The creation of virtual humans increasingly leverages automated synthesis of speech and gestures, enabling expressive, adaptable agents that effectively engage users. However, the independent development of voice and gesture generation technologies, alongside the growing popularity of virtual reality (VR), presents significant questions about the integration of these signals and their ability to convey emotional detail in immersive environments. In this paper, we evaluate the influence of real and synthetic gestures and speech, alongside varying levels of immersion (VR vs. 2D displays) and emotional contexts (positive, neutral, negative) on user perceptions. We investigate how immersion affects the perceived match between gestures and speech and the impact on key aspects of user experience, including emotional and empathetic responses and the sense of co-presence. Our findings indicate that while VR enhances the perception of natural gesture–voice pairings, it does not similarly improve synthetic ones—amplifying the perceptual gap between them. These results highlight the need to reassess gesture appropriateness and refine AI-driven synthesis for immersive environments.

Clinical group bereavement therapy often promotes narrative sharing as a therapeutic intervention to facilitate grief processing. Increasingly, people turn to social media to express stories of loss and seek support surrounding bereavement experiences, specifically, the loss of loved ones from suicide. This paper reports the results of a computational linguistic analysis of narrative expression within an online suicide bereavement support community. We identify distinctive characteristics of narrative posts (compared to non-narrative posts) in linguistic style. We then develop and validate a machine-learning model for tagging narrative posts at scale and demonstrate the utility of applying this machine-learning model to a more general grief support community. Through comparison, we validate our model's narrative tagging accuracy and compare the proportion of narrative posts between the two communities we have analyzed. Narrative posts make up about half of all total posts in these two grief communities, demonstrating the importance of narrative posts to grief support online. Finally, we consider how the narrative tagging tool presented in this study can be applied to platform design to more effectively support people expressing the narrative sharing of grief in online grief support spaces.

Humans need a sense of community (SOC), and social media platforms afford opportunities to address this need by providing users with a sense of virtual community (SOVC). This paper explores SOVC on Reddit and is motivated by two goals: (1) providing researchers with an excellent resource for methodological decisions in studies of Reddit communities; and (2) creating the foundation for a new class of research methods and community support tools that reflect users' experiences of SOVC. To ensure that methods are respectfully and ethically designed in service and accountability to impacted communities, our work takes a qualitative and community-centered approach by engaging with two key stakeholder groups. First, we interviewed 21 researchers to understand how they study community" on Reddit. Second, we surveyed 12 subreddits to gain insight into user experiences of SOVC. Results show that some research methods can broadly reflect user experiences of SOVC regardless of the topic or type of subreddit. However, user responses also evidenced the existence of five distinct Community Archetypes: Topical Q&A, Learning & Perspective Broadening, Social Support, Content Generation, and Affiliation with an Entity. We offer the Community Archetypes framework to support future work in designing methods that align more closely with user experiences of SOVC and to create community support tools that can meaningfully nourish the human need for SOC/SOVC in our modern world.

Despite frequent efforts to combat racism, almost no research has explored how to cultivate positive experiences of thriving Black culture on Reddit. In this case study, we surveyed users of r/BlackPeopleTwitter (BPT)--a large, popular subreddit that showcases screenshots of hilarious or insightful social media posts made by Black people (mainly from Black Twitter). Our research questions seek to understand users' motivations for visiting BPT, how they experience a sense of virtual community (SOVC) and membership in BPT, and how BPT's governance influences these experiences. We find that that users come to BPT primarily for excellent humor and entertainment, sociopolitical context on issues relevant to Black people, and/or partaking in the shared Black experience. Black users are more likely to report higher SOVC and to identify as members, whereas non-Black users are more likely to identify as guests or visitors to the community. To protect Black expression, the BPT moderation team implemented a governance strategy for verifying racial identity and limiting participation to only verified users in certain threads. Our data suggest that this policy is a contentious but influential aspect of SOVC that simultaneously constructs and challenges the sense of the subreddit existing as a safe space for Black people. We synthesize these results by discussing how: differing platform affordances across Twitter and Reddit combine to cultivate a thriving Black community on Reddit; the need for Black authenticity on an otherwise anonymous platform can guide future research in identity verification; and the limitations of this study motivate future work to support all marginalized communities online.

