2012 45th Hawaii International Conference on System Sciences
Categorizing Behavior in Online Communities: A Look into the World of Cake Bakers Dale Ganley Peter B. Gustavson School of Business, University of Victoria
[email protected]
Christine Moser Dept. of Organization Sciences, VU University Amsterdam
[email protected]
offline volunteer communities, online communities must include relatively stable groups of individuals who voluntarily engage in a variety of positive behaviors that keep the community functioning over time [15, 44]. Understanding the behaviors that manifest in online communities is a growing concern for management professionals. To date, there is limited work empirically addressing the types of behaviors that may be seen in online communities. In particular, the connections between online behavior and the positions that individuals occupy in a social network remain mostly in the dark. Network measures that shed light on implicit power relations are important, since most online communities refrain from adopting reputational measures. This article endeavors to fill that gap by providing an exploratory analysis into behavior patterns that emerge in online communities. The purpose of this article is to examine how behavior in online communities can be measured by observable factors. We contribute to literature on methodological approaches toward online community research [25, 30, 48]. Furthermore, we introduce a measure of community involvement we call ‘participant intensity’ that in our view is distinguished from other measures by revealing peoples’ connectedness to each other in an online community. Our hope is that the results will help websites interested in creating lively communities to encourage and manage high quality participation to the benefit of its online presence. To explore this problem, we develop a list of the observable measures and relational measures of community behavior (what we call behavior markers). We draw from literature on online behavior and social network theory to identify outcome behaviors and relational. We demonstrate how to calculate these behavior markers in an environment where some are explicit and some are hidden. Finally, we show how the behavior markers can be used to categorize participants in a way that may be helpful in practice and future research. This article is structured as follows. First, we provide a short review of research into online
Abstract In this article, we examine the patterns of behavior of online community members. We develop a list of measurable markers of behavior, drawing on online behavior and social network literature, and show how to construct the markers from the hidden relationship data in online communities. We then conduct an exploratory analysis of an online community devoted to cake baking in the Netherlands. Our results indicate that online community behaviors can be clustered into four distinct categories: utility posters, team players, low profiles, and story tellers, and that these behavioral roles are related to the commitment users have to the website. We provide an account of a novel methodological approach to elucidating social network markers of implicit relationships, using not survey but behavioral data. Finally, we discuss the implications of this line of inquiry for future work into behavior in online communities, and how to use this knowledge optimally in practice.
1. Introduction Online communities are a rapidly growing phenomenon that is being used to facilitate interaction and collaboration. More and more firms engage with social media for various purposes such as marketing, innovating, market research, or branding [39, 12, 16, 28]. More than 2 billion people worldwide use the Internet [Internet World 60], with 46% using social media [USC 4]. The implications for traditional businesses are immense: who would not want to engage with a potential 1 billion customers? A key factor of online communities is that they are dependent on individuals’ continued involvement for them to prosper. This is a critical issue in an online environment where the activities of the visitors may produce most of the website’s value, for little or no traditional compensation, and with extremely low switching costs to other alternatives [64]. Just like 978-0-7695-4525-7/12 $26.00 © 2012 IEEE DOI 10.1109/HICSS.2012.146
Peter Groenewegen Dept. of Organization Sciences, VU University Amsterdam
[email protected]
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exchange of organizational knowledge. Other topics that have been investigated by researchers in relation to online communities include entrepreneurship [20, 56, 37], innovation [21] and health care [58]. While different online communities may have varied goals, focus, and interests; identity formation and communication always play an important role in efficient information exchange and online relationship building [63]. A critical success factor of these communities is the social element in encouraging knowledge sharing, commitment and participation by volunteers. Social networks, explicit or not, provide a framework to facilitate interactions, build trust and loyalty, and create a community identity, leading to a more flexible and enduring [29] and more manageable community [7].
communities and discuss how this is related to theories of online behavior and social networks. Next, we describe in detail behavior markers and relational measures that are observable in online communities. Then we conduct an analysis of one online community, show how to extract the measures from the data available online, and illustrate the value of examining behavior markers with a cluster analysis of the results. We conclude with a discussion where we point out future research directions.
