Although knowledge workers are increasingly able to adopt remote and hybrid working arrangements and work productively, many organizations continue to question the effectiveness of remote work and focus on its concerns and challenges. Previous CSCW research shows that remote workers have limited awareness of other workers, require more explicit coordination, and feel excluded from in-person colleagues. Research also shows that adopting work practices and technologies that are remote work-friendly can offset many of these challenges. Identifying which effective practices and challenges are most helpful or hurtful to remote workers-and how workplace attributes (e.g., team structure; communication frequency; tool use) affect them-could strengthen organizations' strategies and policies for remote work. Through a theoretically-informed survey of 1,526 U.S. knowledge workers, we find many factors prior research has argued as essential to remote work, such as knowing your teammates personally, to be the least important for remote workers, and show how workplace attributes influence those perceptions. We provide theoretical and practical implications for future research for organizations that wish to support remote and hybrid work modalities.

Enterprise collaboration technologies (ECTs) are increasingly recognized for supporting effective and efficient digital collaboration, such as decision-making activities, among employees. Given the social and collaborative nature of ECT use, social network theory offers important and helpful insights into how and why employees' social network relations facilitate their ECT use. However, existing research primarily examines the effects of a single social network relation or several social network relations separately, without applying a holistic approach to investigate the joint effect of multiple social network relations on ECT use. Drawing on a novel technique of fuzzy-set qualitative comparative analysis (fsQCA) and social network analysis, this study explores how multiple social network relations (i.e., advice, friendship, and communication) collectively influence ECT use. Using multi-source data from 178 employees in the human resources department of a global technology company, we identify several configurations of multiple social network relations associated with high ECT use and low ECT use. Our findings indicate that a single social network relation is insufficient to explain ECT use and should be considered alongside other social network relations. Overall, this study provides an integrative framework to unpack the complex and contingent effects of multiple social network relations on ECT use.

In this paper, we analyze the factors that are most likely to explain the formation of friendship and advice ties among 44 students from a professional STEM graduate program. To answer our research questions, we investigate how students' characteristics influence the formation of their friendship and advice networks using descriptive network analysis, community detection, and Exponential Random Graph Models. The results show that the formation of friendship and advice ties is mostly driven by demographic homophily and prior group activities. Our findings also suggest that female students were more constrained in their friendship and advice networks than male students. We discuss the implications of these results for how graduate students' social networks form at the beginning of their program.

Despite the benefits of team diversity, individuals often choose to work with similar others. Online team formation systems have the potential to help people assemble diverse teams. Systems can connect people to collaborators outside their networks, and features can quantify and raise the salience of diversity to users as they search for prospective teammates. But if we build a feature indicating diversity into the tool, how will people react to it? Two experiments manipulating the presence or absence of a "diversity score" feature within a teammate recommender demonstrate that, when present, individuals avoid collaborators who would increase team diversity in favor of those who lower team diversity. These results have important practical implications. Though the increased access to diverse teammates provided by recommender systems may benefit diversity, designers are cautioned against creating features that raise the salience of diversity as this information may undermine diversity.

The emergence of team-assembly technologies has brought with it new challenges in designing and implementing socio-technical systems. Our understanding of how systems shape the team-assembly processes is still limited. How do systems enable users to find teammates? How do users make decisions when using these systems? And what factors explain the characteristics of the teams assembled? Building on existing literature from CSCW, computer science, and management science, we propose a taxonomy to characterize how systems influence team assembly. This taxonomy argues that two dimensions determine how systems shape team assembly: (i) users? agency, to what extent the system enables its users to exercise their agency, and (ii) users? participation, how many users the system allows to participate in the team-formation process. The intersection of these two dimensions manifest four types of teams enabled by systems: self-assembled teams, staffed teams, optimized teams, and augmented teams. We characterize each one of these types of teams, considering their qualities, advantages, and challenges. To contextualize these types of teams, we map the current literature of team-assembly systems using a scoping literature review. Lastly, we discuss ways through which these two dimensions alter users' behavior, team diversity, and team composition. This paper provides theoretical implications and research questions for future systems that reconfigure the organization of people into teams.

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.

People and organizations are increasingly using online platforms to assemble teams. In response, HCI researchers have theorized frameworks and created systems to support team assembly. However, little is known about how users search for and choose teammates on these platforms. We conducted a field study where 530 participants used a team formation system to assemble project teams. We describe how users' traits and social networks influence their teammate searches, teammate choices, and team composition. Our results show that (a) what users initially search for differs from what they finally choose: initially they search for experts and sociable users, but they are ultimately more likely to choose their prior social connections as their teammates; (b) users' decisions lead to non-diverse and segregated teams, where most of the expertise and social capital are concentrated in a few teams. We discuss the implications of these results for designing team formation systems than promote users' agency.