In the aftermath of COVID-19, remote working has become the norm, and graduates now need an even wider range of skills, which traditional classrooms and internships do not always provide. Working in multiple time zones, within global multi-cultural teams, and only ever meeting colleagues through online technology are just some of the challenges, which require a new type of global graduate. Transversal skills including leadership, collaboration, innovation, digital, green, organization and communication skills are critical. The disruption from COVID-19 also presents unprecedented opportunities to develop more inclusive approaches to internships and international experiences, to level the playing field for students with special needs, from underrepresented groups or with caring commitments. In this position paper, we present a new Global Innovation internship model that has the aim of allowing students to complete technology internships and projects by working together virtually on real world challenges, guided by experienced industry and academic mentors. The model is being developed as part of an Erasmus+ funded project, and the partnership includes seven Higher Education Institutions from six different countries around the world. This position paper describes the design and development of a pilot programme of the Global Innovations internship model.

Bots are increasingly being used for governance-related purposes in online communities, yet no instrumentation exists for measuring how users assess their beneficial or detrimental impacts. In order to support future human-centered and community-based research, we developed a new scale called GOVernance Bots in Online communiTies (GOV-BOTs) across two rounds of surveys on Reddit (N=820). We applied rigorous psychometric criteria to demonstrate the validity of GOV-BOTs, which contains two subscales: bot governance (4 items) and bot tensions (3 items). Whereas humans have historically expected communities to be composed entirely of humans, the social participation of bots as non-human agents now raises fundamental questions about psychological, philosophical, and ethical implications. Addressing psychological impacts, our data show that perceptions of effective bot governance positively contribute to users' sense of virtual community (SOVC), whereas perceived bot tensions may only impact SOVC if users are more aware of bots. Finally, we show that users tend to experience the greatest SOVC across groups of subreddits, rather than individual subreddits, suggesting that future research should carefully re-consider uses and operationalizations of the term "community."

We present a first-person, retrospective exploration of two radio sonification pieces that employ narrative scaffolding to teach audiences how to listen to data. To decelerate and articulate design processes that occurred at the rapid pace of radio production, the sound designer and producer wrote retrospective design accounts. We then revisited the radio pieces through principles drawn from guidance design, data storytelling, visualization literacy, and sound studies. Finally, we speculated how these principles might be applied through interactive, voice-based technologies. First-person methods enabled us to access the implicit knowledge embedded in radio production and translate it to technologies of interest to the human–computer-interaction community, such as voice user interfaces that rely on auditory display. Traditionally, sonification practitioners have focused more on generating sounds than on teaching people how to listen; our process, however, treated sound and narrative as a holistic, sonic-narrative experience. Our first-person retrospection illuminated the role of narrative in designing to support people as they learn to listen to data.

Whether we recognize it or not, the Internet is rife with exciting and original institutional forms that are transforming social organization on and offline. Governing these Internet platforms and other digital institutions has posed a challenge for engineers and managers, many of whom have little exposure to the relevant history or theory of institutional design. The dominant guiding practices for the design of digital institutions to date in human-computer interaction, computer-supported cooperative work, and the tech industry at large have been an incentive-focused behavioral engineering paradigm encompassing atheoretical approaches such as emulation, A/B-testing, engagement maximization, and piecemeal issue-driven engineering. One institutional analysis framework that has been useful in the study of traditional institutions comes from scholars of natural resource management, particularly that community of economists, anthropologists, and environmental and political scientists focused around the work of Elinor Ostrom, known collectively as the "Ostrom Workshop." A key finding from this community that has yet to be broadly incorporated into the design of many digital institutions is the importance of including participatory change mechanisms in what is called a "constitutional layer" of institutional design. The institutional rules that compose a constitutional layer facilitate stakeholder participation in the ongoing process of institutional design change. We explore to what extent consideration of constitutional layers is met or could be better met in three varied cases of digital institutions: cryptocurrencies, cannabis informatics, and amateur Minecraft server governance. Examining such highly varied cases allows us to demonstrate the broad relevance of constitutional layers in many different types of digital institutions.

Wikipedia plays a crucial role for online information seeking and its editors have a remarkable capacity to rapidly revise its content in response to current events. How did the production and consumption of political information on Wikipedia mirror the dynamics of the 2016 U.S. Presidential campaign? Drawing on systems justification theory and methods for measuring the enthusiasm gap among voters, this paper quantitatively analyzes the candidates' biographical and related articles and their editors. Information production and consumption patterns match major events over the course of the campaign, but Trump-related articles show consistently higher levels of engagement than Clinton-related articles. Analysis of the editors' participation and backgrounds show analogous shifts in the composition and durability of the collaborations around each candidate. The implications for using Wikipedia to monitor political engagement are discussed.

