Anti-social behaviors with the label "toxicity" are commonly understood as inescapable when playing online games with strangers, particularly in multiplayer online battle arenas (MOBA). Prior work in understanding in-game toxicity has primarily focused on verbal communication methods such as voice and text chat, which we extend into non-verbal communication, such as pings, emotes, and map annotations. In a semi-structured interview study among ten players of popular MOBAs (League of Legends and DotA2), we explored players’ perspectives on perceived toxicity through nonverbal communication features and the consequences of that toxicity. Our results echo prior work that "toxicity" refers to an exceptionally broad domain of behaviors and discuss player perceptions that may contribute to its normalization. In regard to nonverbal communications, pings stand out as players’ primary nonverbal communication method, primarily as strategic communication but often also toxic pings, which players consider unique in their form and intensity of toxicity compared to verbal communications. We discuss design implications for MOBAs, particularly in the context of player perspectives on limiting communication exclusively to nonverbal communication.

Large language models (LLMs) enable scalable generation of instructional datasets tailored to learners' interests, offering new opportunities to increase relevance and engagement in data-science education. However, personalized synthetic data may appear arbitrary or unrealistic, limiting its instructional value. We examine how structural features of LLM-generated personalized datasets shape participants' perceptions of dataset authenticity and quality. In an online lab experiment, 82 participants selected a topic of interest from five popular domains and evaluated LLM-generated datasets that systematically varied in dataset size and topic specificity. Participants rated the appropriateness of variable names and the plausibility of value ranges. Linear mixed-effects models showed that larger datasets were consistently perceived as less realistic than smaller ones. Greater topic specificity mitigated this negative effect, while topic domain did not significantly influence judgments. The results suggest that perceived dataset quality depends on structural features of dataset (i.e. size and specificity). Our research contributes to the broader literature on engaging learners in data science literacy. We provide empirical guidance for designing LLM-generated instructional materials that support authenticity, relevance, and learner engagement.

The intersection of language and identity refers to the ways in which language use reflects and shapes one's sense of self and group membership. It is important to design voice-based technologies that represent and support diverse types of identities to increase accessibility. Due to constraints in the data and algorithms used to train and improve artificial intelligence (AI) systems like social robots or voice assistants, they are often unable to represent certain dialects of English and other languages. This could result in data bias, identity exclusion, and communication barriers that can impact the user's outcomes when using such dialects. Technology companies are discussing how to close this gap by improving training data; however, some researchers believe that the intersection of identity and technology calls for effective collaborative design methodologies that involve intended users. This work-in-progress paper presents preliminary findings on a participatory design method, comic-boarding, that can accomplish the above goal. Using comic-boarding, our intention is to create a space for critical reflection on language and identity in human-human and human-agent dialogue for children from marginalized communities.

Abstract With over 300 language communities, Wikipedia offers a multilingual environment for collective reporting of events worldwide. However, most research on documenting events has focused on English Wikipedia. Our work explores the extent of global coverage of event articles in the English Wikipedia and Wikipedia communities associated with three of the most spoken languages globally: Arabic, Chinese and Spanish. We also compare the process of collaboratively writing event articles across these communities. Our results provide evidence that while there is an indication of self-focus bias, there is also strong evidence of a global representation of events across different Wikipedia communities. Moreover, we observe that while limited resources in smaller Wikipedia communities have different collaboration patterns, their efforts lead to similar levels of content quality after a week. We discuss the implications of our results for Wikipedia and the design of multi-language information production communities.

Explanations of AI Agents' actions are considered to be an important factor in improving users' trust in the decisions made by autonomous AI systems. However, as these autonomous systems evolve from reactive, i.e., acting on user input, to proactive, i.e., acting without requiring user intervention, there is a need to explore how the explanation for the actions of these agents should evolve. In this work, we explore the design of explanations through participatory design methods for a proactive auto-response messaging agent that can reduce perceived obligations and social pressure to respond quickly to incoming messages by providing unavailability-related context. We recruited 14 participants who worked in pairs during collaborative design sessions where they reasoned about the agent's design and actions. We qualitatively analyzed the data collected through these sessions and found that participants' reasoning about agent actions led them to speculate heavily on its design. These speculations significantly influenced participants' desire for explanations and the controls they sought to inform the agents' behavior. Our findings indicate a need to transform users' speculations into accurate mental models of agent design. Further, since the agent acts as a mediator in human-human communication, it is also necessary to account for social norms in its explanation design. Finally, user expertise in understanding their habits and behaviors allows the agent to learn from the user their preferences when justifying its actions.

