Self-disclosure on social media has become increasingly popular, offering unique benefits to people who have experienced racism in real life. Insofar as encounters with racism universally produce adverse effects, they manifest differently based on the roles individuals occupy and the context in which they unfold. The elucidation of such variations sheds light on the universalities and differences of racism's impacts across different marginalized groups and also their specific needs in different scenarios. This research bridges studies of online self-disclosure and experiences of racism to understand the roles and scenarios in the self-disclosure of experiences of racism on social media. Adopting the Construal Level Theory (CLT), we first develop a framework that categorizes the narratives of experiences of racism into nine categories by identifying the psychological distance between the narrator and both the perpetrator and the victim. Guided by our framework, we then perform content analysis on 874 submissions collected from the subreddit r/racism. We found a considerable number of disclosed narratives where the narrators were only bystanders, even if the victims were strangers to them. Additionally, a substantial portion of the narratives portrayed racism occurring within the narrators' close social circles. Our qualitative analysis revealed distinct emotional responses, dilemmas, and challenges within and across different narrative categories, shaped by the interplay of roles and scenarios. Our novel framework establishes a topology to dissect the complexities of narratives of experiences of racism, contributing to the general study of experiences of racism. Our discovery of the prominent themes bears significance to digital platforms, community organizers, and policymakers in helping people cope with racist encounters. We call for future research to focus on the impact of racism on bystanders in racist encounters, the attention on the everydayness of racism experiences, and the pressing need for available offline anti-racist resources.?

The COVID-19 pandemic broke down the human infrastructure of many community-based programs, disrupting in-person care services for low-resourced families. Yet, minimal work has explored how actors repair these breakdowns and how other infrastructures may interfere with repairs in such contexts. Interviewing adolescents and adults affiliated with a youth empowerment program, we used the pandemic to examine how a human infrastructure that previously facilitated a sense of community broke down and how members attempted to repair this infrastructure. While organized activities, resources, and interpersonal interactions aligned to facilitate in-person care that established a sense of community, incorporating information and communication technologies to align a sociotechnical infrastructure during social restrictions could not overcome multiple constraints imposed by other infrastructures that limited this sense of community. We discuss limitations to care and aligning together multiple disjointed infrastructures, calling for CSCW researchers to critically consider asset-based design as a methodology that might help sustain a community's well-being.

Social injustices are commonly discussed on social media, presenting opportunities for youth to engage with this content and develop into engaged citizens. While much has been written about youths’ online activism, less is known about how engaging with sociopolitical content may build their capacity for activist work. We explore the extent to which youths’ engagement with sociopolitical content on various social media platforms is associated with critical consciousness—an awareness of inequities, the motivation to address them, and action that combats injustice. To investigate this relationship, we conducted a survey with 339 high school-aged youths. While sociopolitical engagement on some platforms was positively associated with youths’ critical consciousness measures, sociopolitical engagement on other platforms was negatively associated. Qualitative post-hoc analysis was used to suggest reasons for possible differences. In light of our findings, we discuss the relationship between online sociopolitical engagement and critical consciousness and suggest directions for future work.

Participants’ body language during interactions with others in a group meeting can reveal important information about their individual personalities, as well as their contribution to a team. Here, we focus on the automatic extraction of visual features from each person, including her/his facial activity, body movement, and hand position, and how these features co-occur among team members (e.g., howfre- quently a person moves her/his arms or makes eye contact when she/he is the focus of attention of the group). We correlate these features with user questionnaires to reveal relationships with the "Big Five" personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroti- cism), as well as with team judgements about the leader and dominant contributor in a conversation. We demonstrate that our algorithms achieve state-of-the-art accuracy with an average of 80% for Big-Five personality trait prediction, potentially enabling integration into automatic group meeting understanding systems.

Social Networking Sites (SNS) provide a platform for engaging youth in activism (e.g., by helping mobilize civic action). While youth typically employ casual approaches to online activism (i.e., quick actions, such as broadcast posts to advertise social justice events), more strategic practices (i.e., those that are more creative and informed) can increase the likelihood of successful online campaigns. However, little work has examined how youth activists can be supported to use SNS more strategically. To address this research gap, we conducted interviews with youth activists, exploring how youth made sense of social network visualizations and their perspectives on how such tools could support their activism efforts. Our findings characterize how participants made inferences about followers? identities based on their hashtag use, and how they used those inferences in outreach decisions. We conclude with design implications for future research in this area.

