Moderation, Response Rate, and Message Interactivity: Features of Online Communities and Their Effects on Intent to Participate

We conducted two experiments to explore how moderation, response rate, and message interactivity affected people’s intent to participate in a web‐based online community. In our first experiment, 62 participants observed either a moderated or an unmoderated online community and answered questions about their intent to participate in the community. The participants who viewed the moderated community reported significantly higher intent to participate than participants who viewed the unmoderated community. In our second experiment, 59 participants observed a different online community in which we manipulated both the rate (in time) of posted comments and the interactivity of each comment. We derived our manipulation of interactivity from Rafaeli’s (1988) definition of interactivity as message contingency. Participants reported significantly greater intent to participate in an online community featuring interactive messages, but only when response rate was slow. These results indicate that both structural features of interfaces and content features of interactions affect people’s intent to participate in online communities.     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

Polynomial Time Learnability of Simple Deterministic Languages

This paper is concerned with the problem of learning simple deterministic languages. The algorithm described in this paper is based on the theory of model inference given by Shapiro. In our setting, however, nonterminal membership queries, except for the start symbol, are not permitted. Extended equivalence queries are used instead. Nonterminals that are necessary for a correct grammar and their intended models are introduced automatically. We give an algorithm that, for any simple deterministic language L, outputs a grammar G in 2-standard form, such that L = L(G), using membership queries and extended equivalence queries. We also show that the algorithm runs in time polynomial in the length of the longest counterexample and the number of nonterminals in a minimal grammar for L.     

Distributed Apprenticeship in Online Communities

Supporting learning in online communities is an important direction for the future of human–computer interaction as people increasingly leverage social technologies to support professional growth and development. However, few have studied how people leverage the socio-technical affordances of online informal workplace communities to develop professional skills in the absence of dedicated expert guidance. We draw from theories of apprenticeship to introduce an emergent theory of distributed apprenticeship, which outlines how community expectations of transparency and mutual support allow for instruction to be directed by a distributed network of nonexperts. We develop distributed apprenticeship through a qualitative study of crowdfunding entrepreneurs, where novices leverage social interactions with community members to develop a wide range of entrepreneurial skills. We then generalize distributed apprenticeship to other workplace contexts and provide design implications for online communities where people develop professional skills with minimal dedicated formal guidance.     

Netnography as a Method of Lead User Identification

Lead users are rare subjects, which are difficult to detect. In theory and practice, mass screening is the main method of lead user identification. It is a standardized, quantitative approach, based on screening a large number of potentially relevant users. Shortcomings of screening are low sample efficiency, high search costs and the reliance on the self‐assessment of respondents. Thus, the elaboration of lead user identification methods is still a major challenge to researchers in the field. In this paper, we propose netnography as a new method of lead user identification. Netnography, made up of internet and ethnography, is an approach to analyse online communities systematically. The empirical results of our explorative study of the online community utopia show that 9 out of 40 of the most active online community members possess lead user attributes (22.5 per cent). Hence, we may conclude that netnography is a viable method of lead user identification, which relies on external assessments instead of self‐assessments and is probably less costly than mass screening.     

Community building and knowledge sharing by individuals with disabilities using social media

The use of social media to share information, enhance learning, and connect with an online community has grown rapidly over the past 10 years. As social media becomes a more common tool in both formal and informal education, it is imperative to understand how it is used by individuals with disabilities. Through a systematic study of the literature, 215 articles on social media used by individuals with disabilities were selected and 29 selected for in-depth thematic analysis. Six major themes were identified: community, cyberbullying, self-esteem, self-determination, access to technology, and accessibility. To confirm these six categories, we expanded our search, yielding an additional 30 articles, for a total 59 articles reviewed in-depth. Interactions between individuals with disabilities within online communities often had the goal of acquiring knowledge or learning new information. A communities of practice theoretical framework is used to discuss interactions among the elements of social media design, learning, and the building of community by individuals with disabilities.     

Becoming Friends in Online Brand Communities: Evidence From China

With the aid of information technology, consumers have increasingly engaged in social interaction in online brand communities. How can these strangers make friends online? Drawing on embeddedness theory and media richness theory, we examine the antecedents and intermediate mechanisms of online friendship. We theorize that online brand community interactivity aided by instant messaging technology is the main driving force of online friendship, whereas social presence and a sense of yuan (a Chinese concept describing predetermined relations) mediate online friendship development. Online friendship in turn enhances consumer online brand community commitment. We test our conceptual model with a sample of consumers from Chinese online sporting goods forums. The results support our hypotheses and inform online brand community research and practice.     