2. Literature Review 2.1. Online Communities In this paper we adopt the broad view of online communities that was introduced by Preece [51: 10] in her seminal work as consisting of
2.2. Social Networks Network research focuses on the relations between actors, and the patterns that can be detected [65]. Both an individual’s position as well as the overall structure of the network are important. Network theory has been employed in traditional settings such as collaboration [3], entrepreneurship [57] and in many studies of online communities [64, 22, 17, 18]. A measure that is extensively used in social network research is centrality of actors in any given network degree centrality expresses the number of ties an individual has with others in any given network; it can be conceptualized as a measure of popularity [65]. From a social network point of view, individuals with higher degree centrality have more ties, and hence a structural advantage [23]. In general prominent network positions are strategically beneficial [6, 46, 55, 59]. Another line of network research examines the consequences of structural holes, gaps between parts of a larger network. Burt [9, 10] presents the two dueling theories – brokerage vs. closure – and argues for the value to an agent of structural holes. He offers a measure called the constraint index (CI) of the extent of structural holes bridged by each agent. Brokers (low CI) have one of the few links across a structural hole. Their position enables them to establish novel information flows between actors. Closure (high CI) emphasizes a link pattern that is redundant with others and enables an in-depth exchange of information within a group of highly connected people. Burt suggests that closure is useful in small groups and teams to focus efforts on specific targets or goals, and brokerage is useful for broadening the network contributions to the wider circle without overloading it with too much
‘People, who interact socially as they strive to satisfy their own needs or perform special roles, such as leading or moderating. A shared purpose, such as an interest, need, information exchange, or service that provides a reason for the community. Policies, in the form of tacit assumptions, rituals, protocols, rules, and laws that guide people’s interactions. Computer systems, to support and mediate social interaction and facilitate a sense of togetherness.’ (Preece 2000) This definition includes a variety of communities that have been studied before under different names. For example, virtual communities have been defined as ‘cultural aggregations that emerge when enough people bump into each other often enough in cyberspace’ [53: 57-58]. Furthering this line of thought, Wellman & Gulia [67: 169] speak of computer-supported social networks, which ‘provide companionship, social support, information, and a sense of belonging.’ More recent work has focused on the concept of communities of practice [32, 8]. Here, groups of people interact around a shared interest or profession; interacting with peers facilitates learning and socialization in the group. The purpose of the communities suggests some of the motivations that drive the behavior of the participants. Some early work focused on a shared interest [40, 11, 49] or the interaction and communication between members [33, 35]. Much of the community of practice literature has been brought to the online domain as ‘networks of practice’ [64, 2], where the community serves as a platform for the
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Community building, labeled community citizenship behavior in Bateman et al. [5], is the ‘development and propagation of community norms and encouragement of socially appropriate conduct’. We include any type of activity, comments or actions by participants that are directed towards establishing a community identity, enforcing rules, or engaging in community goal setting. Some community members may have an explicit community-building role (i.e. moderators), and some community-building activities may be misguided or unwanted which is why we adjusted the name from community citizenship. Community support, labeled audience engagement in Bateman et al. [5], this is ‘the consumption and/or use of resources made available through the community’. We include any type of activity, comments or actions that interact with content posted by others or with the activity, comments or actions of others besides community building or content provision. The most common types of community support activities would be downloading or otherwise viewing content, engaging in light discussion with other participants, or otherwise distinguishing oneself from the invisible lurkers. Content provision – ‘the contribution of valuable resources in the form of posting information and/or knowledge for public consumption’ [5]. Any type of activity that directly contributes to the content value of the site would fall into this category.
information. Thus, both situations create positive environments in rich networks and they often exist in duality.