Social Networking Sites (SNS) offer youth activists and youth empowerment organizations (where adults help youth address community issues) opportunities for civic action. Impression management is critical to youth empowerment organizations' work online, as they attempt to influence the opinions of their audience. However, there is a dearth of research characterizing online impression management in the context of youth empowerment organizations. To address this research gap, we conducted a qualitative study investigating the use of SNS in a youth empowerment organization. Using Goffman's dramaturgical model, we characterized how youth tried to hack SNS algorithms, and their desire to better identify their audience. Our findings reveal how youth use SNS to create authentic images and connections with their audience. On the other hand, we discuss adults' desire to convey a curated organizational image and challenges that arose. We conclude with design implications for tools that support impression management online for youth activists.

Collaborative group tasks require efficient and productive verbal and non-verbal interactions among the participants. Studying such interaction patterns could help groups perform more efficiently, but the detection and measurement of human behavior is challenging since it is inherently multimodal and changes on a millisecond time frame. In this paper, we present a method to study groups performing a collaborative decision-making task using non-verbal behavioral cues. First, we present a novel algorithm to estimate the visual focus of attention (VFOA) of participants using frontal cameras. The algorithm can be used in various group settings, and performs with a state-of-the-art accuracy of 90%. Secondly, we present prosodic features for non-verbal speech analysis. These features are commonly used in speech/music classification tasks, but are rarely used in human group interaction analysis. We validate our algorithms on a multimodal dataset of 14 group meetings with 45 participants, and show that a combination of VFOA-based visual metrics and prosodic-feature-based metrics can predict emergent group leaders with 64% accuracy and dominant contributors with 86% accuracy. We also report our findings on the correlations between the non-verbal behavioral metrics with gender, emotional intelligence, and the Big 5 personality traits.

Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants' privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. Participants' post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions.

The social stigma looming over disclosures of sexual violence discourages many women from publicly sharing their stories, limiting their ability to seek support and obscuring the epidemic of sexual violence against women. By inviting women to share their ordinarily silenced stories, the hashtag #MeToo surfaced a network of survivors to confront this stigma. Through a mixed-methods analysis of over 1.8 million tweets posted during the first two weeks after #MeToo gained widespread popularity in 2017, we map the landscape of disclosures that emerged and disentangle the effects of network-level reciprocal disclosures, or disclosures made in reaction to seeing others disclose. We detail how survivors disclosed a diversity of sexual violence experiences in solidarity with others, composing nearly half of all authored tweets and comprising a disproportionate number of interactions within the #MeToo network. Further, we show that the more disclosures an individual potentially saw prior to disclosing, the more likely they were to share details with their disclosure. We argue that such network-level reciprocal disclosures may have reduced stigma, creating a counterpublic space safe for disclosure which, subsequently, generated more disclosures. Our work illustrates how feminist hashtag activism, like #MeToo, can unify individual and collective narratives to dismantle the stigma surrounding disclosures of sexual violence. Content warning: This article heavily discusses issues of sexual violence against women.

Group meetings can suffer from serious problems that undermine performance, including bias, "groupthink", fear of speaking, and unfocused discussion. To better understand these issues, propose interventions, and thus improve team performance, we need to study human dynamics in group meetings. However, this process currently heavily depends on manual coding and video cameras. Manual coding is tedious, inaccurate, and subjective, while active video cameras can affect the natural behavior of meeting participants. Here, we present a smart meeting room that combines microphones and unobtrusive ceiling-mounted Time-of-Flight (ToF) sensors to understand group dynamics in team meetings. We automatically process the multimodal sensor outputs with signal, image, and natural language processing algorithms to estimate participant head pose, visual focus of attention (VFOA), non-verbal speech patterns, and discussion content. We derive metrics from these automatic estimates and correlate them with user-reported rankings of emergent group leaders and major contributors to produce accurate predictors. We validate our algorithms and report results on a new dataset of lunar survival tasks of 36 individuals across 10 groups collected in the multimodal-sensor-enabled smart room.