Bayesian community detection

Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled.     

An SNS-based model for finding collaborative partners

This paper proposes a model, Recommendation of Appropriate Partners (RAP), used on a Social Networking Service (SNS) for locating appropriate “helpers” for users based on individual users’ Chain of Friends (CoF) relationships. Using the RAP model, individual users can participate in a collaborative online community in remote locations, whereby helpers are willing to help other users solve their tasks/problems, and it is intended that both the users and helpers gain knowledge from these interactive online sessions. An example of the RAP-based system was implemented to invite Program Committee members to an international conference. The system was evaluated and the experimental results show that our model is very effective for discovering collaboration partners and finding users with similar interests in order to create communities for providing future and longer-term helping exchange.

     

Community building among deaf and hard of hearing people by using written language on social networking sites

Since D/deaf and hard-of-hearing users of social networking sites (SNSs) may have communication specificities in comparison to hearing people, we proposed a model for understanding what factors affect building online communities. The model includes written language skills, the frequency of written communication, online Deaf and hearing identity, and the tendency for community building. One-hundred-and-sixty-two German D/deaf and hard-of-hearing users completed an online questionnaire in German sign and written language. Evaluation of the model with structural equation modelling revealed three main findings. Firstly, identification with the hearing online world has a positive effect on written language skills, the frequency of written communication on SNSs and indirectly on the tendency to build online communities. Secondly, the frequency of written communication has a positive effect on the tendency to build community. Thirdly, a positive effect of online Deaf identity on the frequency of written communication was found. Our findings may aid in understanding that, despite possible technological constraints, both D/deaf and hard-of-hearing people communicate on SNSs in written language more frequently due to their identification either with the Deaf or hearing online world which results in an increased tendency to build online communities.     

Influences of Web interactivity and social identity and bonds on the quality of online discussion in a virtual community

People participate in virtual communities (VCs) for knowledge sharing or social interaction. However, most studies of VCs have focused on elucidating knowledge sharing rather than predicting virtual social interactions. This study considers “quality of online discussion” an appropriate metric for assessing group-level outcomes of virtual social interactions, and thus for predicting member willingness to sustain an ongoing relationship with a virtual community (VC). This study develops a research model, grounded in Web interactivity, social identity and social bond theories, for predicting the quality of online discussion in terms of cognitive and social influences. Empirical results from an online survey of a VC verify distinct direct and indirect social influences (perceived internalization bonds and perceived identification bonds) and cognitive influences (perceived communication and perceived control). Implications for academics and practitioners are also discussed.     

Supporting children's online identity in international communities

Increasingly, adults and children socialise and communicate online. Children's safe and secure online communication with people from all over the world can increase their understanding of other cultures, which is an important goal in today's multicultural world. Our research studied such interactions between children from three countries, Hungary, Mexico, and the USA. The goals of our research were to study how children represent their identity online and what the implications are for the design of children's online communities. We used qualitative methods to derive a deep understanding of children's behaviours and motivations. Our results show that children exposed their true and complete identities online. They focused on sharing and learning about personal, ethnic, and gender identities via online media and largely ignored cultural identity. They learned about children from other countries and developed positive attitudes towards them. Based on our results we describe design guidelines for children's online identity tools.     

Cross-domain community detection in heterogeneous social networks

With the recent surge of location-based social networks (LBSNs), e.g., Foursquare and Facebook Places, huge amount of human digital footprints that people leave in the cyber-physical space become accessible, including users’ profiles, online social connections, and especially the places that they have checked in. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. Meanwhile, unlike social networks which only contain a single type of social interaction, the coexistence of online/offline social interactions and user/venue attributes in LBSNs makes the community detection problem much more challenging. In order to capitalize on the large number of potential users/venues as well as the huge amount of heterogeneous social interactions, quality community detection approach is needed. In this paper, by exploring the heterogenous digital footprints of LBSNs users in the cyber-physical space, we come out with a novel edge-centric co-clustering framework to discover overlapping communities. By employing inter-mode as well as intra-mode features, the proposed framework is able to group like-minded users from different social perspectives. The efficacy of our approach is validated by intensive empirical evaluations based on the collected Foursquare dataset.     