Today, disaster events are mobilizing digital volunteers to meet the data needs of those on the ground. One form of this crowd work is Volunteered Geographic Information. This peer-produced spatial data creates the most up-to-date map of the affected region; maintaining the accuracy of these data is therefore a critical task. Accuracy is one aspect of data quality, a relative concept requiring standards to measure against. The field of Geographic Information Sciences has developed standards for this comparison, achieving widespread acceptance. However, the peer production model of spatial data presents new opportunities—and challenges—to traditional methods of quality assessment. Through analysis of the OpenStreetMap database, we show that temporal editing patterns and contributor characteristics can provide additional means of understanding spatial data quality. Drawing upon experiences from Wikipedia, we offer and evaluate three intrinsic quality metrics of peer-produced spatial data to assess the quality of contributions to OpenStreetMap for crisis response.

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise pre-existing knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a proficiency-congruency dilemma between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how players negotiate this dilemma. Using data from League of Legends, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million players to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that the two factors, player proficiency and team congruency, both increase team performance, with the former having a stronger impact. We also find that elite players are better at balancing the two factors than the novice players. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly prioritize individuals' expertise and teams' compatibility.

Non-verbal communication plays a large role in online competitive multiplayer games, as team members attempt to coordinate with each other without distraction to achieve victory. Some games enable this communication through "pings," alerts that are easy to activate and provide auditory and visual cues for teammates. In this paper, we review the literature on gestures and non-verbal communication and, through an empirical analysis of 84,489 players across 10,293 matches in the popular game, League of Legends, illustrate ping use in multiplayer games and test the impact of ping actions on performance in teams. We show that the amount of pings depends on player role and in-game activity and that pings by players have a positive but concave relationship with player performance. These findings demonstrate the importance of non-verbal communication and interruption on the performance of virtual team members. We conclude by discussing the implications of these results for theorizing and designing sociotechnical systems that rely on users to engage in synchronous, collaborative work in shared visual spaces.

Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration. We outline a research agenda for researchers in computer-supported cooperative work (CSCW) to understand the relationships, patterns, antecedents, and consequences of sequential behavior using methods already developed in fields like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns. We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration.

Sporting championships and other media events can induce very strong feelings of co-presence that can change communication patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dynamics. We employ a computational focus group method to identify a population of 790,744 international Twitter users, and we track their behavior before, during, and after the 2014 FIFA World Cup. We pick, in particular, a set of Twitter users who specified the teams that they are supporting, such that we can identify communities of fans of the teams, as well as the entire community of World Cup fans. The structure, dynamics, and content of communication of these communities of users are analyzed to compare behavior outside of the matches to behavior during the event and to examine behavioral responses across languages. Specifically, the temporal patterns of the tweeting volume, topics, retweet- ing, and mentioning behaviors are analyzed. We find there are similarities in the responses to media events, characteristic changes in activity patterns of users, and substantial differences in linguistic features. These findings have implications for designing more resilient socio-technical systems during crises and developing better models of complex social behavior.

Following the deaths of notable people, Wikipedians incorporate this new knowledge by updating or creating biographical articles. Drawing on literature from death studies and peer production, we demonstrate how the creation of these "wiki-bituaries" requires complex coordination work and highlight processes of commemoration and memorialization within socio-technical systems. Using the corpus of 6,132 articles about people who died in 2012, we examine the network relationships and contribution dynamics of users who perform this work and identify behavioral and content dynamics on the biographical articles about the deceased. The collaborations that emerge from posthumous editing of these biographies are sites of significant activity that coalesce into complex but temporary collaborations. Based on these findings, we argue that Wikipedia has re-imagined the obituary into a genre for creating memory spaces in which the death of a subject prompts a form of "death work" involving the collective re-evaluation of article content and a transition into a new mode of data stewardship.

Most education and workplace learning takes place in classroom contexts far removed from laboratories or field sites with special arrangements for scientific research. But digital online resources provide a novel opportunity for large-scale efforts to bridge the real-world and laboratory settings which support data collection and randomized A/B experiments comparing different versions of content or interactions [2]. However, there are substantial technological and practical barriers in aligning instructors and researchers to use learning technologies like blended lessons/exercises & MOOCs as both a service for students and a realistic context to conduct research. This paper explains how the concept of a "MOOClet" can facilitate research-practitioner collaborations. MOOClets [3] are defined as modular components of a digital resource that can be implemented in technology to: (1) allow modification to create multiple versions, (2) allow experimental comparison and personalization of different versions, (3) reliably specify what data are collected. We suggest a framework in which instructors specify what kinds of changes to lessons, exercises, and emails they would be willing to adopt, and what data they will collect and make available. Researchers can then: (1) specify or design experiments that compare the effects of different versions on quantifiable outcomes. (2) Explore algorithms for maximizing particular outcomes by choosing alternative versions of a MOOClet based on the input variables available. We present a prototype survey tool for instructors intended to facilitate practitioner-researcher matches and successful collaborations.