The field of Computer Supported Cooperative Work (CSCW) has an enduring interest in studying and designing technologies that bring people together in partnerships, teams, crowds, communities, and other collectives. As the technologies enabling group formation have evolved, so too have the guiding questions pursued by CSCW scholars. This review outlines the trajectory of scholarship on group formation with an eye towards the most pressing future questions in this area. To understand how CSCW researchers have studied technology-enabled group formation, we systematically review articles published at CSCW from 1992 to 2018. Exploring more than 2,000 potentially relevant works, we identified 35 focused on technologies and group formation. Content coding and thematic analysis revealed four periods and six themes in the study of online group formation. These themes include: group composition, self-presentation, assembly mechanisms, recruitment, organizing structures, and group culture. Quo vadis? Based on our review, we offer recommendations for the next generation of CSCW scholarship seeking to understand and enable collectives joining together online.

Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

Fraudulent cash-out is an increasingly serious problem in China, which costs financial facilities billions of dollars. Unlike most of the well-studied credit card fraud, where only one party illicitly seeks financial gain, fraudulent cash-out involves both parties of the transaction. When prior information, such as credit score and reputation score, about the majority of consumers and shops is available, the phenomenon can be readily analyzed by using the Markov random field models. In this paper, we investigate the detection of fraudulent cash-out under the circumstance where no prior information but only the labels of a small set of consumers and shops are available. The novelty of this work is building a semi-supervised learning algorithm that automatically tunes the prior and parameters in Markov random field while inferring labels for every node in the graph. We evaluate our algorithm with data from JD Finance.

Networks evolve at multiple levels; edges or relations may change, and the characteristics of the individuals within the network may change as well. Often these processes are intertwined, and in order to study them, statistical models must be developed that account for coevolution of multiple network components. The increased availability of continuous time network data has prompted new models for network inference such as the relational event framework. While network data may be continuously observable, the characteristics or state of an individual can still only be observed periodically. This type of panel data can be readily analyzed using actor-oriented models, but these methods do not accommodate continuous network data. We propose a model that integrates the relational event framework with actor-oriented models for behavioral change, allowing us to model the joint dynamics of relational events and individual states. This composite model preserves the advantages of each method, while leveraging the richer information available in relational event data. We apply our model to datasets collected from virtual team experiments to highlight the utility of our method.

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within--subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.

The term 'Gold Farmer' refers to a class of players in massive online games (MOGs) involved in a set of interrelated activities which are considered to be deviant activities. Consequently these gold farmers are actively banned by game administrators. The task of gold farmer detection is to identify gold farmers in a population of players but just like other clandestine actors they not labeled as such. In this paper the problem of extending the label of gold farmers to players which are not labeled as such is considered. Two main classes of techniques are described and evaluated: Network-based approaches and similarity based approaches. It is also explored how dividing the problem further by relabeling the data based on behavioral patterns can further improve the results

Team collaboration in multi-player online games provides opportunities for players to interact with each other. Facilitating teams has become one of the main design principles to increase social activities. However, there is no research evidence that collaborating on tasks in game teams can produce the desired relational outcome. This paper examines more than half a million solo and team activities during a week in Dragon Nest, an MMO game. We measure the degree of team engagement using the percentage of time played in teams and the percentage of play with repeated teammates, and we then identify different types of players using this. The results show that solo players and team players are two distinct populations and they are highly predictable based on players' in-game status. Moreover, we find that spending more time in teams does not always lead to more social interactions. The interviews with players are conducted to validate the findings.

This study investigates unfollow behavior in Twitter, i.e. people removing others from their Twitter following lists. Considering the interdependency and dynamics of unfollow decisions, we use actor-oriented modeling (SIENA) to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. Focusing on ordinary users in tightly-knitted user groups, the results show that relational properties play key roles in the emergence of unfollow behavior: mutual following relations and common followees reduce the likelihood of unfollowing. And unfollow tends to be reciprocal: when a user is unfollowed by someone, he or she will unfollow back. However, there is no evidence of the impacts of homophily based on common interests and informativeness of interactions. The findings suggest that Twitter has many heterogeneous user groups and relational and informational factors may not be applicable universally.

