Artificial intelligence (AI) is deepening human social experiences within online spaces in increasingly layered ways. Amid these shifts, we designed AmongOthers, an online community populated with 800 AI agents, and rethought human–AI social interactions. Eight participants engaged with AmongOthers for four weeks. For the first two weeks, they were told the community was exclusively for immigrants and international students, after which we disclosed that most users were AI agents. Participants shared periodic reflections and later joined interviews. Initially, AmongOthers was described as warm and respectful. However, after disclosure, participants diverged in their attitudes toward AI in online social communities, ranging from embracing and denying to imagining it only as a conditional possibility. We discuss these tensions in human perceptions of AI and highlight the risks of framing AI as failed replicas or preferable proxies. We finally suggest rethinking AI as distinct social entities in their own right.

Self-disclosure is central to mental health, and chatbots are increasingly used to elicit it by lowering the risk of social judgment. With the rapid growth of voice-based chatbots, it is crucial to understand how their voice identity shapes self-disclosure, yet this relationship remains underexplored. We address this gap through a mixed-method study that combined a 14-day in-the-wild deployment (N = 61) with post-study interviews. Participants interacted daily with chatbots that spoke in one of three voices varying in social distance: their own, a family member’s, or a stranger’s. Findings show that chatbots using the user’s own voice were rated as more attractive and sustained deeper levels of disclosure over time. Family voice chatbots prompted reflection on interpersonal relationships, where participants reported comfort in discussing some topics but reluctance in others. Together, these findings highlight voice identity as a key design lever for steering both the amount and focus of self-disclosure.

Non-native speakers (NNSs) face significant language barriers in multilingual communication with native speakers (NSs). While AI-mediated communication (AIMC) tools offer efficient one-time assistance, they often overlook opportunities for NNSs’ continuous language acquisition. We introduce ChatLearn, an enhanced AIMC system that leverages NNSs’ communication difficulties as learning opportunities. Beyond comprehension and expression assistance, ChatLearn simultaneously captures NNSs’ language challenges, and subsequently provides them with spaced review as the conversation progresses. We conducted a mixed-methods study using a communication task with 43 NNS-NS pairs, after which ChatLearn NNSs recalled significantly more expressions than the baseline group, while there was no substantial decline in communication experience. Our findings highlight the value of contextual learning in NNS-NS communication, providing a new direction for AIMC systems that foster both immediate collaboration and continuous language development.

Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary challenges in analyzing writing behaviors are data complexity and the uncertainties introduced by automated methods. In response, we present \textsc{COALA}, a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters, generating behavior summaries using large language models, and visualizing writing-related actions at multiple granularities. We validated the effectiveness of \textsc{COALA} through user studies with domain experts (N=2+2) and researchers with relevant experience (N=8). We present the insights discovered by participants using \textsc{COALA}, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.

In today's globalized world, there are increasing opportunities for individuals to communicate using a common non-native language (lingua franca). Non-native speakers often have opportunities to listen to foreign languages, but may not comprehend them as fully as native speakers do. To aid real-time comprehension, live transcription of subtitles is frequently used in everyday life (e.g., during Zoom conversations, watching YouTube videos, or on social networking sites). However, simultaneously reading subtitles while listening can increase cognitive load. In this study, we propose Dynamik, a system that reduces cognitive load during reading by decreasing the size of less important words and enlarging important ones, thereby enhancing sentence contrast. Our results indicate that Dynamik can reduce certain aspects of cognitive load, specifically, participants' perceived performance and effort among individuals with low proficiency in English, as well as enhance the users' sense of comprehension, especially among people with low English ability. We further discuss our methods' applicability to other languages and potential improvements and further research directions.

Non-native speakers (NNSs) often face speaking challenges in real-time multilingual communication, such as struggling to articulate their thoughts. To address this issue, we developed an AI-based speaking assistant (AISA) that provides speaking references for NNSs based on their input queries, task background, and conversation history. To explore NNSs' interaction with AISA and its impact on NNSs' speaking during real-time multilingual communication, we conducted a mixed-method study involving a within-subject experiment and follow-up interviews. In the experiment, two native speakers (NSs) and one NNS formed a team (31 teams in total) and completed two collaborative tasks-one with access to the AISA and one without. Overall, our study revealed four types of AISA input patterns among NNSs, each reflecting different levels of effort and language preferences. Although AISA did not improve NNSs' speaking competence, follow-up interviews revealed that it helped improve the logical flow and depth of their speech. Moreover, the additional multitasking introduced by AISA, such as entering and reviewing system output, potentially elevated NNSs' workload and anxiety. Based on these observations, we discuss the pros and cons of implementing tools to assist NNS in real-time multilingual communication and offer design recommendations.

AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes.

Global work collaboration creates invisible physical and social barriers. Collaborating across non-physical barriers can be communicatively and cognitively taxing, which may disincentivize workers to form and participate in global teams with diverse members. As virtual environments (VEs) enabled by VR and game technologies become prevalent in global work, it is important to consider the design of workers' profiles to socially display language information in a way that supports global team formation. We conducted an online study involving participants from the U.S. and Japan to find team members in a VE - Gather Town. Participants were asked to form teams and complete a slogan generation task under one of the following profile display conditions - no display, constant display, and adaptive display to supplement language and personal cues. We studied how participants' search cost and attitudes towards global teamwork were affected. Our findings reveal team formation strategies depending on workers' local cultures and profile information available.

Previous research has shown that workplace social norms influence employee well-being. However, such norms vary based on the cultures in which workplaces are embedded, suggesting that cultural differences may influence perceived norms about when and where work should occur. These differences, in turn, could impact employee well-being. Accordingly, through the lenses of cultural tightness-looseness and individualism-collectivism, this paper investigates cultural differences in perceived social norms, and the relationship between those norms and hybrid workers’ well-being. We conducted a survey of 1,000 Japanese and 1,000 American hybrid workers. Results indicated that American respondents perceived stronger norms and demonstrated a higher willingness to conform to norms compared to Japanese respondents. Additionally, strong injunctive norms were positively associated with well-being among Americans but not among Japanese. Interviews (N = 24) showed that Japanese perceived injunctive norms negatively, while Americans saw them positively. We discuss implications for future remote-collaboration technologies in hybrid-work settings.

