Social media users have repeatedly advocated for control over the currently opaque operations of feed algorithms. Large language models (LLMs) now offer the promise of custom-defined feeds—but users often fail to foresee the gaps and edge cases in how they define their custom feed. We introduce feed elicitation interviews, an interactive method that guides users through identifying these gaps and articulating their preferences to better author custom social media feeds. We deploy this approach in a online study to create custom Bluesky feeds and find that participants significantly prefer the feeds produced from their elicited preferences to those produced by users manually describing their feeds. Through feed elicitation interviews, we advance users’ ability to control their social media experience, empowering them to describe and implement their desired feeds.

Users trust algorithms more when they can predict the algorithms’ behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have often been assumed too complex for people to build predictive mental models, especially in the social media domain. In this paper, we describe conditions under which even complex algorithms can yield predictive mental models, opening up opportunities for a broader set of human-centered algorithms. We theorize that users will form an accurate predictive mental model of an algorithm’s behavior if and only if the algorithm simultaneously satisfies three criteria: (1) cognitive availability of the underlying concepts being modeled, (2) concept compactness (does it form a single cognitive construct?), and (3) high alignment between the person’s and algorithm’s execution of the concept. We evaluate this theory through a pre-registered experiment ( N = 1250) where users predict behavior of 25 social media feed ranking algorithms that vary on these criteria. We find that even complex (e.g., LLM-based) algorithms enjoy accurate prediction rates when they meet all criteria, and even simple (e.g., basic term count) algorithms fail to be predictable when a single criterion fails. We also find that these criteria determine outcomes beyond prediction accuracy, such as which mental models users deploy to make their predictions.

Positive social interactions can occur in groups of many shapes and sizes, spanning from small and private to large and open. However, social media tends to binarize our experiences into either isolated small groups or into large public squares. In this paper, we introduce Burst, a social media design that allows users to share and curate content between many spaces of varied size and composition. Users initially post content to small trusted groups, who can then ''burst'' that content, routing it to the groups that would be the best audience. We instantiate this approach into a mobile phone application, and demonstrate through a ten-day field study (N=36) that Burst enabled a participatory curation culture. With this work, we aim to articulate potential new design directions for social media sharing.

In spite of efforts to increase participation, many online groups struggle to survive past the initial days, as members leave and activity atrophies. We argue that a main assumption of online group design---that groups ask nothing of their members beyond lurking---may be preventing many of these groups from sustaining a critical mass of participation. In this paper, we explore an alternative commitment design for online groups, which requires that all members commit at regular intervals to participating, as a condition of remaining in the group. We instantiate this approach in a mobile group chat platform called Commit, and perform a field study comparing commitment against a control condition of social psychological nudges with N=57 participants over three weeks. Commitment doubled the number of contributions versus the control condition, and resulted in 87% (vs. 19%) of participants remaining active by the third week. Participants reported that commitment provided safe cover for them to post even when they were nervous. Through this work, we argue that more effortful, not less effortful, membership may support many online groups.

How can online communities execute a focused vision for their space? Curation offers one approach, where community leaders manually select content to share with the community. Curation enables leaders to shape a space that matches their taste, norms, and values, but the practice is often intractable at social media scale: curators cannot realistically sift through hundreds or thousands of submissions daily. In this paper, we contribute algorithmic and interface foundations enabling curation at scale, and manifest these foundations in a system called Cura. Our approach draws on the observation that, while curators' attention is limited, other community members' upvotes are plentiful and informative of curators' likely opinions. We thus contribute a transformer-based curation model that predicts whether each curator will upvote a post based on previous community upvotes. Cura applies this curation model to create a feed of content that it predicts the curator would want in the community. Evaluations demonstrate that the curation model accurately estimates opinions of diverse curators, that changing curators for a community results in clearly recognizable shifts in the community's content, and that, consequently, curation can reduce anti-social behavior by half without extra moderation effort. By sampling different types of curators, Cura lowers the threshold to genres of curated social media ranging from editorial groups to stakeholder roundtables to democracies.