2.3. Online Behavior Research is beginning to examine the issues related to optimizing the behavior of participants in online communities in a variety of different settings. There are two dominant streams: one looks at the motivational antecedents of behavior [61, 13, 26, 54, 27, 67, 64, 63]. Another line of research examines the evidence of behavioral choices in the structure of relationships that emerges from the community, i.e. its social network [50, 47, 45, 22]. Researchers have defined different types of behaviors observed in online communities [34, 43, 31]. Related to the ideas of community of practice as mentioned above, Preece & Shneiderman [52] developed a framework that summarizes possible online community ‘careers’ in terms of social participation, and distinguish behaviors in terms of roles, e.g. reader, contributor or leader. Bateman et al. [5]. They discern three categories of behaviors, that are more generally applicable across all types of communities. First, community citizenship behavior as the ‘development and propagation of community norms and encouragement of socially appropriate conduct’ [6: 985]. Second, content provision is concerned with the addition of new information or knowledge to the existing resource base of the community. Third, audience engagement covers the actual consumption and usage of content provided by other community members.
3.2. Behavioral Markers We use the term “behavior markers” to indicate observable metrics of behavior patterns by participants on a social network site. In this paper, we base our discussion of the implications of the markers on the theoretically suggested motivations that may be in play, but the natural future extension of this work is to triangulate the markers and their theoretical support with interviews and surveys focusing on participant motivations and perceptions. In our research, we distinguish three primary groups of behavior markers. The first are markers that are formed from the quantity and timing of posts, personal information revealed, and any personal rating given by the site (like a reputation system). In other words, we use available information that is attached to the actual content of a post. The second group is derived from this content of the posts. The third is based on evidence of the relationships expressed as degree centrality, and thus based on characteristics of the social network. We will address each of these groups in turn; the full list is summarized in Table 1.
3. Measuring Behavior and Social Network Position The two central concepts this article considers to build its analysis on are online behavior and the way that behavior(s) can be observed and measured. In the following sections, we will first describe a useful method of behavior categorization and then the measures we can use to disaggregate broad behavior patterns into individual activities. We will complete this discussion with an explanation of how we operationalized the concepts within a case study sample.
3.1. Categories of Behavior We have synthesized the broad categories of behaviors that have been discussed in the literature into three categories as follows:
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constraint index recognizes that a small number of very intense links, especially to prominent nodes, can be very influential in network structure. Borgatti (1997) has shown that these two measures are highly correlated, so we confine our study to one measure based on the constraint index that we call participant intensity. We can describe this as the degree to which a user is focusing on a cluster of other users versus spreading their attentions around.
The first group of markers covers things that reflect personal information or decisions, which are embedded in the information that is attached to the actual content of a posting (such as number of posts, time stamp, personal statements etc.). One would expect commitment to be indicated if the visits are paced appropriately to the website, as opposed to bursting in and out, so we create a measure of the Visit Pace. Further, we have measures of the Personal info revealed. Some online community members decide to include personal information in their messages. Presence of such information might indicate different behavior as well as intentions. For example, a person who adds a promotional text to her messages (such as ‘the nicest shop in City X! The nicest workshops in the region!’) might have different objectives than a person who states her favorite cake citation (such as ‘Eating, sleeping, breathing cakes!’). The second group of markers is concerned with the content of the posts and is inspired by some recent work in content analysis and semantic maps [36, 38, 62]. Based on this literature, we create the markers of Post Length, Style Affect, and Embedded Info, as well as Directed Posts from interpersonal bonds. The length of a post might indicate how much time a person spends to create a post, as well as the thoroughness of the post and/or the dedication to the community. Style affect is concerned with the emotional side of messages. In online communities, affective content often is communicated in the form of emoticons, or smiley’s (such as -). Emoticons are used to ‘express emotion, strengthen a message, and to express humor’ [14: 99]. Therefore, the presence of one or more emoticons indicates that a message is more or less laden with emotions and/or humor. Whether or not community members include links to external