in online travel communities. Journal of Business Research. Retrieved from

Dunbar, R. I. M. (2008). Cognitive constraints on the structure and dynamics of social networks. Group Dynamics: Theory, Research, and Practice, 12(1), 7.

Durant, K. T., McCray, A. T., & Safran, C. (2010). Modeling the temporal evolution of an online cancer forum. In Proceedings of the 1st ACM International Health Informatics Symposium (pp. 356–365). Retrieved from

Fichter, D. (2005). The many forms of e-collaboration: Blogs, wikis, portals, groupware, discussion boards, and instant messaging. Online, 29(4), 48–50.

Gruzd, A., Wellman, B., & Takhteyev, Y. (2011). Imagining Twitter as an imagined community. American Behavioral Scientist, 55(10), 1294–1318.

Huffaker, D. (2010). Dimensions of Leadership and Social Influence in Online Communities. Human Communication Research, 36(4), 593–617. doi:10.1111/j.1468-2958.2010.01390.x

Humphries, M. D., & Gurney, K. (2008). Network “small-world-ness”: a quantitative method for determining canonical network equivalence. PLoS One, 3(4), e0002051.

Iriberri, A., & Leroy, G. (2009). A life-cycle perspective on online community success. ACM Computing Surveys (CSUR), 41(2), 11.

Jones, Q. (1997). Virtual-Communities, Virtual Settlements & Cyber-Archaeology: A Theoretical Outline. Journal of Computer-Mediated Communication, 3(3), 0–0. doi:10.1111/j.1083-6101.1997.tb00075.x

Jones, Q., & Rafaeli, S. (2000). Time to split, virtually:’Discourse architecture’and’community building’create vibrant virtual publics. Electronic Markets, 10(4), 214–223.

Joyce, E., & Kraut, R. E. (2006). Predicting continued participation in newsgroups. Journal of Computer-Mediated Communication, 11(3), 723–747.

Kundu, S., Murthy, C. A., & Pal, S. K. (2011). A new centrality measure for influence maximization in social networks. In Pattern Recognition and Machine Intelligence (pp. 242–247). Springer. Retrieved from

Leimeister, J. M., Sidiras, P., & Krcmar, H. (2004). Success factors of virtual communities from the perspective of members and operators: An empirical study. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on (p. 10–pp). Retrieved from

Lento, T., Welser, H. T., Gu, L., & Smith, M. (2006). The Ties that Blog: Examining the Relationship Between Social Ties and Continued Participation in the Wallop Weblogging System. In 3rd Annual Workshop on the Weblogging Ecosystem (p. 12).

Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., & Tomkins, A. (2005). Geographic routing in social networks. Proceedings of the National Academy of Sciences of the United States of America, 102(33), 11623–11628. doi:10.1073/pnas.0503018102

Ludford, P. J., Cosley, D., Frankowski, D., & Terveen, L. (2004). Think different: increasing online community participation using uniqueness and group dissimilarity. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 631–638). Retrieved from

Matthews, T., Whittaker, S., Badenes, H., Smith, B. A., Muller, M., Ehrlich, K., … Lau, T. (2013). Community Insights: Helping Community Leaders Enhance the Value of Enterprise Online Communities. Retrieved from

McCully, W., Lampe, C., Sarkar, C., Velasquez, A., & Sreevinasan, A. (2011). Online and offline interactions in online communities. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (pp. 39–48).

McMillan, D. W., & Chavis, D. M. (1986). Sense of community: A definition and theory. Journal of community psychology, 14(1), 6–23.

Muller, M., Ehrlich, K., Matthews, T., Perer, A., Ronen, I., & Guy, I. (2012). Diversity among enterprise online communities: collaborating, teaming, and innovating through social media. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems (pp. 2815–2824). Retrieved from

Oldenburg, R. (1999). The Great Good Place: Cafes, Coffee Shops, Bookstores, Bars, Hair Salons, and Other Hangouts at the Heart of a Community (3rd ed.). Marlowe & Company.