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

Wikipedia's coverage of current events blurs the boundaries of what it means to be an encyclopedia. Drawing on Gieyrn's concept of "boundary work", this paper explores how Wikipedia's response to the 9/11 attacks expanded the role of the encyclopedia to include newswork, excluded content like the 9/11 Memorial Wiki that became problematic following this expansion, and legitimized these changes through the adoption of news-related policies and routines like promoting "In the News" content on the homepage. However, a second case exploring WikiNews illustrates the pitfalls of misappropriating professional newswork norms as well as the challenges of sustaining online communities. These cases illuminate the social construction of new technologies as they confront the boundaries of traditional professional identities and also reveal how newswork is changing in response to new forms of organizing enabled by these technologies.

The MediaWiki platform supports popular socio-technical systems such as Wikipedia as well as thousands of other wikis. This software encodes and records a variety of relationships about the content, history, and editors of its articles such as hyperlinks between articles, discussions among editors, and editing histories. These relationships can be analyzed using standard techniques from social network analysis, however, extracting relational data from Wikipedia has traditionally required specialized knowledge of its API, information retrieval, network analysis, and data visualization that has inhibited scholarly analysis. We present a software library called the NodeXL MediaWiki Importer that extracts a variety of relationships from the MediaWiki API and integrates with the popular NodeXL network analysis and visualization software. This library allows users to query and extract a variety of multidimensional relationships from any MediaWiki installation with a publicly-accessible API. We present a case study examining the similarities and differences between different relationships for the Wikipedia articles about "Pope Francis" and "Social media." We conclude by discussing the implications this library has for both theoretical and methodological research as well as community management and outline future work to expand the capabilities of the library.

Social media have been employed to assess public opinions on events, markets, and policies. Most current work focuses on either developing aggregated measures or opinion extraction methods like sentiment analysis. These approaches suffer from unpredictable turnover in the participants and the information they react to, making it difficult to distinguish meaningful shifts from those that follow from known information. We propose a novel approach to tame these sources of uncertainty through the introduction of "computational focus groups" to track opinion shifts in social media streams. Our approach uses prior user behaviors to detect users' biases, then groups users with similar biases together. We track the behavior streams from these like-minded sub-groups and present time-dependent collective measures of their opinions. These measures control for the response rate and base attitudes of the users, making shifts in opinion both easier to detect and easier to interpret. We test the effectiveness of our system by tracking groups' Twitter responses to a common stimulus set: the 2012 U.S. presidential election debates. While our groups' behavior is consistent with their biases, there are numerous moments and topics on which they behave "out of character," suggesting precise targets for follow-up inquiry. We also demonstrate that tracking elite users with well-established biases does not yield such insights, as they are insensitive to the stimulus and simply reproduce expected patterns. The effectiveness of our system suggests a new direction both for researchers and data-driven journalists interested in identifying opinion shifting processes in real-time.

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.

Although Wikipedia has increasingly attracted attention for its in-depth and timely coverage of breaking news stories, the social dynamics of how Wikipedia editors process breaking news items has not been systematically examined. Through a 3-month study of 161 deliberations over whether a news item should appear on Wikipedia's front page, we demonstrate that elite users fulfill a unique gatekeeping role that permits them to leverage their community position to block the promotion of inappropriate items. However, these elite users are unable to promote their supported news items more effectively than other types of editors. These findings suggest that "one-sided gatekeeping" may reflect a crucial stasis in social media where the community has to balance the experience of its elite users while encouraging contributions from non-elite users.

An important element of any routing protocol used for wireless mesh networks (WMNs) is the link cost function used to represent the radio link characteristic. The majority of the routing protocols for WMNs attempt to accurately characterise the radio link quality by constructing the link cost function from the measurements obtained using active probing techniques, which introduces overhead. In this paper we propose a new approach called resource aware routing for mesh (RARE) which instead employs passive monitoring to gather radio link information. This results in a smaller overhead than the other methods that require active network probing, and is load independent since it does not require an access to the medium. Moreover, we show that our RARE approach performs well in a real radio environment through a number of experiments performed on a static 17 node WLAN mesh testbed.