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1. Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

   https://doi.org/10.1145/3617654  

   Lee, Jooyoung; Rajtmajer, Sarah; Srivatsavaya, Eesha; Wilson, Shomir (December 2023, ACM Transactions on Social Computing)

   We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: (1) users were more likely to share their age, education, and location information when seeking both informational and emotional support as opposed to pursuing either one; (2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; (3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; and (4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement. 

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2. Automatic text generation using deep learning: providing large-scale support for online learning communities

   https://doi.org/10.1080/10494820.2021.1993932  

   Du, Hanxiang; Xing, Wanli; Pei, Bo (October 2021, Interactive learning environments)

   Participating in online communities has significant benefits to students learning in terms of students’ motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support. 

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3. Conspiracies Online: User Discussions in a Conspiracy Community Following Dramatic Events

   Samory, Mattia; Mitra, Tanushree (June 2018, Twelfth International AAAI Conference on Web and Social Media)

   Online communities play a crucial role in disseminating conspiracy theories. New theories often emerge in the aftermath of catastrophic events. Despite evidence of their widespread appeal, surprisingly little is known about who participates in these event-specific conspiratorial discussions or how do these discussions evolve over time. We study r/conspiracy, an active Reddit community of more than 200,000 users dedicated to conspiratorial discussions. By focusing on four tragic events and 10 years of discussions, we find three distinct user cohorts: joiners, who never participated in Reddit but joined r/conspiracy only after the event; converts who were active Reddit users but joined r/conspiracy only after the event; and veterans, who are longstanding r/conspiracy members. While joiners and converts have a shorter lifespan in the community in comparison to the veterans, joiners are more active during their shorter tenure, becoming increasingly engaged over time. Finally, to investigate how these events affect users’ conspiratorial discussions, we adopted a causal inference approach to analyze user comments around the time of the events. We find that discussions happening after the event exhibit signs of emotional shock, increased language complexity, and simultaneous expressions of certainty and doubtfulness. Our work provides insight on how online communities may detect new conspiracy theories that emerge ensuing dramatic events, and in the process stop them before they spread. 

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4. Together But Alone: Atomization and Peer Support among Gig Workers

   https://doi.org/10.1145/3479535  

   Yao, Zheng; Weden, Silas; Emerlyn, Lea; Zhu, Haiyi; Kraut, Robert_E (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   The individualistic nature of gig work allows workers to have high levels of flexibility, but it also leads to atomization, leaving them isolated from peer workers. In this paper, we employed a qualitative approach to understand how online social media groups provide informational and emotional support to physical gig workers during the COVID-19 pandemic. We found that social media groups alleviate the atomization effect, as workers use these groups to obtain experiential knowledge from their peers, build connections, and organize collective action. However, we noted a reluctance among workers to share strategic information where there was a perceived risk of being competitively disadvantaged. In addition, we found that the diversity among gig workers has also led to limited empathy for one another, which further impedes the provision of emotional support. While social media groups could potentially become places where workers organize collective efforts, several factors, including the uncertainty of other workers' activities and the understanding of the independent contractor status, have diminished the effectiveness of efforts at collective action. 

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5. Why do People Participate in Small Online Communities?

   https://doi.org/10.1145/3479606  

   Hwang, Sohyeon; Foote, Jeremy_D (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes. 

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