Teaching unbiased decision-making is crucial for addressing biased decision-making in daily life. Although both raising awareness of personal biases and providing guidance on unbiased decision-making are essential, the latter topics remains under-researched. In this study, we developed and evaluated an AI system aimed at educating individuals on making unbiased decisions using fairness-aware machine learning. In a between-subjects experimental design, 99 participants who were prone to bias performed personal assessment tasks. They were divided into two groups: a) those who received AI guidance for fair decision-making before the task and b) those who received no such guidance but were informed of their biases. The results suggest that although several participants doubted the fairness of the AI system, fair machine guidance prompted them to reassess their views regarding fairness, reflect on their biases, and modify their decision-making criteria. Our findings provide insights into the design of AI systems for guiding fair decision-making in humans.

Discussions about project values are important for engineering software that meets diverse human needs and positively impacts society. Because value-related discussions involve deeply held beliefs, they can lead to conflicts or other outcomes that may affect motivations to continue contributing to open source projects. However, it is unclear what kind of value-related discussions are associated with significant changes in turnover. We address this gap by identifying discussions related to important project values and investigating the extent to which those discussions predict project turnover in the following months. We collected logs of GitHub issues and commits from 52 projects that share similar ethical commitments and were identified as part of the DWeb (Decentralized Web) community. We identify issues related to DWeb's core values of respectfulness, freedom, broadmindedness, opposing centralized social power, equity & equality, and protecting the environment. We then use Granger causality analysis to examine how changes in the proportion of discussions related to those values might predict changes in incoming and outgoing turnover. We found multiple significant relationships between value-related discussions and turnover, including that discussions about respectfulness predict an increase in contributors leaving and a decrease in new contributors, while discussions about social power predicted better contributor retention. Understanding these antecedents of contributor turnover is important for managing open source projects that incorporate human-centric issues. Based on the results, we discuss implications for open source maintainers and for future research.

Chatbots are increasingly being used in mental healthcare - e.g., for assessing mental-health conditions and providing digital counseling - and have been found to have considerable potential for facilitating people's behavioral changes. Nevertheless, little research has examined how specific chatbot designs may help reduce public stigmatization of mental illness. To help fill that gap, this study explores how stigmatizing attitudes toward mental illness may be affected by conversations with chatbots that have 1) varying ways of expressing their interpretations of participants' statements and 2) different styles of self-disclosure. More specifically, we implemented and tested four chatbot designs that varied in terms of whether they interpreted participants' comments as stigmatizing or non-stigmatizing, and whether they provided stigmatizing, non-stigmatizing, or no self-disclosure of chatbot's own views. Over the two-week period of the experiment, all four chatbots' conversations with our participants centered on seven mental-illness vignettes, all featuring the same character. We found that the chatbot featuring non-stigmatizing interpretations and non-stigmatizing self-disclosure performed best at reducing the participants' stigmatizing attitudes, while the one that provided stigmatizing interpretations and stigmatizing self-disclosures had the least beneficial effect. We also discovered side effects of chatbot's self-disclosure: notably, that chatbots were perceived to have inflexible and strong opinions, which undermined their credibility. As such, this paper contributes to knowledge about how chatbot designs shape users' perceptions of the chatbots themselves, and how chatbots' interpretation and self-disclosure may be leveraged to help reduce mental-illness stigma.

Content creation today often takes place via collaborative writing. A longstanding interest of CSCW research lies in understanding and promoting the coordination between co-writers. However, little attention has been paid to individuals who write in their non-native language and to co-writer groups involving them. We present a mixed-method study that fills the above gap. Our participants included 32 co-writer groups, each consisting of one native speaker (NS) of English and one non-native speaker (NNS) with limited proficiency. They performed collaborative writing adopting two different workflows: half of the groups began with NNSs taking the first editing turn and half had NNSs act after NSs. Our data revealed a 'late-mover disadvantage' exclusively experienced by NNSs: an NNS's ideational contributions to the joint document were suppressed when their editing turn was placed after an NS's turn, as opposed to ahead of it. Surprisingly, editing help provided by AI-powered tools did not exempt NNSs from being disadvantaged. Instead, it triggered NSs' overestimation of NNSs' English proficiency and agency displayed in the writing, introducing unintended tensions into the collaboration. These findings shed light on the fair assessment and effective promotion of a co-writer's contributions in language diverse settings. In particular, they underscore the necessity of disentangling contributions made to the ideational, expressional, and lexical aspects of the joint writing.

Previous studies have suggested that organizational social norms can positively affect employee well-being. However, such social norms have not been well developed during the post-COVID-19 transition to hybrid work, which combines office and remote work, and it is unclear how employees’ perceptions of social norms for hybrid work affect their well-being. In this study, we investigated the impact of social norms for hybrid work on the well-being of hybrid workers living in Japan through a mixed-method approach consisting of an online survey (n = 212) and semi-structured interviews (n = 20). The results indicate that hybrid workers who feel subject to strong social norms have lower well-being. Conversely, those who are more willing to conform to social norms have higher well-being. Given our findings, we discuss implications for the design of systems to help hybrid workers conform to organizational social norms and to improve their well-being.

Chatbots have been designed to provide interventions in mental healthcare. However, how chatbot-based social contact can mitigate social stigma in mental illness remains under-explored. We designed two chatbots that deliver either first-person or third-person narratives about mental illness and evaluated them using a mixed methods study. Compared to a web survey group, participants in both chatbot groups decreased their beliefs that individuals are personally responsible for their mental illnesses, and increased their intentions to help. Additionally, participants in the first-person chatbot group showed a reduced level of fear, and a lower desire for social distance from people with mental illness. Many in the first-person chatbot group also reported a feeling of relationship with the chatbot, and chose to phrase their responses empathetically. Results demonstrated that chatbot-based social contact has promising potential for mitigating mental illness stigma. Implications for designing chatbot-based social contact are discussed.

When receiving text messages from unacquainted colleagues in fully remote workplaces, insufficient mutual understanding and limited social cues can lead people to misinterpret the tone of the message and further influence their impression of remote colleagues. Emojis have been commonly used for supporting expressive communication; however, people seldom use emojis before they become acquainted with each other. Hence, we explored how changing facial expressions in profile pictures could be an alternative channel to communicate socio-emotional cues. By conducting an online controlled experiment with 186 participants, we established that changing facial expressions of profile pictures can influence the impression of the message receivers toward the sender and the message valence when receiving neutral messages. Furthermore, presenting incongruent profile pictures to positive messages negatively affected the interpretation of the message valence, but did not have much effect on negative messages. We discuss the implications of affective profile pictures in supporting text-based communication.

