The COVID-19 pandemic that broke out globally in December 2019 put us all in an unprecedented situation. Social media became a vital source of support and information during the pandemic, as physical interactions were limited by people staying at home. This paper investigates support dynamics and user commitment in an online COVID-19 community of Reddit. We define various support classes and observe them along with user behavior and temporal phases for a coherent in the community. We perform survival analysis using Cox Regression to identify factors influencing a user's commitment to the community. People seeking more emotional and informational support while they are COVID-positive stay longer in the community. Surprisingly, people who give more support in their early phases are less likely to stay. Additionally, contrary to common belief, our findings show that receiving emotional and informational support has little effect on users' longevity in the community. Our results lead to a better understanding of user dynamics related to community support and can directly impact moderators and platform owners in designing community guidelines and incentive structures.

Social media has grown exponentially in a short period, coming to the forefront of communications and online interactions. Despite their rapid growth, social media platforms have been unable to scale to different languages globally and remain inaccessible to many. In this paper, we characterize Koo, a multilingual micro-blogging site that rose in popularity in 2021, as an Indian alternative to Twitter. We collected a dataset of 4.07 million users, 163.12 million follower-following relationships, and their content and activity across 12 languages. We study the user demographic along the lines of language, location, gender, and profession. The prominent presence of Indian languages in the discourse on Koo indicates the platform's success in promoting regional languages. We observe Koo's follower-following network to be much denser than Twitter's, comprising of closely-knit linguistic communities. An N-gram analysis of posts on Koo shows a #KooVsTwitter rhetoric, revealing the debate comparing the two platforms. Our characterization highlights the dynamics of the multilingual social network and its diverse Indian user base.