information (what we label embedded info) or quote others (what we label directed posts) is also part of each individual profile. The third group of behavioral measures is concerned with the structure of the network. A social network perspective on behavior allows that an individual’s position in a network can be both an outcome and a predictor of certain behaviors. The network measures of social network theory serve to encapsulate characteristics of network position that have been shown to be relevant. We find two measures potentially applicable for our purpose: degree centrality and constraint index. The degree centrality counts the number of links to a node and is considered analogous to a measure of popularity. The constraint index has been proposed by Burt (2000) as a measure of the power and influence a node has relative to the number of links it has. In essence, the
Table 1. Overview of behavior markers Behavior Markers
Defined as
Visit Pace Personal Info Post Length Style Affect Embedded Info Directed Posts
Index of time between posts Inclusion of personal signature in post Number of characters in a post Number of “smiley” characters in a post Number of links in a post Percent posts that quote an earlier message Intensity of relationships links
Participant Intensity
4. Methodology 4.1. Setting of the study The studied community of Dutch cake bakers was founded by a cake fan in 2000. Since 2004, it is possible to register as a community member. This membership is voluntary and free of charge. The community has at the time of writing more than 15,000 members, although the most explosive growth only occurred during the last three years. Members communicate mainly through an asynchronous message board: messages can be left at any time, are archived, and other members can react at their convenience. At the time of writing, members left more than 1.5 million messages. Topics covered include techniques of cake baking and decorating, tips and tricks, how-to’s, social and professional support, sharing of knowledge and insider tips, diffusion of opportunities for workshops, and other cake-related issues.
4.2. Operationalizing the data We obtained data from the website on all posts in one sub-topic area that is the section focusing on sharing information and techniques. This area is the third-most frequented area, and thus can be assumed to be a reasonable area for investigation. Although it might be possible that this particular area hosts a subcommunity that differs from other sub-communities or the overall community in terms of online behavior, we assume that this is not the case. This assumption is based on the observation that most community
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large online communities where many, if not most, of the members participant with pseudo-anonymous status. Our solution is to focus on what can be observed. We have developed an alternative method to reveal the ‘hidden’ social network representing the probable awareness of ties between community users. We created a relationship tie between each pair of contributors in each thread, based on their cooccurrence at this particular thread similar to a standard affiliation network construction [19]. However, our methodological contribution is to vary the weights of the ties according to likelihood that the participants are aware of one another. We believe that in a site where thousands of individuals are interacting asynchronously on hundreds of threads, an unweighted affiliation network is fine for identifying users at the extremes of behavior. However, we feel our method will reveal the subtle relationships that would be hidden otherwise. To do the weighting, we assume that contributors to any thread on the site are likely to be (reasonably) aware of other contributors to the same thread before their post. We also assume that they are most aware of the contributor who posted the first message (since that interested them enough to read the thread) and the contributor who posted immediately before them (since that is the last thing they read before posting). We weigh these relationship ties most heavily, and then decay the weightings, as the posts are further apart in time and their positions in the thread. We further assume that the tie strength dies out (becomes 0) at a separation of 10 posts, which is the number of posts that is visible on each page. Thus, each affiliation relationship in a thread gets a value from 0 to 1, which is a directed weight of the relationship tie from the poster to those that posted before. We add up the relationship ties from each thread into a cumulative value for each relationship, thus creating the weighted network map of all the participants on the site. As discussed before the constraint index [10]1 measures the extent to which a person’s network time and energy is concentrated in just a few contacts. Participant Intensity is the constraint index calculated on the weighted network map of the active users, and thus should be a reasonable measure of the likelihood of a user having a relationship (at least awareness) with some self-selected subset of users
users are active in more than one particular sub-topic area, and thus engage in at least more than one subcommunity. The sub-topic has posts from Nov 6, 2004 to May 12, 2011 in 12726 threads. 4975 registered users have posted anywhere from 1 to 197 comments in each thread, with an average of 4.6 comments. A summary of the thread count over time is shown in Figure 1.