Social computing prototypes probe the social behaviors that may arise in an envisioned system design. This prototyping practice is currently limited to recruiting small groups of people. Unfortunately, many challenges do not arise until a system is populated at a larger scale. Can a designer understand how a social system might behave when populated, and make adjustments to the design before the system falls prey to such challenges? We introduce social simulacra, a prototyping technique that generates a breadth of realistic social interactions that may emerge when a social computing system is populated. Social simulacra take as input the designer’s description of a community’s design—goal, rules, and member personas—and produce as output an instance of that design with simulated behavior, including posts, replies, and anti-social behaviors. We demonstrate that social simulacra shift the behaviors that they generate appropriately in response to design changes, and that they enable exploration of “what if?” scenarios where community members or moderators intervene. To power social simulacra, we contribute techniques for prompting a large language model to generate thousands of distinct community members and their social interactions with each other; these techniques are enabled by the observation that large language models’ training data already includes a wide variety of positive and negative behavior on social media platforms. In evaluations, we show that participants are often unable to distinguish social simulacra from actual community behavior and that social computing designers successfully refine their social computing designs when using social simulacra.

Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual content, the visual design and layout patterns, or the app contexts. Many representation techniques also require significant manual data annotation efforts. This paper presents Screen2Vec, a new self-supervised technique for generating representations in embedding vectors of GUI screens and components that encode all of the above GUI features without requiring manual annotation using the context of user interaction traces. Screen2Vec is inspired by the word embedding method Word2Vec, but uses a new two-layer pipeline informed by the structure of GUIs and interaction traces and incorporates screen- and app-specific metadata. Through several sample downstream tasks, we demonstrate Screen2Vec’s key useful properties: representing between-screen similarity through nearest neighbors, composability, and capability to represent user tasks.

For a decade, our institution has offered both a biology-based CS1 (CS1-B) and a traditional, breadth-based CS1. This project follows the paths of students in both courses -- tracking their subsequent interests (what courses do the two groups choose afterwards') and their grades in those courses. Within the biology-based cohort, we also contrast the futures of the students who chose a biology-themed introduction with the group who expressed no preference or requested the breadth-based approach. Even when student preference was not accommodated, equitable downstream performance results hold. We discuss the implications of these results, including the possibility that, like introductory writing, introductory computing is a professional literacy in which many disciplines have a stake.

The controllability of a temporal network is defined as an agent's ability to navigate around the uncertainty in its schedule and is well-studied for certain networks of temporal constraints. However, many interesting real-world problems can be better represented as Probabilistic Simple Temporal Networks (PSTNs) in which the uncertain durations are represented using potentially-unbounded probability density functions. This can make it inherently impossible to control for all eventualities. In this paper, we propose two new dynamic controllability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. The first approach, which we call Min-Loss DC, finds a dynamic scheduling strategy that minimizes loss of control by using a conflict-directed search to decide where to sacrifice the control in a way that optimizes overall success. The second approach, which we call Max-Gain DC, works in the other direction: it finds a dynamically controllable schedule and then attempts to progressively strengthen it by capturing additional uncertainty. Our approaches are the first known that work by finding maximally dynamically controllable schedules. We empirically compare our approaches against two existing PSTN offline dispatch approaches and one online approach and show that our Min-Loss DC algorithm outperforms the others in terms of maximizing execution success while maintaining competitive runtimes.

Controllability for Simple Temporal Networks with Uncertainty (STNUs) has thus far been limited to three levels: strong, dynamic, and weak. Because of this, there is currently no systematic way for an agent to assess just how far from being controllable an uncontrollable STNU is. We provide new insights inspired by a geometric interpretation of STNUs to introduce the degrees of strong and dynamic controllability — continuous metrics that measure how far a network is from being controllable. We utilize these metrics to approximate the probabilities that an STNU can be dispatched successfully offline and online respectively. We introduce new methods for predicting the degrees of strong and dynamic controllability for uncontrollable networks. We further generalize these metrics by defining likelihood of controllability, a controllability measure that applies to Probabilistic Simple Temporal Networks (PSTNs). Finally, we empirically demonstrate that these metrics are good predictors of actual dispatch success rate for STNUs and PSTNs.