Knowledge Sharing in Virtual Communities: A Study of Citizenship Behavior and Its Social-Relational Antecedents

A virtual community is a type of online structure that enables Internet users to communicate and collaborate. Users' knowledge contributions are critical to the viability and sustainability of virtual communities. This article studies virtual community members' knowledge sharing from the perspective of citizenship behavior defined as members' spontaneous contribution to the community without expectation of return or reciprocation. The social-relational antecedents of citizenship behavior are explored through an examination of how members' general attitude and desire for relationship building and maintaining, including attachment motivation, social support orientation, and disposition to trust influence their trusting beliefs and citizenship knowledge-sharing behavior. Hypotheses are developed and tested with survey data from Chinese and American users of virtual communities. In general, the results of data analyses support our research model. This article contributes empirically to virtual community research and has practical implications for virtual community development.     

Mapping virtual communities by their visual productions: The example of the Second Life Steampunk community

In the digital age, the identities and structures of virtual communities develop outside the traditional definitions of geography and physical constraints. Among the best models to study the identification process in virtual communities are the communities of fans of imaginary universes. Steampunk—neo-Victorian fiction with a science fiction twist—has for instance given rise to a large community, which is very active both in real life and in virtual spaces, for instance in the online 3D immersive platform Second Life. By collecting and analysing hundreds of visual artifacts generated by members of the steampunk community of Second Life, we found a repertoire of visual and lexical characteristics with which to identify the community. In addition, examining the characteristics displayed by visual productions from other communities allowed us to map relationships between different communities based on their aesthetics. This data suggests that the quantification of visual clues points out to interlocking relationships between different community aesthetics, forming a network where differences are visible, but fluidity still dominates. Furthermore, our results could serve as a model to study how communities in different media (immersive universes, games, social media based on visual materials such as Pinterest or Instagram) generate their own visual identity.     

Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

An online learning community enables learners to access up-to-date information via the Internet anytime–anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities.     

Predicting continuance in online communities: model development and empirical test

Popular interest in online communities has grown rapidly in recent years as a result of the widespread diffusion of Web 2.0 applications. However, the full values and potential of online communities cannot be realised without users' ongoing participation. Thus, this study aims at developing and empirically testing a research model to examine users' continuance intention to participate in an online community based on an extended information systems (IS) continuance model. Specifically, entertainment value and affective commitment are included in the IS continuance model and empirically examined in the context of online communities. A total of 240 returns collected from an online survey, which was conducted among users of a website bulletin board-based community in China, were analysed using partial least squares. The results reveal that users' continuance intention to participate in an online community is determined by both satisfaction and affective commitment. Satisfaction and affective commitment are, in turn, influenced by positive disconfirmations of purposive and entertainment values. The findings of this study contribute not only to theory building in online community continuance but also inform online community moderators in their effort to develop strategies for retaining their users.     

Guidelines for facilitating the development of learning communities in online courses

Online learning has grown exponentially in recent years; however, dropout problem remains challenging for some online programmes. The dropout problem can be attributed to a number of reasons, with a lack of interaction between learners and the instructor constituting one of the main reasons. The lack of interaction also leads to learners' feeling of isolation. Learning communities can provide learners with an environment conducive to increased interactions and alleviate their feeling of isolation. Unfortunately, there are no clear rules that instructors can follow to help learners create learning communities. In this paper, we propose guidelines for online instructors to facilitate the development of learning communities in online courses. We first review the definition of a learning community, importance of a learning community and factors affecting the development of a learning community. Afterwards, based on a review of the existing guidelines and other relevant literature, we propose guidelines for facilitating the development of learning communities in online courses.     

A joint model for analyzing topic and sentiment dynamics from large-scale online news

Many of today’s online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: (1) The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. (2) The user interactions among news comments can easily lead to inaccurate topic extraction and sentiment classification. In this paper, we propose a novel probabilistic generative model (DTSA) to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. In DTSA, three different timescale models are studied to account for the historical dependencies of sentiment-topic word distributions at current epoch, continuous, skip and multiple timescale models. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. In order to mine more interpretable topics, a Conditional Random Fields (CRF) model is adopted to label a set of meaningful phrases for augmenting the bag-of-word features. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.     

How media choice affects learner interactions in distance learning classes

In this research we explore aspects of learning, social interaction and community across online learning, also known as distance learning, in higher education. We measure the impact of online social networking (OSN) software versus traditional learning management system (LMS) software. Guided by a theoretical model for how individuals learn and interact within online communities, we measure student perceptions of learning, social interaction and course community before and after our interventions. Survey instruments measure perceived learning, social interaction and community, which we further explore using social network analysis (SNA). Survey results identified that students experienced higher levels of perceived social interaction and course community and, overall, had higher levels of satisfaction with OSN software than those using LMS software. Along this line, SNA results corroborated that OSN software yielded a higher number of interactions, providing a more engaging learning experience.