Academic writing in English can be challenging for non-native English speakers (NNESs). AI-powered rewriting tools can potentially improve NNESs’ writing outcomes at a low cost. However, whether and how NNESs make valid assessments of the revisions provided by these algorithmic recommendations remains unclear. We report a study where NNESs leverage an AI-powered rewriting tool, Langsmith, to polish their drafted academic essays. We examined the participants’ interactions with the tool via user studies and interviews. Our data reveal that most participants used Langsmith in combination with other tools, such as machine translation (MT), and those who used MT had different ways of understanding and evaluating Langsmith’s suggestions than those who did not. Based on these findings, we assert that NNESs’ quality assessment in AI-powered rewriting tools is influenced by the simultaneous use of multiple tools, offering valuable insights into the design of future rewriting tools for NNESs.

Data portability--the capability to transfer one's data from one platform to another--has been described as an important tool for giving individuals more control over their data. It is defined in significant regulations such as the GDPR, and implemented by major online platforms. Unfortunately, there is a lack of research investigating internet users' perceptions and expectations of this technology in specific contexts, which is vital for building effective designs. One particularly important context for studying user perspectives is social media, since it is deeply embedded into daily life and is particularly complex regarding the value and portability of user data. This paper addresses that gap through a survey and interviews of social media users in the United States. We identify current attitudes and practices toward controlling their social media data, and examine participants' impressions about the extent to which data portability may enhance their control. Participants had generally favorable impressions, but had differing opinions about what forms of control are important and the extent to which those could be served by data portability. Based on the results, we propose future directions for improving users' control in the context of social media, such as fine-tuned filtering of data to be transferred and ways to coordinate transfers alongside social contacts.

Non-native speakers (NNS) often face challenges gaining the speaking floor in conversations with native speakers (NS) of a common language. To help NNS to contribute more, we developed a conversational agent that opens up the speaking floor either automatically, after NS have taken a certain number of consecutive speaking turns, or manually, upon NNS request. We compared these automatic and manual agents to a control condition in a laboratory study in which one NNS collaborated with two NS using English as a common language. Participants (N=48) communicated over video conferencing from separate locations in a research institution to collaborate on three survival tasks. Based on data gathered from the experiments, the automatic agent encouraged NNS to participate more, which previous studies had attempted but failed to achieve. Excerpts from group discussions further showed the crucial role of the automatic agent on NNS participation. Interview results suggested that while NNS appreciated the automatic agent's help to participation, NS perceived the agent's interruption as unfair because they thought all members were speaking equally, which was not the case. The mismatch in their perceptions further emphasizes the need to intervene, and we provide design implications based on the results.

Discussions about polarizing topics are essential to have, yet they can easily become hostile, aggressive, or distressing on current social media platforms. Content moderation interventions aim to mitigate this issue, though such approaches are reactionary, removing harmful content only after it has been posted. We conducted a mixed-methods experiment with 40 participants to investigate how a design friction that manipulates the temporal flow during a contentious conversation can foster interpersonal mindfulness, a trait critical for productive communication. Dyads were randomly assigned into the Control Group which received no intervention, and the Experiment Group where participants were limited to sending one message per two-minute interval. Triangulating quantitative and qualitative data from conversation logs, questionnaires, interviews, and computational text analysis, our findings revealed a two-fold effect: Experiment Group participants felt simultaneously frustrated by the intervention as it disrupted the pacing of their conversation and interfered with rapport-building, and appreciative of the intervention as it nudged them towards writing thoughtful and task-focused messages. We discuss implications of these findings for future investigation into the design of temporal interventions to influence interpersonal mindfulness during polarizing online conversations.

Sexual-minority women (SMWs) in China are often subject to strong stigmatization and tend to have limited opportunities to connect with other SMWs in offline contexts. Although dating apps help them connect and seek social support, little is known about SMWs’ practices of self-disclosure and connection-building through those apps. To address this gap, we interviewed 43 SMW dating-app users in China. We found that these SMWs developed distinctive self-disclosure strategies, such as posting non-facial photos and implicitly disclosing their whereabouts by blending location information into photos that only those in the know could understand, to avoid interference from aggressive acquaintances and other risks of unintentional disclosure of their SMW identities. Moreover, they used dating apps not only to recognize other SMWs offline and build relationships with them, but to exchange emotional support in the process of SMW identity development. Our findings have design implications for supporting SMWs and improving their online dating experiences.

COVID-19 has demonstrated the importance of digital contact tracing apps in reducing the spread of disease. Despite people widely expressing interest in using contact tracing apps, actual installation rates have been low in many parts of the world. Prior studies suggest that decisions to use these apps are largely shaped by pandemic beliefs, social influences, perceived benefits and harms, and other factors. However, there is a gap in understanding what factors motivate intention, but not subsequent behavior of actual adoption. Reporting on a survey of 290 U.S. residents, we disentangle the intention-behavior gap by investigating factors associated with installing a contact tracing app from those associated with intending to install, but not actually installing. Our results suggest that social norms can be leveraged to span the intention-behavior gap, and that a privacy paradox may influence people’s adoption decisions. We present recommendations for technologies that enlist individuals to address collective challenges.

No abstract available.

In China, lesbian and bisexual women face intense stigma and difficulties developing relationships with each other. Although prior research has shown that online communities help LGBT people connect and exchange social support, few studies have explored the challenges Chinese lesbian and bisexual women face when initiating, growing, and sustaining such communities, in an atmosphere of platform censorship of LGBT-related content and intense discrimination from non-LGBT people. To address this gap, we interviewed 40 Weibo users in China, four bloggers and 36 followers of their blogs, who self-identified as lesbian or bisexual women. We found that a key technique these bloggers used to initiate their online communities was helping followers publish posts seeking support, sharing personal experiences, and seeking offline relationships. Then, their followers built relationships with bloggers by journaling their daily experiences as lesbian or bisexual women via private-messaging channels. As the communities' members grew more attached to them, bloggers and their followers began to work together to protect themselves from external threats, including Weibo's censorship and non-LGBT+ infiltrators' harassment. However, such attachment to the communities sometimes might lead to conflicts within them, which in turn prompted many members to leave, raising questions about the communities' long-term prospects. Our findings foreground important design considerations for those seeking to help lesbian and bisexual women in China and other discriminatory environments to develop safe online communities.