Prior research has shown that gesture-based communication plays an integral role in online multiplayer games, especially for teams consisting of strangers with no prior common experiences. What motivates a particular form of in-game communication and its impact, especially promptly after a communication attempt, on team performance is less explored. In this paper, we present a framework for studying individual communication attempts using a gestured-based tool, "Ping", in a popular MOBA game, Heroes of the Storm. We design a framework to capture the game's situational context prior to the communication, the communication attempt, and its immediate impact. Using this framework, we analyze and identify game factors that influence players' decisions to communicate and their choice of communication. We also present how communication has an immediate positive effect on the team's performance. Our findings further the discussion of studying communication behaviors for virtual teams engaging in synchronous, collaborative work with shared visual spaces.

The ease of smartphone communications has created an expectation of constant connectivity. While the adoption of virtual assistants has improved, their capabilities for handling proactive communication tasks remain underexplored. We present the design, implementation, and evaluation of a Contextual Auto-Response agent to communicate users’ situational awareness. The agent creates auto-responses by modeling availability using smartphone sensors and sharing contextual information on behalf of the user. In a two-week study with 12 participants, we evaluated the perception of this agent and its impact on device usage behavior. Many participants found the agent useful for signaling unavailability, with some caveats. Participants also reported altering device and agent usage based on their understanding of its functions. Our findings indicate the importance of transparency in proactive agent designs and the need for personalization to enable an enhanced and cooperative human-agent interaction.

The COVID-19 global pandemic has ignited lightning-fast adoption of digital tools in our communities, organizations, and systems of governance. It also inspired an unprecedented level of providing access to digital devices to communities and individuals lacking prior access. The situation and circumstances provide a unique opportunity to understand digital divides through a new lens. In this work, we contribute a contemporaneous understanding of digital divides beyond access by qualitatively analyzing over 300 calls made to a volunteer-based community IT help desk. We highlight the intertwined network of challenges leading to ecosystem digital divides and contribute new insights into how the complex socio-technical systems of practice, and the tools to support them, must adapt to bridge digital divides more effectively.

Smart voice assistants such as Amazon Alexa and Google Home are becoming increasingly pervasive in our everyday environments. Despite their benefits, their miniaturized and embedded cameras and microphones raise important privacy concerns related to surveillance and eavesdropping. Recent work on the privacy concerns of people in the vicinity of these devices has highlighted the need for 'tangible privacy', where control and feedback mechanisms can provide a more assured sense of whether the camera or microphone is 'on' or 'off'. However, current designs of these devices lack adequate mechanisms to provide such assurances. To address this gap in the design of smart voice assistants, especially in the case of disabling microphones, we evaluate several designs that incorporate (or not) tangible control and feedback mechanisms. By comparing people's perceptions of risk, trust, reliability, usability, and control for these designs in a between-subjects online experiment (N=261), we find that devices with tangible built-in physical controls are perceived as more trustworthy and usable than those with non-tangible mechanisms. Our findings present an approach for tangible, assured privacy especially in the context of embedded microphones.

Social media has become an important source where people gather and communicate news. Prior studies in conventional mass media suggest that gatekeepers play an important role in the production of news messages. Despite the initial claim of social media being a place of democratized participation, we now know, social media is not free of gatekeepers either. However, it is unclear who social media gatekeepers are, how to identify them, and most importantly how do they impact news content production and dissemination. Due to fundamental differences between the structure and workings of social media vs. traditional media, what we know from mass media cannot directly apply in the context of social media. To answer these questions, we propose an actionable definition of social media gatekeepers backed by literature on news reporting in social media and traditional mass media. We then present a case study of identifying gatekeepers on Twitter at scale, using a set of 70k Twitter users interested in the news topic of "immigration''. The results of our mixed research approach highlight that, unlike the general Twitter users, the Twitter gatekeepers are often self-determining citizen journalists who manage their media presentation strategically. Moreover, Twitter gatekeepers tend to exhibit behavior mostly in accordance with the journalism norms and they contribute to and guard the truthfulness and neutrality of content.

Sensor-enabled computers in the form of 'IoT' devices such as home security cameras and voice assistants are increasingly becoming pervasive in our environment. With the embedded cameras and microphones in these devices, this 'invasion' of our everyday spaces can pose significant threats to the privacy of bystanders. Because of their complex functionality, even when people attempt privacy measures (such as asking the owner to "turn the camera off"), these devices may still record information because of the lack of a 'real' off button. With the ambiguities of current designs, a bystander's perceived privacy can diverge from their actual privacy. Indeed, being able to assess one's actual privacy is a key aspect in managing one's privacy according to Altman's theory of boundary regulation, and current designs fall short in assuring people of their privacy. To understand how people as bystanders manage their privacy with IoT devices, we conducted an interview study about people's perceptions of and behaviors around current IoT devices. We find that although participants' behaviors line up with Altman's theory of boundary regulation, in the face of uncertainty about their privacy, they desire or engage in various 'tangible' workarounds. Based on our findings, we identify and introduce the concept of 'tangible privacy' as being essential to boundary regulation with IoT devices. We argue that IoT devices should be designed in a way that clearly and unambiguously conveys sensor states to people around them and make actionable design recommendations to provide strong privacy assurances to bystanders.

