Peer review determines which scholarship is legitimized; however, review biases often disadvantage scholarship that diverges from the norm. Human–Computer Interaction (HCI) lacks a systemic inquiry into how such biases affect underrepresented Global South (GS) scholarship. To address this critical gap, we conducted four focus groups with 16 HCI researchers studying the GS. Participants reported experiencing reviews that confined them to development research, dismissed their theoretical contributions, and questioned situated knowledge from GS communities. Both as authors and reviewers, participants reported experiencing the epistemic burden of over-explaining why knowledge from GS communities matters. Further, they noted being tokenized as “cultural experts” when assigned to review papers and pointed out that the hidden curriculum of writing HCI papers often gatekeeps GS scholarship. Using epistemic oppression as a lens, we discuss how review practices marginalize GS scholarship and outline actionable strategies for nurturing equitable epistemological evaluation of HCI scholarship.

Large language models (LLMs) are increasingly used to promote prosocial and constructive discourse online. Yet little is known about how these models negotiate and shape underlying values when reframing people’s arguments on value-laden topics. We conducted experiments with 465 participants from India and the United States, who wrote comments on homophobic and Islamophobic threads, and reviewed human-written and LLM-rewritten constructive versions of these comments. Our analysis shows that LLM systematically diminishes Conservative values while elevating prosocial values such as Benevolence and Universalism. When these comments were read by others, participants opposing same-sex marriage or Islam found human-written comments more aligned with their values, whereas those supportive of these communities found LLM-rewritten versions more aligned with their values. These findings suggest that value homogenization in LLM-mediated prosocial discourse runs the risk of marginalizing conservative viewpoints on value-laden topics and may inadvertently shape the dynamics of online discourse.

WhatsApp is the largest social media platform in the Global South and is a virulent force in global misinformation and political propaganda. Due to end-to-end encryption WhatsApp can barely review any content and mostly rely on volunteer moderation by group admins. Yet, little is known about how WhatsApp group admins manage their groups, what factors and values influence moderation decisions, and what challenges they face while managing their groups. To fill this gap, we interviewed admins of 32 diverse groups and reviewed content from 30 public groups in India and Bangladesh. We observed notable differences in the formation, members' behavior, and moderation of public versus private groups, as well as in how WhatsApp admins operate compared to those on other platforms. We used Baumrind's typology of 'parenting styles' as a lens to examine how admins enact care and control during volunteer moderation. We identified four styles based on how caring and controlling the admins are and discuss design recommendations to help them better manage problematic content in WhatsApp groups.

This paper examines if large language models (LLMs) can help people write constructive comments on divisive social issues due to the difficulty of expressing constructive disagreement online. Through controlled experiments with 600 participants from India and the US, who reviewed and wrote constructive comments on threads related to Islamophobia and homophobia, we observed potential misalignment between how LLMs and humans perceive constructiveness in online comments. While the LLM was more likely to prioritize politeness and balance among contrasting viewpoints when evaluating constructiveness, participants emphasized logic and facts more than the LLM did. Despite these differences, participants rated both LLM-generated and human-AI co-written comments as significantly more constructive than those written independently by humans. Our analysis also revealed that LLM-generated comments integrated significantly more linguistic features of constructiveness compared to human-written comments. When participants used LLMs to refine their comments, the resulting comments were more constructive, more positive, less toxic, and retained the original intent. However, LLMs often distorted people's original views--especially when their stances were on a spectrum instead of being outright polarizing. Based on these findings, we discuss ethical and design considerations in using LLMs to facilitate constructive discourse online.

WhatsApp groups have become a hotbed for the propagation of harmful content including misinformation, hate speech, polarizing content, and rumors, especially in Global South countries. Given the platform's end-to-end encryption, moderation responsibilities lie on group admins and members, who rarely contest such content. Another approach is fact-checking, which is unscalable, and can only contest factual content (e.g., misinformation) but not subjective content (e.g., hate speech). Drawing on recent literature, we explore deliberation---open and inclusive discussion---as an alternative. We investigate the role of a conversational agent in facilitating deliberation on harmful content in WhatsApp groups. We conducted semi-structured interviews with 21 Indian WhatsApp users, employing a design probe to showcase an example agent. Participants expressed the need for anonymity and recommended AI assistance to reduce the effort required in deliberation. They appreciated the agent's neutrality but pointed out the futility of deliberation in echo chamber groups. Our findings highlight design tensions for such an agent, including privacy versus group dynamics and freedom of speech in private spaces. We discuss the efficacy of deliberation using deliberative theory as a lens, compare deliberation with moderation and fact-checking, and provide design recommendations for future such systems. Ultimately, this work advances CSCW by offering insights into designing deliberative systems for combating harmful content in private group chats on social media.

Social media platforms use content moderation to reduce and remove problematic content. However, much of the discourse on the benefits and pitfalls of moderation has so far focused on users in the West. Little is known about how users in the Global South interact with the humans and algorithms behind opaque moderation systems. To fill this gap, we conducted interviews with 19 Bangladeshi social media users who received restrictions for violating community standards on Facebook. We found that the users perceived the underlying human-AI infrastructure to imbibe coloniality in the form of amplifying power relations, centering Western norms, and perpetuating historical injustices and erasure of minoritized expressions. Based on the findings, we establish that the current moderation systems often propagate historical power relations and patterns of oppression, and discuss ways to rethink moderation in a fundamentally decolonial way.

We present a qualitative study with 36 diverse social media users in India to critically examine how low-resource communities engage with fake videos, including cheapfakes and AI-generated deepfakes. We find that most users are unaware of digitally manipulated fake videos and perceive videos to be fake only when they present inaccurate information. Few users who know about doctored videos expect them to be of poor quality and know nothing about sophisticated deepfakes. Moreover, most users lack the skills and willingness to spot fake videos and some were oblivious to the risks and harms of fake videos. Even when users know a video to be fake, they prefer to take no action and sometimes willingly share fake videos that favor their worldview. Drawing on our findings, we discuss design recommendations for social media platforms to curb the spread of fake videos.

This paper presents a between-subjects design experiment with 478 people in India to investigate how rural and urban social media users perceive credible and fake posts, and how different types of sources impact their perceptions of information credibility and sharing behaviors. Our findings reveal that: (1) rural social media users were less adept in differentiating between credible and fake posts than their urban counterparts, and (2) source effects on trust and sharing intent manifested differently for urban and rural users. For example, fake posts from family members garnered greater trust among urban users but were trusted the least by rural users. In case of sharing Facebook posts, urban users were more willing to share fake posts from family, whereas, rural users were more inclined to share fake posts from journalists. Drawing on these findings, we propose design interventions to counteract fake news in low-resource environments of the Global South.

Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.