Information seeking is crucial for people's self-care and wellbeing in times of public crises. Extensive research has investigated empirical understandings as well as technical solutions to facilitate information seeking by domestic citizens of affected regions. However, limited knowledge is established to support international migrants who need to survive a crisis in their host countries. The current paper presents an interview study with two cohorts of Chinese migrants living in Japan (N=14) and the United States (N=14). Participants reflected on their information seeking experiences during the COVID pandemic. The reflection was supplemented by two weeks of self-tracking where participants maintained records of their COVID-related information seeking practice. Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries. They also made strategic use of the Mandarin information to perform selective reading, cross-checking, and contextualized interpretation of COVID-related information in Japanese or English. While such practices enhanced participants' perceived effectiveness of COVID-related information gathering and sensemaking, they disadvantaged people through sometimes incognizant ways. Further, participants lacked the awareness or preference to review migrant-oriented information that was issued by the host country's public authorities despite its availability. Building upon these findings, we discussed solutions to improve international migrants' COVID-related information seeking in their non-native language and cultural environment. We advocated inclusive crisis infrastructures that would engage people with diverse levels of local language fluency, information literacy, and experience in leveraging public services.

Communication technologies have significant social impacts, and it is important to consider how designers' and developers' values shape their design. Increasingly, these technologies are released as continually evolving platforms and services, so their development involves ongoing discussions and debates about unforeseen problems and future directions. However, there is a gap in research about how designers, developers, and other stakeholders engage with values during later stages of development. We investigate discussions about values in the context of open source software development, focusing on projects related to the Decentralized Web. We conducted a large-scale analysis of GitHub issues among diverse yet ideologically-related projects. We show that the percentage of discussions about values increases later in development, and we identify features and outcomes of conflicts related to open source participants' values. Finally, we propose suggestions to improve upon existing discussion practices by supporting common ground among collaborators with diverse goals, perspectives, and experiences.

We are delighted to present this issue of the Proceedings of the ACM on Human-Computer Interaction, which contains scholarship from the Computer-Supported Cooperative Work and Social Computing (CSCW) community. This issue has 293 papers, 94 that were accepted from the April 2021 cycle, 61 that were accepted from the July 2021 cycle, and 138 that were accepted from the January 2022 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process. As Papers Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship during an ongoing global pandemic.

Workers tend to make inferences about one another's commitment and dedication to work depending on what cues are available to them, affecting worker relationships and collaboration outcomes. In this work, we investigate how remote work affects workers' perceptions of their colleagues with different levels of social connectivity, commonly referred to as strong ties and weak ties. When working remotely, workers' perceptions of weak ties may suffer due to the lack of in-person interaction. On the other hand, workers' inferences about their strong ties may also be impacted by losing richer communication cues, even though they had more connections with their strong ties than weak ties. This study explores how remote workers make inferences about engagement levels of and willingness to collaborate with weak ties compared to strong ties. We used a mixed-methods approach involving survey data, experience sampling, and in-depth interviews with 20 workers from different companies in Taiwan. Results showed that workers depended on one-on-one synchronous tools to infer the engagement level of strong ties but used group-based communication tools to infer the engagement level of weak ties. Interestingly, the absence of cues in remote workplaces exacerbated prior impressions formed in the physical office. Furthermore, remote work led workers to develop polarized perceptions of their respective ties. We discuss how characteristics of computer-mediated communication tools and interaction types interplay to affect workers' perceptions of remote colleagues and identify design opportunities for helping remote workers maintain awareness of weak ties.

Prior work in cross-cultural psychology and neuroscience has shown robust variations in visual attention patterns. People from East Asian societies, in which a holistic thinking style predominates, have been found to attend to contextual information in scenes more than Westerners, whose tendency to think analytically expresses itself in greater attention to foreground objects. This paper applies these findings to website design, using an online study to evaluate whether Japanese (N=65) remember more and are faster at finding contextual website information than US Americans (N=84). Our results do not support this hypothesis. Instead, Japanese overall took significantly longer to find information than US participants—a difference that was exacerbated by an increase in website complexity—suggesting that Japanese may holistically take in a website before engaging with detailed information. We discuss implications of these findings for website design and cross-cultural research.

Multiparty collaboration using a common language is often challenging for nonnative speakers (NNS). Conversation can move forward rapidly, with terms and references unfamiliar to NNS often going unexplained because NNS do not request clarification due to cognitive overload or face concerns. Language difficulties may further lead to NNS having a low level of participation in a conversation, which could be a loss for multilingual teams. To help NNS resolve potential confusions due to unfamiliar language use without risking face concerns, we created a conversation agent that asked clarification questions intended to help NNS follow and participate in multiparty conversations. We conducted a within-subjects laboratory experiment with 17 triads of 2 NS and 1 NNS, who performed a series of collaborative tasks under three conditions: a) no agent, b) a high-level agent that resembles a NNS with good command of English, and c) a low-level agent that resembles a NNS with poor English skills. Results suggest that NS made significantly more clarifications in both agent conditions than without an agent. In the high-level agent condition, NNS reported an increase in understanding after the agent's interruption and spoke significantly more. Further, NNS evaluated their communication competence in English highest in the low-level agent condition and lowest in the control condition. Our findings suggest several directions to improve the tool to better facilitate multilingual multiparty communication.

Contact tracing apps have been suggested as a promising approach towards containing viral spread during pandemics, yet their actual use in the COVID-19 pandemic has been low. While researchers have examined reasons for or against installing contact tracing apps, we have less understanding of their ongoing use and how they interact with everyday pressures related to work, communities, and mental well-being. Through a survey of 153 working people in Japan and 15 follow-up interviews, we investigated attitudes toward installing and using Japan's national contact tracing app, COCOA, and how these related to respondents' daily lives, work structures, and general attitudes about the pandemic. We found that motivations about installing the app differed from those related to ongoing usage. Specifically, we identified ways that people navigate uncertain norms of behaviour during the pandemic, and how people consider individual risks such as COVID-related stigmas, anxiety, and financial precarity when deciding if and how to use COCOA. In light of these, we discuss the tension between COCOA's design and desires to protect oneself by selective controlling disclosures. We note that perceived risks are closely tied to respondents' local contexts, and based on our analysis, we identify ways to address these challenges and tensions through design interventions at multiple scales.

Chatbots are regarded as a promising technology for delivering guidance. Prior studies show that chatbots have the potential of coaching users to learn different skills; however, several limitations of chatbot-based approaches remain. People may become disengaged from using chatbot-guided systems and fail to follow the guidance for complex tasks. In this paper, we design chatbots with (HC) and without (OC) human support to deliver guidance for people to practice journaling skills. We conducted a mixed-method study with 35 participants to investigate their actual interaction, perceived interaction, and the effects of interacting with the two chatbots. The participants were randomly assigned to use one of the chatbots for four weeks. Our results show that the HC participants followed the guidance more faithfully during journaling practices and perceived a significantly higher level of engagement and trust with the chatbot system than the OC participants. However, after finishing the journaling-skill training session, the OC participants were more willing to keep using the learned skills than the HC participants. Our work provides new insights into the design of integrating human support into chatbot-based interventions for delivering guidance.