Attracting and retaining newcomers is critical and challenging for online production communities such as Wikipedia, both because volunteers need specialized training and are likely to leave before being integrated into the community. In response to these challenges, the Wikimedia Foundation started the Wiki Education Project (Wiki Ed), an online program in which college students edit Wikipedia articles as class assignments. The Wiki Ed program incorporates many components of institutional socialization, a process many conventional organizations successfully use to integrate new employees through formalized on-boarding practices. Research has not adequately investigated whether Wiki Ed and similar programs are effective ways to integrate volunteers in online communities, and, if so, the mechanisms involved. This paper evaluates the Wiki Ed program by comparing 16,819 student editors in 770 Wiki Ed classes with new editors who joined Wikipedia in the conventional way. The evaluation shows that the Wiki Ed students did more work, improved articles more, and were more committed to Wikipedia. For example, compared to new editors who joined Wikipedia in the conventional way they were twice as likely to still be editing Wikipedia a year after their Wiki Ed class was finished. Further, students in classrooms that encouraged joint activity, a key component of institutional socialization, produced better quality work than those in classrooms where students worked independently. These findings are consistent with an interpretation that the Wiki Ed program was successful because it incorporated elements of institutionalized socialization.

Identifying instances when a user will not able to attend to an incoming message and constructing an auto-response with relevant contextual information may help reduce social pressures to immediately respond that many users face. Mobile messaging behavior often varies from one person to another. As a result, compared to a generic model considering profiles of several users, a personalized model can capture a user's messaging behavior more accurately to predict their inattentive states. However, creating accurate personalized models requires a non-trivial amount of individual data, which is often not available for new users. In this work, we investigate a weighted hybrid approach to model users' attention to messaging. Through dynamic performance-based weighting, we combine the predictions of three types of models, a general model, a group model and a personalized model to create an approach which can work through the lack of initial data while adapting to the user's behavior. We present the details of our modeling approach and the evaluation of the model with over three weeks of data from 274 users. Our results highlight the value of hybrid weighted modeling to predict when a user cannot attend to their messages.

Organizations conduct series of face-to-face meetings aiming to improve work practices. In these meetings, participants from different backgrounds collaboratively design artifacts, such as knowledge or process maps. Such meetings are orchestrated and carried out by facilitators and the success of the meetings almost solely depends on the experience of the facilitators. Previous research has mainly focused on approaches that support facilitators and participants in the upfront planning of such events. There is however, little guidance for facilitators and participants once a meeting has started. One critical aspect -- among others -- is that during a meeting, the facilitator and participants need to decide for how long the iterative process of discussion and design should continue. We argue that we can provide support for such decisions based on the evolution of artifacts collaboratively created during such meetings. This paper presents a multi-level, multi-method analysis of artifacts based on experts' observations in combination with network analytics. We study the use of automated analytics to assess the evolution of collaboratively created artifacts and to indicate maturity and established consensus of the collaborative practice. We propose a computational approach to support facilitators and participants in deciding when to stop face-to-face meetings.

Various information systems have emerged to facilitate citizen participation in the life of their communities. However, there is a lack of robust understanding of what enables the sustainability of such systems. This work introduces a framework to identify and analyze various factors that influence the sustainability of “hyper-local” information systems. Using longitudinal observations of participation from 35 online neighborhood discussion forums over six years, we analyze the relationship between sustainability and online–offline community characteristics. Our results not only show patterns consistent with previous observations but reveal the dubious influences of member heterogeneity and network structure. Design insights are discussed.

We present a photo co-creation activity, called "Stitched Groupies," in a photo-taking and sharing platform deployed inside IBM. "Stitched Groupies" allow employees to take and combine photos with peers asynchronously across physical boundaries. In a 25-day exploratory field study with 50 users taking 68 half-photos (of which 52 were completed by others), we categorized themes such as Spliced Faces, Composed Scenes, Body Modifications, Inanimate Objects and Doppelgangers. Our results suggest that photo co-creation can stimulate playfulness and fun in the workplace.

This note reports on the use of ICTs by a small nonprofit organization that serves LGBT youth. Our work centers on a reflective evaluation of the use of online communities for LGBT community through qualitative interviews with the organization. Perceived issues around ICT use in the organization were shaped by the blurred lines between professional and personal interactions online, the small size of the community and ubiquity of social media use, and ambivalence of members toward online communication. The project models one way for researchers in ICT4D to work within communities to develop an understanding of self-identified issues in vulnerable populations.