The proliferation of digital knowledge repositories (DKRs) used for distributed and collocated work raises important questions about how to manage these technologies. This study investigates why individuals contribute information toDKRsby applying and extending transactive memory theory. Data from knowledge workers (N = 208) nested in work groups (J = 17) located in Europe and theUnitedStates revealed, consistent with transactive memory theory, that perceptions of experts' retrieval of information were positively related to the likelihood of information provision toDKRs. The relationship between experts' perceptions of retrieval and information provision varied from group to group, and cross‐level interactions indicated that trust in how the information would be used and the interdependence of tasks within groups could explain that variation. Furthermore, information provision toDKRswas related to communication networks in ways consistent with theorizing regarding the formation of transactive memory systems. Implications for theory and practice are discussed, emphasizing the utility of multilevel approaches for conceptualizing and modeling why individuals provide information toDKRs.

Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p*/ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia.

The advance of the Internet in the past decade has radically changed the way people communicate and collaborate with each other. Physical distance is no more a barrier in online social networks, but cultural differences (at the individual, community, as well as societal levels) still govern human-human interactions and must be considered and leveraged in the online world. The rapid deployment of high-speed Internet allows humans to interact using a rich set of multimedia data such as texts, pictures, and videos. This position paper proposes to define a new research area called ‘connected multimedia’, which is the study of a collection of research issues of the super-area social media that receive little attention in the literature. By connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and explicitly exploiting the cultural differences. We justify why it is necessary to bring attention to this new research area and what benefits of this new research area may bring to the broader scientific research community and the humanity.

The goal of this study is to understand how consultants' information seeking from human and digital knowledge sources is influenced by their relationships with both types of knowledge sources and the characteristics of the knowledge domain in which information seeking takes place. Grounded on and extending transactive memory theory, this study takes a multidimensional approach to predict consultants' information seeking based on expertise recognition, source accessibility, peer information-seeking behaviors, knowledge complexity, and codifiability. Using data collected from 110 consultants across 9 project teams from 2 multinational consulting firms, this study found that consultants' information seeking from human knowledge sources was mostly driven by the expertise and accessibility level of their team members, whereas their information seeking from digital knowledge repositories was strongly influenced by how much information the digital knowledge source had and whether colleagues with whom they had strong social communication ties were seeking information from the digital source. Finally, knowledge complexity had a negative influence on consultants' information seeking from digital knowledge repositories, but knowledge codifiability had no significant effects on information seeking from either knowledge source. This study demonstrates the importance and viability of using a multidimensional network approach to advancing transactive memory theory to study consultants' information-seeking practices.

The mobile space is evolving at an astonishing rate with over 4.1 billion subscribers in existence. The world is also witnessing an explosion in social web services with more users seeking novel ways of interacting with friends and family. We are interested in the combination of these two exciting research spaces: the social web and the mobile web. We believe that the social mobile web is going to be a highly influential research area in the near future and given the huge growth that both these fields have experienced in recent times we feel that now is an excellent time to discuss this nascent research space. This workshop continues the successful social mobile web workshop held as part of SocialCom in 2009. The workshop explores the current state of the social mobile web and combines technical presentations, demos and position papers to drive interaction and discussion among participants.

Gossip protocols provide probabilistic reliability and scalability, but their inherent randomness may lead to high variation in number of messages that are received at different nodes. This paper presents techniques that leverage simple social network principles enabling nodes to select gossip targets intelligently. The simple heuristics presented in the paper achieve a more uniform message overhead at each node, lowering the system-wide gossip traffic, while simultaneously reducing the latency of gossip spread (by up to 25%). We experimentally compare our system, called JetStream, against canonical gossip as well as gossip on the chord overlay. Intuitively, JetStream seeks to make gossip spread more deterministic and predictable, while still inheriting its scale and reliability. JetStream also provides an added benefit by reducing network bandwidth utilization with a low sustained rate of gossip injection

NCSA's Environmental Cyberinfrastructure Demonstration (ECID) project is developing a cyberenvironment to support environmental science and hydrology research. The ECID Cyberenvironment is based on a set of coordinating technologies that together provide unique capabilities for integrating local and remote work and for capturing and exploiting data and interaction provenance. The CyberCollaboratory portal provides collaboration tools to discover, share, analyze, and discuss data and information. The CyberIntegrator is a meta-workflow engine combining heterogeneous (local and remote) tools into workflows to support complex scientific analyses and simulations. Both of these tools record rich provenance into the RDF-based Tupelo 2 Toolkit where it can be graphically browsed or used through CI-KNOW, social network analysis software, to provide context-specific recommendations within the CyberCollaboratory and CyberIntegrator. These capabilities, and the ability to dynamically connect new tools into group spaces, workflows, and provenance trails, will be critical to the next-generation of community-scale, persistent cyberinfrastructure efforts.