We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests.

Chatbots have great potential to serve as a low-cost, effective tool to support people's self-disclosure. Prior work has shown that reciprocity occurs in human-machine dialog; however, whether reciprocity can be leveraged to promote and sustain deep self-disclosure over time has not been systematically studied. In this work, we design, implement and evaluate a chatbot that has self-disclosure features when it performs small talk with people. We ran a study with 47 participants and divided them into three groups to use different chatting styles of the chatbot for three weeks. We found that chatbot self-disclosure had a reciprocal effect on promoting deeper participant self-disclosure that lasted over the study period, in which the other chat styles without self-disclosure features failed to deliver. Chatbot self-disclosure also had a positive effect on improving participants' perceived intimacy and enjoyment over the study period. Finally, we reflect on the design implications of chatbots where deep self-disclosure is needed over time.

Chatbots (conversational agents) are increasingly receiving attention in mental health domains because they elicit honest self-disclosure about personal experiences and emotions. Although such self-disclosure contents can be useful for gauging mental status, little research has addressed how to automatically assess mental status from self-disclosures to a chatbot. If a chatbot can automatically assess the mental status of users, it can help them improve their mental wellness or facilitate access to mental professionals. In this paper, we examine whether indicators that identify depression from written texts (e.g., social media posts) are also useful for assessing mental status from disclosures to a chatbot. We first ran a study with 30 participants who engaged in daily journaling with a chatbot that prompted them to record their moods and experiences for three weeks. We then divided the participants' self-disclosure data into three groups based on their mental state changes before and after the study: improved vs. deteriorated vs. no change. Comparing the data among the three groups, participants whose mental states deteriorated during the study gradually used fewer positive emotion and concrete words but more negative emotion words when describing their daily experiences and feelings to the chatbot.

Chatbots are becoming increasingly popular. One promising application for chatbots is to elicit people's self-disclosure of their personal experiences, thoughts, and feelings. As receiving one's deep self-disclosure is critical for mental health professionals to understand people's mental status, chatbots show great potential in the mental health domain. However, there is a lack of research addressing if and how people self-disclose sensitive topics to a real mental health professional (MHP) through a chatbot. In this work, we designed, implemented and evaluated a chatbot that offered three chatting styles; we also conducted a study with 47 participants who were randomly assigned into three groups where each group experienced the chatbot's self-disclosure at varying levels respectively. After using the chatbot for a few weeks, participants were introduced to a MHP and were asked if they would like to share their self-disclosed content with the MHP. If they chose to share, the participants had the option of changing (adding, deleting, and editing) the content they self-disclosed to the chatbot. Comparing participants' self-disclosure data the week before and the week after sharing with the MHP, our results showed that, within each group, the depth of participants' self-disclosure to the chatbot remained after sharing with the MHP; participants exhibited deeper self-disclosure to the MHP through a more self-disclosing chatbot; further, through conversation log analysis, we found that some participants made different edits on their self-disclosed content before sharing it with the MHP. Participants' interview and survey feedback suggested an interaction between participants' trust in the chatbot and their trust in the MHP, which further explained participants' self-disclosure behavior.

Video communication systems have been suffered from the narrow field of view. To solve this limitation, one study proposed symmetric 360° video communication system by combining an omnidirectional camera and a hemispherical display. However, the system still had several issues, e.g., the invisibility of hemisphere which was at the opposite side from a user caused the inconvenience of observing the remote environment. To solve these issues, we introduce OmniGlobe, a novel symmetric full 360° video communication system which incorporates an omnidirectional camera, a full spherical display, and several visual or interactive techniques. Based on an experiment, we could indicate that our system is effective in reducing the inconvenience of observing the remote environment and increased the remote space awareness and user's gaze awareness to support remote collaboration. We also discuss the takeaways, limitations and application areas in our system which help improve the system.

Collaborating using a common language can be challenging for non-native speakers (NNS). These challenges can be reduced when native speakers (NS) adjust their speech behavior for NNS, for example by speaking more slowly. In this study, we examined whether the use of real-time speech rate feedback (a speech speedometer) would help NS monitor their speaking speed and adjust for NNS accordingly. We conducted a laboratory experiment with 20 triads of 2 NS and 1 NNS. NS in half of the groups were given the speech speedometer. We found that NS with the speech speedometer were significantly more motivated to slow down their speech but they did not actually speak more slowly, although they made other speech adjustments. Furthermore, NNS perceived the speech of NS with the speedometer less clear, and they felt less accommodated. The results highlight the need for tools that create scaffolding to help NS make speech accommodations. We conclude with some design ideas for these scaffolding tools.

Field-of-view limitation has been a long-standing issue in video communication systems. With the advancement of omnidirectional panoramic technology, the omnidirectional camera, which can provide a 360° field of view, has become increasingly popular in the last few years. Previous research indicated that one-way video communication systems with a wider field of view improve task efficiency. Therefore, we propose to utilize omnidirectional cameras in a symmetrical video communication system and study how this configuration affects remote collaboration. In this study, we conducted experiments based on two conditions, which are an omnidirectional camera with a spherical display and an omnidirectional camera with a horizontally placed 2D flat display. Under these conditions, we analyzed how the display types affected remote collaboration. Our results show that participants marginally preferred the spherical display to the 2D flat display. We also show the advantages and disadvantages of each display. The findings contribute to our understanding of how to design an environment for remote collaboration that captures and shows a 360° panoramic view of a remote site.

Previous research has shown that tracking technologies have the potential to help family caregivers optimize their coping strategies and improve their relationships with care recipients. In this paper, we explore how sharing the tracked data (i.e., caregiving journals and patient's conditions) with other family caregivers affects home care and family communication. Although previous works suggested that family caregivers may benefit from reading the records of others, sharing patients' private information might fuel negative feelings of surveillance and violation of trust for care recipients. To address this research question, we added a sharing feature to the previously developed tracking tool and deployed it for six weeks in the homes of 15 family caregivers who were caring for a depressed family member. Our findings show how the sharing feature attracted the attention of care recipients and helped the family caregivers discuss sensitive issues with care recipients.