Over the last decade, a citizen science movement has tried to engage students, laymen and other non-scientists in the production of science. However, there has been less attention in citizen science projects to use the public to disseminate scientific knowledge. Wikipedia provides a platform to study engagement of citizen scientists in knowledge dissemination. College and university students are especially appropriate members of the public to write science articles, because of the course-work and mentorship they receive from faculty. This paper describes a project to support students' writing of scientific articles in Wikipedia. In collaboration with a scientific association, we involved 640 students from 36 courses in editing scientific articles on Wikipedia. This paper provides details in the design of the program and our quantitative and qualitative approaches to evaluating it. Our results show that the Wikipedia classroom experiment benefits both the Wikipedia community and students. Undergraduate and graduate students substantially improved the scientific content of over 800 articles, at a level of quality indistinguishable from content written by PhD experts. Both students and faculty endorsed the motivational benefits of an authentic writing experience that would be read by thousands of people.

Turnover is commonplace in many online groups because of low barriers of entry and exit. In offline settings, turnover can have a negative impact because of reduced attachment to the group as an entity. However, in an online setting, turnover in terms of changes in the visible membership of a group may have a very different impact. Online only a limited amount of information about members and their activities is observable; in particular, it is easier to see the behavior of the subset of members who are active than the potentially larger set who are not. In this paper, we describe an experiment examining the influence of visible membership turnover on commitment to an online group. Our results suggest that increased turnover in an online group may increase social presence, creating perceptions of liveness, in turn leading to increased levels of participation in the group. However, this result holds primarily for groups with a common identity, suggesting that attention to behavior of others may be stronger when people share an identity with those others. Our results extend understandings of attachment in an online setting as well as theory about social tuning.

Although many off-line organizations give their employees training, mentorship, a cohort and other socialization experiences that improve their retention and productivity, online production communities rarely do this. This paper describes the planning, execution and evaluation of a socialization regime for an online technical support community. In a two-phase project, we first automatically identified from participants' early behavior, those with high potential to become core members. We then designed, delivered and experimentally evaluated socialization experiences intended to build commitment and competence among these potential core members. We were able to identify potential core members with high accuracy from only two weeks of behavior. A year later, those classified as potential core members participated in the community ten times more actively than those not identified. In an evaluation experiment, some potential core members were randomly assigned to receive socialization experiences, while others were not. A year later, those who had participated in the socialization regime contributed more answers in the community compared to those in the control condition. The socialization experiences, however, undercut their sense of connection to the community and the quality of their contributions. We discuss what was effective and what could be improved in designing socialization experiences for online groups.

Successful contributors in online communities go through a lifecycle of membership status starting from the periphery and moving to the core over time. We argue that a personalized incremental engagement strategy can mobilize a larger proportion of members through this process. This paper presents our approach to identify a progression of contributions in an online community and the design of personalized incremental engagement strategy which will drive users' contributions one step forward. The results of our field study show that the personalized incremental approach significantly boosts contributions in an online community for academic conferences.

The existence and survival of online communities depends upon the commitment and retention of their members. This paper compares alternative ways of designing online sites to increase member commitment. We report the results of two experiments conducted within a Facebook game application. The results show that designs can increase commitment and retention of players either by visually highlighting individual members, or by emphasizing the community as a whole. These designs influence commitment through different routes.

As online communities, such as social network sites, mature, they face challenges in sustaining user engagement. To address this, we designed and deployed a rating system to encourage a broad set of users to promote a diverse set of content on a social network site. By evaluating the impact of the promotions on the site's content and users over time, we found that the system successfully promotes more diverse content and encourages new social interactions between users.

Success and sustainability of social networking sites is highly dependent on user participation. To encourage contribution to an opt-in social networking site designed for employees, we have designed and implemented a feature that rewards contribution with points. In our evaluation of the impact of the system, we found that employees are initially motivated to add more content to the site. This paper presents the analysis and design of the point system, the results of our experiment, and our insights regarding future directions derived from our post-experiment user interviews.

The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.

The size and diversity of the Web has been the root cause of the poor performance of many retrieval systems, with little navigational support provided by many large online formation repositories. The online information retrieval process cross different repositories shares similarities with content access facilities and user behaviors even when containing inherently different content types. In this work, we introduce our social recommender system called ASSIST. The recommendation framework in ASSIST can be applied to any online information retrieval service with key information access components, search and browsing. ASSIST exploits multiple forms of social implicit feedback in order to generate well-informed user recommendations in the online information retrieval domain.