01-11
01-10
01-09
01-08
01-07
01-06
80000 60000 40000 20000 0
Messages
Figure 1. Thread count over time We calculated the measures of Visit Pace by counting the number of posts by each contributor by their unique Username and noting the time-stamp on each post. While it is probable that a few Usernames are secondary accounts for contributors, it is impossible for us to tell how prevalent this would be. However, we assume that it is not a large problem in our data analysis, since the owner of the website regularly cleans the database and deletes duplicate accounts that belong to the same email-address or user. Furthermore, Boolean values of whether or not contributors have set up a signature file at the time of each post are used for the measure of Personal Info. We analyze the content of each post for the count of characters for Post Length, the number of smiley characters (i.e. -, etc.) for the Style Affect, the number of links to external information for Embedded Info, and a Boolean value for the presence of a quote of another post for Directed Posts. This enables us to build a profile of each contributor’s style on average over time and compared to the site norms. Finally, we develop a measure from a social network analysis (SNA) of the relationships apparent in the website at a point in time. SNA is dependent on quantifying the relationship ties between each set of actors (contributors) active on the website. One of the largest stumbling blocks to SNA on social network websites is that the relationships are hidden; most websites do not have explicit listings of who is aware of whom. Much previous research develops an assessment of ties by conducting surveys of users [e.g., 24], but this has significant flaws especially in
1
The constraint index (CI) measures the extent to which the contacts of an individual i are directly or indirectly connected to a contact j, corrected for the size of the network: i(pij + qpiqpqj)2, for qI,j, where pij if the proportion of i's relations invested in contact j [10].
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beyond a random distribution. Table 2 shows descriptive statistics of all behavioral markers.
Table 3. Factor loadings on behavioral markers PD FR Visit pace .82 Participant .84 Intensity Personal Info .38 Post Length -.71 Style Affect .75 Embedded info Directed post Variance 51.8 18.4 Explained Note: PD = Pedant; FR=Friendly; ID=InfoDesk
Table 2. Descriptive statistics of behavioral markers Variable
Obs
Mean
Std Dev
Min
Max
Visit Pace (VP) Personal Info (PI) Post Length (PL) Style Affect (SA) Embedded Info (EI) Directed Posts (DP) Participant Intensity (PN)
830 830 830 830 830 830 830
33.7 .45 202.7 .36 .03 .08 .35
42.8 .49 82.7 .38 .12 .18 .20
.12 0 48.4 0 0 0 0
368.4 1 781.4 2.75 1 1 1
VP
P I
PL
SA
E I
DP
VP
1
P I
-.02
PL
.08
-.08
1
SA
.-.05
.05
-.20
1
EI
-.05
.02
-.01
.03
1
DP
-.08
.03
-.02
.02
.03
1
PN
.41
-.08
.19
-.02
-.07
-.02
ID
.65 .73 4.1
We then conducted a cluster analysis of the users according to their factor scores calculated from the above factors. We use a k-means analysis technique, choosing the number of clusters that maximizes the Calinski-Harabasz pseudo-F index as is standard, and create four clusters of contributors. Because the clusters are based on the factor scores of each contributor, the dominant profile of the clusters is based on the average factor scores in each cluster. We named each cluster based on a descriptive interpretation of its dominant profile. The resultant clusters, their profiles, and the number of contributors identified in each cluster are described in Table 4.