When multiple non-native speakers (NNSs) who share the same native language join a group discussion with native speakers (NSs) of the common language used in the discussion, they sometimes switch back and forth between common language and their native language to reach common ground. However, such code-switching makes others feel excluded and thus not considered appropriate during formal meetings. To offer NNSs more flexibility to code-switch in a group discussion while minimizing the cost of excluding others, we introduced a language support tool that automatically detects a user's spoken language, and then transcribe as well as translate them into another language (common language or NNS's native language). In a within-subject study involving 19 quads (two Japanese and two Chinese) in a collocated setting, participants were asked to perform a series of decision-making tasks with and without the tool. Results showed that the language support tool encouraged diverse use of language during a meeting, resulting in more participation from NNSs - they increased active initiating behaviors from NNSs. Although the perceived quality of collaboration became lower, it also elicited helping behaviors among the NNS pairs.

Current videoconferencing systems can be roughly divided into two types: a window-type where a computer display works as a window to reveal a remote partner, and a mirror-type whose display shows the mirrored reflections of both participants. While mirror-type systems enhance the feeling of togetherness by merging the two sites into one display, an inherent problem remains. Despite the mirror metaphor, the partner has no physical body in front of the display. To cope with this incongruence, we placed a partition in front of the display. Across that partition we further also placed a robotic table and a robotic bench that move based on the partner's behavior. The experiments indicated that the table and bench successfully facilitated feeling as if there were the partner's physical body was present at the opposite side of the partition.

In this paper we report on a pervasive practice in video-mediated communication: where participants show one another one or more objects. This is a distinct activity from others considered by researchers of video-mediated technologies that focus on a face-to-face orientation, or just on the support necessary to help people to refer to objects. We first present examples of this pervasive phenomenon in naturally occurring Skype conversations, revealing how this conduct is configured and organized within the interaction between participants. We reveal how the subtle adjustment of the position of the body, the head and gaze with respect to the handheld objects offers crucial resources for participants to achieve joint seeing. Then we report on a quite different setting, a naturalistic experiment where participants collaborate on a collective task with remote colleagues through maneuverable, orientable devices (Kubis). Again, in these experiments participants frequently show objects, and at times the devices provide additional resources to support these activities. But at other times they also involve some difficulties. We conclude by suggesting possible technological developments, some quite simple, others more radical, that might support participants to show objects, whether they are in domestic settings or undertaking work activities.

Previous research on healthcare technologies has shown how health tracking promotes desired behavior changes and effective health management. However, little is known about how the family caregivers' use of tracking technologies impacts the patient-caregiver relationship in the home. In this paper, we explore how health-tracking technologies could be designed to support family caregivers cope better with a depressed family member. Based on an interview study, we designed a simple tracking tool called Family Mood and Care Tracker (FMCT) and deployed it for six weeks in the homes of 14 family caregivers who were caring for a depressed family member. FMCT is a tracking tool designed specifically for family caregivers to record their caregiving activities and patient's conditions. Our findings demonstrate how caregivers used it to better understand the illness and cope with depressed family members. We also show how our tool improves family communication, despite the initial concerns about patient-caregiver conflicts.

The meaning we attribute to another's actions significantly impact our subsequent behaviors and interactions towards that person. Distributed teams often combine native speakers (NS) and non-native speakers (NNS) and are particularly prone to making attribution errors. Language difficulties place NNS under a higher cognitive load, potentially leading NS to make inaccurate attributions of NNS. We conducted an exploratory laboratory study to investigate the attributions NS and NNS form about each other in multiparty videoconferencing. Our findings revealed significant mismatches in NS' attributions of NNS behavior, but no significant mismatch in NNS' attributions of NS behavior. Due to cognitive overload stemming from language challenges, NNS were only able to engage in "compromised" impression management during the task. Yet, NS were relatively unaware of how profoundly language difficulties impacted NNS' behaviors. Our findings identify opportunities for technology support for NS-NNS interactions, particularly with regards to impression construction and impression management.

In multilingual communication through a common language among both native speakers (NS) and non-native speakers (NNS), NNS may encounter problems in comprehending the messages of NS or following conversations. Even though automated speech recognition (ASR) transcripts provide support to NNS, such transcripts may contain errors and impose the need to simultaneously listen and read. To reduce this burden, we propose adding another channel (i.e., highlighting) through which NS can help NNS by highlighting the critical parts of transcripts, thus making them more useful to NNS. In a laboratory study involving 14 triads (two NS and one NNS in each triad), participants engaged in collaborative discussions under two conditions: audio conferencing plus ASR transcripts with and without the highlighting function. NS showed various motivations to perform the extra task of highlighting. The highlighting efforts helped NS themselves focus on the discussion and enhanced their task performance while increasing the clarity and comfort perceived by NNS during communication. Having NS generating highlights can benefit both NS and NNS, but in different ways. We discuss the implications for research and design of multilingual collaborative work.

Global virtual teams experience intercultural conflict. Yet, research on how Computer-Mediated Communication (CMC) tools can mitigate such conflict is minimal. We conducted an experiment with 30 Japanese-Canadian dyads who completed a negotiation task over email. Dyads were assigned to one of three conditions: C1) no feedback; C2) automated language feedback of participant emails based on national culture dimensions; and C3) automated language feedback (as in C2), and participants' shared self-reflections of that feedback. Results show Japanese and Canadian partners interpreted the negotiation task differently, resulting in perceptions of intercultural conflict and negative impressions of their partner. Compared to C1, automated language feedback (C2) and shared self-reflections (C3) made cultural differences more salient, motivating participants to empathize with their partner. Shared self-reflections (C3) served as a meta-channel to communication, providing insight into each partner's intentions and cultural values. We discuss implications for CMC tools to mitigate perceptions of intercultural conflict.

Previous research has made various efforts to produce human-like presence of autonomous social robots. However, such efforts often require costly equipment and complicated mechanisms. In this paper, we propose a new method that makes a user feel as if an autonomous robot is controlled by a remote operator, with virtually no cost. The basic idea is to manipulate people's knowledge about a robot by using priming technique. Through a series of experiments, we discovered that subjects tended to deduce the presence/absence of a remote operator based on their prior experience with that same remote operator. When they interacted with an autonomous robot after interacting with a teleoperated robot (i.e., a remote operator) whose appearance was identical as the autonomous robot, they tended to feel that they were still talking with the remote operator. The physically embodied talking behavior reminded the subjects of the remote operator's presence that was felt at the prior experience. Their deductions of the presence/absence of a remote operator were actually based on their “beliefs” that they had been interacting with a remote operator. Even if they had interacted with an autonomous robot under the guise of a remote operator, they tended to believe that they were interacting with a remote operator even when they subsequently interacted with an autonomous robot.