Online Community Management & CRM Solutions – Lithium. (n.d.). Retrieved April 28, 2013, from

Overview | Pew Internet & American Life Project. (n.d.). Retrieved April 28, 2013, from

Panciera, K., Priedhorsky, R., Erickson, T., & Terveen, L. (2010). Lurking? cyclopaths?: a quantitative lifecycle analysis of user behavior in a geowiki. In Proceedings of the 28th international conference on Human factors in computing systems (pp. 1917–1926). Retrieved from

Panzarasa, P., Opsahl, T., & Carley, K. M. (2009). Patterns and Dynamics of Users’ Behavior and Interaction: Network Analysis of an Online Community. Journal of the American Society for Information Science and Technology, 60(5), 911–932.

Porter, J. (2008). Designing for the Social Web (1st ed.). New Riders.

Preece, J. (2001). Sociability and usability in online communities: determining and measuring success. Behaviour & Information Technology, 20(5), 347–356.

Prell, C. (2009). Linking Social Capital to Small-worlds: A look at local and network-level processes and structure. Methodological Innovations Online, 4(1), 8–17.

Rafaeli, S., Ravid, G., & Soroka, V. (2004). De-lurking in virtual communities: a social communication network approach to measuring the effects of social and cultural capital. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on (p. 10–pp). Retrieved from

Rafaeli, S., & Sudweeks, F. (1997). Networked interactivity. Journal of Computer-Mediated Communication, 2(4), 0–0.

Rheingold, H. (1993). The virtual community: Homesteading on the electronic frontier. Addison Wesley Publishing Company. Retrieved from

Rheingold, H. (1996). A slice of my life in my virtual community. High noon on the electronic frontier: Conceptual issues in cyberspace, 413–436.

Sarkar, C., Wohn, D. Y., & Lampe, C. (2012). Predicting length of membership in online community everything2 using feedback. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion (pp. 207–210). Retrieved from

Soroka, V., & Rafaeli, S. (2006). Invisible participants: how cultural capital relates to lurking behavior. In Proceedings of the 15th international conference on World Wide Web (pp. 163–172). Retrieved from

Steinfield, C., DiMicco, J. M., Ellison, N. B., & Lampe, C. (2009). Bowling online: social networking and social capital within the organization. In Proceedings of the fourth international conference on Communities and technologies (pp. 245–254). Retrieved from

Steinkuehler, C. A., & Williams, D. (2006). Where everybody knows your (screen) name: Online games as “third places.” Journal of Computer-Mediated Communication, 11(4), 885–909.

Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6), 420–433.

Toole, J. L., Cha, M., & González, M. C. (2012). Modeling the adoption of innovations in the presence of geographic and media influences. PloS one, 7(1), e29528.

Wang, L. (2010). How social network position relates to knowledge building in online learning communities. Frontiers of Education in China, 5(1), 4–25.

Wasko, M. M. ., Teigland, R., & Faraj, S. (2009). The provision of online public goods: Examining social structure in an electronic network of practice. Decision Support Systems, 47(3), 254–265.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world”networks. nature, 393(6684), 440–442.

Wellman, B. (1997). An electronic group is virtually a social network. Culture of the Internet, 179–205.

Wellman, B. (2005). COMMUNITY: From neighborhood to Network. Communications of the ACM, 48(10), 53–55.

Wellman, B., & Gulia, M. (1999). Net surfers don’t ride alone: Virtual communities as communities. Networks in the global village, 331–366.

Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer networks as social networks: Collaborative work, telework, and virtual community. Annual review of sociology, 213–238.

Whittaker, S., Terveen, L., Hill, W., & Cherny, L. (2003). The dynamics of mass interaction. In From Usenet to CoWebs (pp. 79–91). Springer. Retrieved from


Thank you! I have received your message.