PN
1
1
4.3. Behavior clusters
Table 4. Four clusters of contributors
To align our behavior markers with the theoretical suggested characteristics and motivations of users, we conducted a factor analysis and clustering procedure on the website participants. The factor analysis will help condense the behavior markers into the core behavior patterns they might reflect, and the clustering will help to identify the behavior patterns that go together to form the roles that individuals tend to play. We assume that the clusters we identify will at least in part reflect the observations of behavior described by Bateman et al. [5]. We extracted the subset of ‘Current Active’ users, defined as having at least four posts and at least one since January 1, 2011, from our message sample that resulted in 830 observations. An exploratory factor analysis was conducted on this dataset to find evidence of convergent validity of the behavioral markers. Table 3 presents the high (absolute value greater than .60) factor loadings using principal components analysis with varimax rotation. The total variance explained is 74.3 percent. The loadings on each ‘parent’ factor suggest interpretations of the factors according to their dominant behavior markers. We labeled the them ‘Pedant’ (visit pace and participant intensity), ‘Friendly’ (personal info, post length and style affect) and ‘InfoDesk’ (embedded info and directed post’).
Cluster Utility Posters
Description
Number of Members 226
PD (Low) FR (Low) ID (Low) Team Players FR (High) 348 ID (High) Low Profile PD (Med) 200 FR (Med) ID (Med) Story Tellers PD (High) 56 FR (Low) Note: PD = Pedant; FR=Friendly; ID=InfoDesk
It should be noted that we focus on active community members since we want to categorize actual behavior. Therefore, we exclude two groups of individuals from our theoretical reasoning and empirical investigation: Lurkers (individuals who read posts but never participate) and Ghosts (who we define as individuals who contribute a very short time then disappear). Although the literature recognizes the role of lurkers in particular in online communities [41, 42], their role is limited where social interaction is concerned (outside of advertisement revenue). More important is the interaction between registered or visible community members – their behavior - as this eventually establishes a stable environment.
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These individuals post three or fewer times, and then disappear. They were not included in the above sample, simply because they post so little that it is not possible to extract any meaningful behavior markers. While they constitute the largest percentage of users, they have a low percentage of posts. The second category is the Super Users, who are the opposite of the Ghosts. These are the users who post most often and are most recognizable (at least by screen-name) to any other user. The Super Users all fall into the Team Player Cluster. Figure 4 shows a breakdown of all posters including these additional categories.
5. Results Table 4 presents the results of the cluster analysis of active users. Each of the groups presented in the four-cluster model represents a dominant role played by that user during the time period of the analysis. These results give support to our initial line of inquiry, which asked whether contributors to an online community can be grouped according to dominant behavior patterns. Approximately 27% of the users were classified into cluster 1 (Utility Posters). These are the individuals who post on the site at a higher rate than others but do not appear to make an effort to interact with others. They contribute to the site by engaging in content building, but not community building or community support. Approximately 42% of the individuals were classified into cluster 2 (Team Players). These individuals focus on making short but light comments. This role clearly falls into the community support category. Approximately 24% of the individuals were classified into cluster 3 (Low Profile). These are individuals who rank in the middle of all three factors, emphasizing none of the roles remarkably, but also not neglecting any of them. This cluster may be doing a little content building, community building or community support at any given time. Finally, approximately 7% of the individuals were classified into cluster 4 (Story Telling). These individuals tend to have a slow post rate, longer than average posts and a tighter network of influence. They are the people who focus on Content Building in narrow topics. They can also be the individuals who guide the Community Building efforts although they tend to leave relationship building to others. In Figure 2 we show a graphical representation of the breakdown of posters on the site since Jan 1, 2011. We describe two more categories of posters who are useful to consider for contrast purposes. The first category is the largest one, the Ghosts.