Real-time transcripts generated by automatic speech recognition (ASR) technologies hold potential to facilitate non-native speakers’ (NNSs) listening comprehension. While introducing another modality (i.e., ASR transcripts) to NNSs provides supplemental information to understand speech, it also runs the risk of overwhelming them with excessive information. The aim of this paper is to understand the advantages and disadvantages of presenting ASR transcripts to NNSs and to study how such transcripts affect listening experiences. To explore these issues, we conducted a laboratory experiment with 20 NNSs who engaged in two listening tasks in different conditions: audio only and audio+ASR transcripts. In each condition, the participants described the comprehension problems they encountered while listening. From the analysis, we found that ASR transcripts helped NNSs solve certain problems (e.g., “do not recognize words they know”), but imperfect ASR transcripts (e.g., errors and no punctuation) sometimes confused them and even generated new problems. Furthermore, post-task interviews and gaze analysis of the participants revealed that NNSs did not have enough time to fully exploit the transcripts. For example, NNSs had difficulty shifting between multimodal contents. Based on our findings, we discuss the implications for designing better multimodal interfaces for NNSs.

Conversational grounding, or establishing mutual knowledge that messages have been understood as intended, can be difficult to achieve when some conversational participants are using a non-native language. These difficulties in grounding can be challenging for native speakers to detect. In this paper, we examine the value of signaling potential grounding problems to native speakers (NS) by displaying how non-native speakers (NNS) use automated transcripts and bilingual dictionaries. We conducted a laboratory experiment in which NS and NNS of English collaborated via audio conferencing on a map navigation task. Triads of one NS guider, one NS follower, and one NNS follower performed the task using one of three awareness displays: (a) a no awareness display that showed only the automated transcripts, (b) a general awareness display that showed whether each follower was reading the automated transcripts and/or translating a word; or (c) a detailed awareness display that showed which line of the transcripts a follower was reading and/or which words he/she was translating. NS guiders and NNS followers collaborated most successfully with the detailed awareness display, while NS guiders and NS followers performed equally across conditions. Our findings suggest several ways to improve systems to support multilingual collaboration.

In this paper we report on some experiments with a high fidelity media space, t-Room, an immersive system that presents full scale, real-time images of co-participants. The system has been enhanced to provide more flexibility in the ways participants could organise themselves and the materials they are working on. Drawing on some quasi-naturalistic experiments, where the participants were required to undertake a range of complex tasks, we consider the formations they adopt and the issues and problems that arise when they attempt to establish and preserve a common focus and alignment. We conclude by briefly discussing the consequences for developing advanced spaces to support collaborative work and understanding complex video-mediated interaction.

Real-time transcripts generated by automated speech recognition (ASR) technologies have the potential to facilitate communication between native speakers (NS) and non-native speakers (NNS). Previous studies of ASR have focused on how transcripts aid NNS speech comprehension. In this study, we examine whether transcripts benefit multiparty real-time conversation between NS and NNS. We hypothesized that ASR transcripts would be more beneficial when the transcripts were publicly shared by all group members as opposed to when they were seen only by the NNS. To test our hypothesis, we conducted a lab experiment in which 14 groups of native and non-native speakers engaged in a story-telling task. Half of the groups received private transcripts that were available only to the NNS; the other half received publicly shared transcripts that were available to all group members. NS spoke more clearly, and both NS and NNS rated the quality of communication higher, when transcripts were publicly shared. These findings inform the design of future tools to support multilingual group communication.

Previous studies have shown that people can effectively detect emotions in text-only messages written in their native languages. But is this the same for non-native speakers' In this paper, we conduct an experiment where native English speakers (NS) and Japanese non-native English speakers (NNS) rate the emotional valence in text-only messages written by native English-speaking authors. They also annotate all emotional cues (words, symbols and emoticons) that affected their rating. Accuracy of NS and NNS ratings and annotations are calculated by comparing their average correlations with author ratings and annotations used as a gold standard. Our results conclude that NNS are significantly less accurate at detecting the emotional valence of messages, especially when the messages include highly negative words. Although NNS are as accurate as NS at detecting emotional cues, they are not able to make use of symbols (exclamation marks) and emoticons to detect the emotional valence of text-only messages.

To support astronomy education, we developed a tangible learning environment called the tangible earth system. To clarify its problems, we defined an assessment framework from the aspects of curriculum guidelines, design guidelines of tangible learning environments, and epistemology of agency. Based on the analysis of our small-scale user study, we identified problems of the system in terms of location, dynamics, and correspondence parameters.

Depression is one of the most common disabilities in developed countries. Despite its often devastating impact on families, scant research has focused on how to facilitate the well-being of family caregivers. The aim of this paper is to uncover the challenges faced by family caregivers and support their well-being with the use of technologies. To understand the emotional and social burden of caregivers and how they handle their stress, we conducted in-depth interviews with 15 individuals who have cared for a depressed family member. Our findings reveal the multifaceted dilemma of caring for a depressed family member as well as the various strategies engaged in by caregivers to improve their own situations. Based on our findings, we suggest design implications for healthcare technologies to improve the wellness of caregivers who are looking after depressed family members.

Previous research has shown that audio communication is particularly difficult for non-native speakers (NNS) during multilingual collaborations. Especially when audio signals become distorted, NNS are overburdened by not only having to communicate with imperfect language skills, but also compensating for the deteriorations. Under these faulty audio conditions, NNS need to pay extra time and effort to understand the conversation. In order to give NNS more time to process conversations, we tested the insertion of silent gaps (from 0.2 to 0.4 seconds) between conversational turns. First, gaps were inserted into a previously taped conversation, resulting in a significant improvement of NNS's understanding of the conversation. Second, gaps were inserted during a real-time audio conference by adding artificial delay between native speakers. The results show that the added delays have a combination of beneficial and detrimental effects for both native and non-native speakers. The findings have implications towards how audio conferencing can be improved for NNS.

A concern with “embodied action” has informed both the analysis of everyday action through technologies and also suggested ways of designing innovative systems. In this article, we consider how these two programs, the analysis of everyday embodied interaction on the one hand, and the analysis of technically-mediated embodied interaction on the other, are interlinked. We draw on studies of everyday interaction to reveal how embodied conduct is embedded in the environment. We then consider a collaborative technology that attempts to provide a coherent way of presenting life-sized embodiments of participants alongside particular features of the environment. These analyses suggest that conceptions of embodied action should take account of the interactional accomplishment of activities and how these are embedded in the material environment.

