Brian Keegan

Analysts, athletes and coaches have long relied upon decision support tools, but new AI capabilities raise questions of whether they can “appropriately rely” on these models. In this work, we explore the starting lineup decisions of fantasy American football managers in the presence of projected points. Across more than 2.5 million lineup-weeks from a single fantasy platform during the 2025 NFL season, we find that fantasy managers’ starting lineups appropriately rely on AI advice in only 15.8% to 26.8% of cases. Despite managers’ observed near complete reliance, limited deviation from AI advice is often harmful to their decision-making. Bridging literature from critical data studies and AI reliance, we discuss possible explanations for this behavior, and offer design directions towards complementarity in this setting. Finally, we highlight tensions revealed by the case of fantasy football, and invite researchers to consider SportsHCI as a site to bridge social desirability and technical possibility in and beyond sports.

Despite frequent efforts to combat racism, almost no research has explored how to cultivate positive experiences of thriving Black culture on Reddit. In this case study, we surveyed users of r/BlackPeopleTwitter (BPT)--a large, popular subreddit that showcases screenshots of hilarious or insightful social media posts made by Black people (mainly from Black Twitter). Our research questions seek to understand users' motivations for visiting BPT, how they experience a sense of virtual community (SOVC) and membership in BPT, and how BPT's governance influences these experiences. We find that that users come to BPT primarily for excellent humor and entertainment, sociopolitical context on issues relevant to Black people, and/or partaking in the shared Black experience. Black users are more likely to report higher SOVC and to identify as members, whereas non-Black users are more likely to identify as guests or visitors to the community. To protect Black expression, the BPT moderation team implemented a governance strategy for verifying racial identity and limiting participation to only verified users in certain threads. Our data suggest that this policy is a contentious but influential aspect of SOVC that simultaneously constructs and challenges the sense of the subreddit existing as a safe space for Black people. We synthesize these results by discussing how: differing platform affordances across Twitter and Reddit combine to cultivate a thriving Black community on Reddit; the need for Black authenticity on an otherwise anonymous platform can guide future research in identity verification; and the limitations of this study motivate future work to support all marginalized communities online.

Humans need a sense of community (SOC), and social media platforms afford opportunities to address this need by providing users with a sense of virtual community (SOVC). This paper explores SOVC on Reddit and is motivated by two goals: (1) providing researchers with an excellent resource for methodological decisions in studies of Reddit communities; and (2) creating the foundation for a new class of research methods and community support tools that reflect users' experiences of SOVC. To ensure that methods are respectfully and ethically designed in service and accountability to impacted communities, our work takes a qualitative and community-centered approach by engaging with two key stakeholder groups. First, we interviewed 21 researchers to understand how they study community" on Reddit. Second, we surveyed 12 subreddits to gain insight into user experiences of SOVC. Results show that some research methods can broadly reflect user experiences of SOVC regardless of the topic or type of subreddit. However, user responses also evidenced the existence of five distinct Community Archetypes: Topical Q&A, Learning & Perspective Broadening, Social Support, Content Generation, and Affiliation with an Entity. We offer the Community Archetypes framework to support future work in designing methods that align more closely with user experiences of SOVC and to create community support tools that can meaningfully nourish the human need for SOC/SOVC in our modern world.

The Arab Spring was a historic set of protests beginning in 2011 that toppled governments and led to major conflicts. Collective memories of events like these can vary significantly across social contexts in response to political, cultural, and linguistic factors. While Wikipedia plays an important role in documenting both historic and current events, little attention has been given to how Wikipedia articles, created in the aftermath of major events, continue to evolve over years or decades. Using the archived content of Arab Spring-related topics across the Arabic and English Wikipedias between 2011 and 2024, we define and evaluate multilingual measures of event salience, deliberation, contextualization, and consolidation of collective memory surrounding the Arab Spring. Our findings about the temporal evolution of the Wikipedia articles' content similarity across languages has implications for theorizing about online collective memory processes and evaluating linguistic models trained on these data.

Federated social networking is an increasingly popular alternative to more traditional, centralized forms. Yet, this federated arrangement can lead to dramatically different experiences across the network. Using a sample of the most popular instances on the federated social network Mastodon, we characterize the types of rules present in this emerging space. We then compare these rules to those on Reddit, as an example of a different, less centralized space. Rules on Mastodon often pay particular attention to issues of harassment and hate — strongly reflecting the spirit of the Mastodon Covenant. We speculate that these rules may have emerged in response to problems of other platforms, and reflect a lack of support for instance maintainers. With this work, we call for the development of additional instance-level governance and technical scaffolding, and raise questions for future work into the development, values, and value tensions present in the broader federated social networking landscape.

The legal status of Cannabis is changing, fueling an increased diversity of Cannabis -derived products. Because Cannabis contains dozens of chemical compounds with potential psychoactive or medicinal effects, understanding its phytochemical diversity is crucial. The legal Cannabis industry heavily markets products to consumers based on widely used labelling systems purported to predict the effects of different Cannabis “strains.” We analyzed the cannabinoid and terpene content of tens of thousands of commercial Cannabis samples across six US states, finding distinct chemical phenotypes (chemotypes) which are reliably present. After careful descriptive analysis of the phytochemical diversity and comparison to the commercial labels commonly attached to Cannabis samples, we show that commercial labels do not consistently align with the observed chemical diversity. However, certain labels are statistically overrepresented for specific chemotypes. These results have important implications for the classification of commercial Cannabis , the design of animal and human research, and the regulation of legal Cannabis marketing.

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two "new" communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, evidenced by both language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly "loyal". To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.

The widespread legalization of Cannabis has opened the industry to using contemporary analytical techniques for chemotype analysis. Chemotypic data has been collected on a large variety of oil profiles inherent to the cultivars that are commercially available. The unknown gene regulation and pharmacokinetics of dozens of cannabinoids offer opportunities of high interest in pharmacology research. Retailers in many medical and recreational jurisdictions are typically required to report chemical concentrations of at least some cannabinoids. Commercial cannabis laboratories have collected large chemotype datasets of diverse Cannabis cultivars. In this work a data set of 17,600 cultivars tested by Steep Hill Inc., is examined using machine learning techniques to interpolate missing chemotype observations and cluster cultivars into groups based on chemotype similarity. The results indicate cultivars cluster based on their chemotypes, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. Due to the missing data and to the low signal to noise ratio for some less common cannabinoids, their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

The accelerating legalization of Cannabis has opened the industry to using contemporary analytical techniques. The gene regulation and pharmacokinetics of dozens of cannabinoids remain poorly understood. Because retailers in many medical and recreational jurisdictions are required to report chemical concentrations of cannabinoids, commercial laboratories have growing chemotype datasets of diverse Cannabis cultivars. Using a data set of 17,600 cultivars tested by Steep Hill Inc., we apply machine learning techniques to interpolate missing chemotype observations and cluster cultivars together based on similarity. Our results show that cultivars cluster based on their chemotype, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. However, due to the missing data for some of the cannabinoids their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.