1% 21% Ghosts Low Profile Utility Posters Story Tellers Team Players Super Users
15%
13%
Figure 3. Breakdown of all posters since Jan 1, 2011 Based on Figure 3, we offer a comparison of some of the other measures we gathered by the group contributions. In the top row of Table 5, we list the percentage of posts that originate from each type of user. It is clear that if one views post count as the most basic measure of valuable content on the site, the Team Players are the biggest asset to the site. Even though Ghosts are most numerous, the activities of the Team players dominate the site. Table 5. Statistics on posting behavior Factor Posts First Posts Second Post Reply Posts Link Posts
Total 7972 1771 1754 689 310
Utility 18% 19% 17% 15% 12%
Team 61% 43% 69% 73% 82%
Low 7% 12% 5% 5% 3%
Story 1% 2% 1% 3% 1%
Ghosts 13% 23% 8% 8% 4%
However, Ghosts make about 23% of the first posts (i.e. they start threads) even though they only allow them to ‘support’ the poster of the original message in some manner. Team Players are also the individuals who make the most direct replies to others by the count of replies, but part of the ‘definition’ of the Story Tellers is that they have a disproportionate rate of responses to their few members. The numbers do show that they represent 3% of responses even though they only make about 1% of the posts.
7% 27% 24%
46%
4%
Utility posters Team Players Low Profile Story Telling
42%
Figure 2. Breakdown of posters from sample since Jan 1, 2011
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categories do not completely correlate with categories commonly used such as lurkers, we might speculate to what degree a viable community is supported by, or spawns a certain distribution of, members across the four categories. As representatives of these categories engage in distinct ways in all three of the community-relevant behaviors, it might be beneficial to consider needs and preferences of users when building an online community. For example, team players might value a user interface that enables the composition of quick, animated messages, whereas storytellers might emphasize a sophisticated archival system. In other words, an online community might be more successful when design and functionality are adjusted to the needs and preferences of its user categories. This study has some limitations that should be addressed. First, our sample of behavioral markers is relatively small compared with the size of the overall community. Future research will further engage in more extensive longitudinal analysis and deeper explorations of potential behavior markers. However, we believe that this sample is a sound, if simple, representation of interaction in this particular community. Second, for the purposes of this preliminary study we confined ourselves to measures available from a top-level analysis of the website. We are exploring the possibility of incorporating richer metrics of behavior through content analysis as well as demographic and regional information on community members. Finally, we are encouraged by the demonstrated value of observable behavioral measures as reflecting the social interaction and communication in online communities. Future work will focus on bringing the perspectives offered by survey and interview methods together with the data analysis methods we have explored in this paper.
Finally, we find it interesting that the Team Players make so many of the posts with embedded information (links to other websites), even though, again, that is not specifically part of their profile.
6. Conclusion The purpose of this paper is to examine the patterns of behavior of online community members with regard to activity, network embeddedness, as well as the clustering of these patterns into categories. Three behavioral categories emerged from the data thus grouping behavior as pedant, friendly or information oriented. Subsequently, patterns of these behaviors led us to suggest that members in a community can be classified in four categories. First, team players as the largest group were characterized by a high degree of both friendliness and information sharing. They therefore engage in community support, and typically post short but friendly comments. The second largest group, utility posters, featured low degrees of all observed categories. They mainly engage in content provision by posting a lot but refraining from too much social interaction. The third group, low profile, was observed to have medium degrees of all categories. Finally, the storytellers were found to have a high degree of visit pace and participant intensity (pedant), but a low degree of friendliness. Their main goal is to contribute content and build the community. These three categories suggest further research and theoretical development. It is necessary to assess whether such categories are similar in other online communities. Furthermore, research needs to address the question of life cycles of online community members: do members change behavior over time, and would they then become part of another category? How would this individual life cycle affect the larger community over time? How do transitions from stable to unstable and vice versa take place, and what are the underlying mechanisms? We contribute to social network methodology by offering a measure of Participant Intensity, based on the constraint index, that takes into account not only centrality of individuals in the network, but also degrees of awareness which might influence online behavior. We find that this measure indeed is empirically significant and distinct from the other measures we employed. Future research should address the need for more refined network measures in the domain of online communities, and further develop this avenue of thought. From the research on our cake baking community, we also know that it is lively and growing. As our
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