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   https://doi.org/10.1145/3637346  

   Doyle, Dylan Thomas; Brahm, Charlie_Blue R; Brubaker, Jed R (April 2024, Proceedings of the ACM on Human-Computer Interaction)

   When bereaved individuals seek online support in response to the suicide of a loved one, their expressions of grief take many forms. Although the intense grief expressions individuals bereaved by suicide commonly share in private therapeutic settings can be helpful in healing from traumatic loss, these same expressions may potentially cause harm to others when shared in a public online support community. In this study, we present a qualitative analysis of letters posted on the r/SuicideBereavement subreddit, and comments replying to those posts, to explore what diverse expressions of grief additionally demand of platform design. We find that letter posts contain potentially harmful grief expressions that, in this community, generate mutual support among community members. Informed by our findings, this study considers the design challenges for online platforms as they simultaneously support users receiving support and healing through sharing certain grief expressions, while also supporting users who will be harmed by exposure to those grief expressions. Taking inspiration from offline therapy modalities, we consider the design implications of creating specialized online grief support spaces for diverse grief expressions. 

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2. Let's Talk about Sext: How Adolescents Seek Support and Advice about Their Online Sexual Experiences

   https://doi.org/10.1145/3313831.3376400  

   Razi, Afsaneh; Badillo-Urquiola, Karla; Wisniewski, Pamela J. (April 2020, Let’s Talk about Sext: How Teens Seek Support and Advice for Online Sexual Interactions)

   We conducted a thematic content analysis of 4,180 posts by adolescents (ages 12-17) on an online peer support mental health forum to understand what and how adolescents talk about their online sexual interactions. Youth used the platform to seek support (83%), connect with others (15%), and give advice (5%) about sexting, their sexual orientation, sexual abuse, and explicit content. Females often received unwanted nudes from strangers and struggled with how to turn down sexting requests from people they knew. Meanwhile, others who sought support complained that they received unwanted sexual solicitations while doing so—to the point that adolescents gave advice to one another on which users to stay away from. Our research provides insight into the online sexual experiences of adolescents and how they seek support around these issues. We discuss how to design peer-based social media platforms to support the well-being and safety of youth. 

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3. Spotlight Tweets: A Lens for Exploring Attention Dynamics within Online Sensemaking During Crisis Events

   https://doi.org/10.1145/3577213  

   Zhou, Kaitlyn; Wilson, Tom; Starbird, Kate; Spiro, Emma S. (June 2023, ACM Transactions on Social Computing)

   In this article, we introduce the concept of a spotlight social media post —a post that receives an unexpected burst of attention—and explore how such posts reveal salient aspects of online collective sensemaking and attention dynamics during a crisis event. Specifically, we examine the online conversation surrounding a false missile alert in Hawaii in January 2018. Through a mixed-methods analysis and visualizations, our research uncovers mechanisms that lead to rapid attention gains, such as spotlighting —when a user with existing influence confers attention by sharing others’ content with their audience. We highlight how spotlight social media posts (specifically spotlight tweets ) are distinct from other heavily shared content and that they offer insight into previously overlooked patterns in information exchange. We additionally reveal that attention dynamics may alter the social position of spotlight post authors immediately afterward (and possibly in the long term). We argue that spotlight social media posts offer a productive window for understanding online collective sensemaking, and we discuss how this can inform social media platform design and serve as a basis of future research. 

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4. Toward Context-Aware Privacy Enhancing Technologies for Online Self-Disclosure

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   Du, Tingting; Kim, Jiyoon; Squicciarini, Anna; Rajtmajer, Sarah (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space. 

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5. Benchmarking Multimodal Regex Synthesis with Complex Structures

   https://doi.org/10.18653/v1/2020.acl-main.541  

   Ye, Xi; Chen, Qiaochu; Dillig, Isil; Durrett, Greg (January 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes in these datasets are simple, and the language used to describe them is not diverse. We introduce StructuredRegex, a new regex synthesis dataset differing from prior ones in three aspects. First, to obtain structurally complex and realistic regexes, we generate the regexes using a probabilistic grammar with pre-defined macros observed from real-world StackOverflow posts. Second, to obtain linguistically diverse natural language descriptions, we show crowdworkers abstract depictions of the underlying regex and ask them to describe the pattern they see, rather than having them paraphrase synthetic language. Third, we augment each regex example with a collection of strings that are and are not matched by the ground truth regex, similar to how real users give examples. Our quantitative and qualitative analysis demonstrates the advantages of StructuredRegex over prior datasets. Further experimental results using various multimodal synthesis techniques highlight the challenge presented by our dataset, including non-local constraints and multi-modal inputs. 

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