Various systems for hand tool skill training have been developed in the domain of haptic displays. These systems typically present force to a learner's palm by directly actuating the tool. However, this approach is sometimes ineffective because learners have difficulty sensing the haptic feedback from the tool when they are holding it tightly. Thus, we propose a different approach (OCHD) that effectively guides the learner's hand by presenting force to the back of his/her hand as if an instructor is holding it. A preliminary experiment showed that OCHD effectively guides users with less actuator drive force than cases where the tool is directly actuated.

In this paper we report on some experiments with a high fidelity media space, t-Room, an immersive system that presents full scale, real-time images of co-participants who are in similar spaces many miles apart. Although being designed to provide a coherent environment for interaction the system introduces a number of incongruities, both in time and space. Drawing on some quasi-naturalistic experiments, where the participants were required to analyse complex data, we consider how the participants manage these incongruities. We conclude by briefly discussing the resources people utilize to produce and recognize conduct in embodied spaces.

In this study, we examine how remote collaborators' upper body view affects collaboration when people engage in multiparty fluid tabletop activities across distances. We experimentally investigated the effects of upper body view on four-person group tabletop collaboration, two-by-two at identical locations: shared tabletop vs. shared tabletop plus upper body view. Although previous research has often failed to illustrate the advantages of showing remote participants' upper body view, our study showed that task performance was significantly higher in conditions with upper body view. Furthermore, participants with upper body view tended to take a step away from their remote partners to effectively glance at them while taking a comparable perspective of the tabletop objects. Detailed analysis of the video recordings revealed that upper body view was effective for fluid tabletop collaboration because it helped achieve joint perspective and helped estimate the timing and rough location of subsequent tabletop activity.

Collaborative distributed tabletop activities involving real objects are complicated by invisibility factors introduced into the workspace. In this paper, we propose a technique called "remote lag" to alleviate the problems caused by the invisibility of remote gestures. The technique provides people with instant playback of remote gestures to recover from the missed context of coordination. To examine the effects of the proposed technique, we studied four-person groups who engaged in two mentoring tasks using physical objects with and without remote lags. Our results show that remote lags effectively alleviated the invisibility problems, resulting in fewer questions/confirmations and redundant instructions during collaboration. The technique also decreased the overall workload of workers as well as the temporal demands for both helpers and workers.

When people communicate in their native languages using machine translation, they face various problems in constructing common ground. This study investigates the difficulties of constructing common ground when multiparty groups (consisting of more than two language communities) communicate using machine translation. We compose triads whose members come from three different language communities--China, Korea, and Japan--and compare their referential communication under two conditions: in their shared second language (English) and in their native languages using machine translation. Consequently, our study suggests the importance of not only grounding between speaker and addressee but also grounding between addressees in constructing effective machine-translation-mediated communication. Furthermore, to successfully build common ground between addressees, it seems important for them to be able to monitor what is going on between a speaker and other addressees.

Previous studies have demonstrated the importance of providing users with a coherent environment across distant sites. To date, it remains unclear how such an environment affects people's gestures and their comprehension. In this study, we investigate how a coherent environment across distant sites affects people's hand gestures when collaborating on physical tasks. We present video-mediated technology that provides distant users with a coherent environment in which they can freely gesture toward remote objects by the unmediated representations of hands. Using this system, we examine the values of a coherent environment by comparing remote collaboration on physical tasks in a fractured setting versus a coherent setting. The results indicate that a coherent environment facilitates gesturing toward remote objects and their use improves task performance. The results further suggest that a coherent environment improves the sense of co-presence across distant sites and enables quick recovery from misunderstandings.

没入型VCSにおいて，座席配置とディスプレイとの相対位置の違いによって，話者の体躯の向きが変わることが観察され，それが話者交代，遠隔ユーザとの一体感，議論の満足度に深く影響を及ぼすことを発見した．

In this paper, we present t-Room, the next generation video communication system we are developing. Our approach is to build rooms with identical layouts, including walls of display panels on which users and physical or virtual objects are all shown at life-size. In this way, the user space enclosed by t-Room's surrounding displays can be shared as a common space at any other site. In other words, the enclosed spaces overlap each other. This configuration effectively provides symmetric reproduction of the audio-visual information surrounding local and remote users and objects. The feeling provided by t-Room is different from that by conventional videoconferencing systems, since there is no spatial barrier separating users such as the video screen of a conventional videoconferencing system. Furthermore, t-Room benefits in every way from Next Generation Network (NGN) technology: QoS, service productivity, and security. We view t-Room as a future form of telephone service.

Even though multilingual communities that use machine translation to overcome language barriers are increasing, we still lack a complete understanding of how machine translation affects communication. In this study, eight pairs from three different language communities--China, Korea, and Japan--worked on referential tasks in their shared second language (English) and in their native languages using a machine translation embedded chat system. Drawing upon prior research, we predicted differences in conversational efficiency and content, and in the shortening of referring expressions over trials. Quantitative results combined with interview data show that lexical entrainment was disrupted in machine translation-mediated communication because echoing is disrupted by asymmetries in machine translations. In addition, the process of shortening referring expressions is also disrupted because the translations do not translate the same terms consistently throughout the conversation. To support natural referring behavior in machine translation-mediated communication, we need to resolve asymmetries and inconsistencies caused by machine translations.

Multilingual communities using machine translation to overcome language barriers are showing up with increasing frequency. However, when a large number of translation errors get mixed into conversations, users have difficulty completely understanding each other. In this paper, we focus on misconceptions found in high volume in actual online conversations using machine translation. We first examine the response patterns in machine translation-mediated communication and associate them with misconceptions. Analysis results indicate that response messages to include misconceptions posted via machine translation tend to be incoherent, often focusing on short phrases of the original message. Next, based on the analysis results, we propose a method that automatically predicts the occurrence of misconceptions in each dialogue. The proposed method assesses the tendency of each dialogue including misconceptions by calculating the gaps between the regular discussion thread (syntactic thread) and the discussion thread based on lexical cohesion (semantic thread). Verification results show significant positive correlation between actual misconception frequency and gaps between syntactic and semantic threads, which indicate the validity of the method.

Multilingual communities using machine translation to overcome language barriers are showing up with increasing frequency. However, when a large number of translation errors get mixed into conversation, it becomes difficult for users to fully understand each other. In this paper, we focus on misconceptions found in high volume in actual online conversations using machine translation. By comparing responses via machine translation and responses without machine translation, we extract two response patterns, which may be strongly related to the occurrence of misconceptions in machine translation-mediated communication. The two response patterns are that users tend to respond to short phrases of the original message and tend to trip on the wording of the original message when responding via machine translation.