arXiv subjects

Brian C. Keegan

Publications and source records attributed to Brian C. Keegan.

12 recordsLinked to original sources

arXiv2026

The collective dynamics of online harassment

Fringe online message boards are often studied in the context of the extreme ideology that they produce. So far, however, not much of this research has focused on direct real-world harm in the all-too-common form of collective harassment. We directly analyze the complex dynamics of KiwiFarms, an online message board dedicated largely to the harassment of individuals from vulnerable communities. We conduct exploratory analyses of the hyperlink structure of the platform and linguistic changes over time, and prospective modeling of thread size. After establishing this broader picture of the complex traits of the system, we observe the temporal evolution of community-specific vocabulary, finding that the community's framing of their harassment targets persistently evokes more danger in the early 2020s than the late 2010s. We lastly find that early thread-growth behavior is predictive of longer-term thread virality. We discuss the implications for broader understanding of toxic online behavior and threat assessment, and make the case for studying fringe platforms as complex systems with significant societal impact.

physics.soc-ph↗

arXiv2024

Cross-Language Evolution of Divergent Collective Memory Around the Arab Spring

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.

cs.CY↗

arXiv2023

Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Community

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, 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 users' 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.

cs.HC↗

arXiv2022

Characterizing disruptions in online gaming behavior following software patches

Multiplayer online games are ideal settings for studying the effects of technological disruptions on social behavior. Software patches to online games cause significant changes to the game's rules and require players to develop new strategies to cope with these disruptions. We surveyed players, analyzed the content of software patch notes, and analyzed changes to the character selection behaviors in more than 53 million matches of Dota 2 in the days before and after software patches over a 30-month period. We found that the severity of patches is correlated with the magnitude of behavioral changes following a patch. We discuss the opportunities of leveraging software patches to online games as a valuable but overlooked empirical instrument for measuring behavioral dynamics.

cs.HC↗

arXiv2021

Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic

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.

cs.SI↗

arXiv2020

A Quantitative Portrait of Wikipedia's High-Tempo Collaborations during the 2020 Coronavirus Pandemic

The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with a remarkable ability to create and revise content following current events. Using 973,940 revisions from 134,337 editors to 4,238 articles, this study examines the dynamics of the English Wikipedia's response to the coronavirus pandemic through the first five months of 2020 as a "quantitative portrait" describing the emergent collaborative behavior at three levels of analysis: article revision, editor contributions, and network dynamics. Across multiple data sources, quantitative methods, and levels of analysis, we find four consistent themes characterizing Wikipedia's unique large-scale, high-tempo, and temporary online collaborations: external events as drivers of activity, spillovers of activity, complex patterns of editor engagement, and the shadows of the future. In light of increasing concerns about online social platforms' abilities to govern the conduct and content of their users, we identify implications from Wikipedia's coronavirus collaborations for improving the resilience of socio-technical systems during a crisis.

cs.SI↗

arXiv2018

Mobilizing the Trump Train: Understanding Collective Action in a Political Trolling Community

Political trolls initiate online discord not only for the lulz (laughs) but also for ideological reasons, such as promoting their desired political candidates. Political troll groups recently gained spotlight because they were considered central in helping Donald Trump win the 2016 US presidential election, which involved difficult mass mobilizations. Political trolls face unique challenges as they must build their own communities while simultaneously disrupting others. However, little is known about how political trolls mobilize sufficient participation to suddenly become problems for others. We performed a quantitative longitudinal analysis of more than 16 million comments from one of the most popular and disruptive political trolling communities, the subreddit /r/The\_Donald (T\D). We use T_D as a lens to understand participation and collective action within these deviant spaces. In specific, we first study the characteristics of the most active participants to uncover what might drive their sustained participation. Next, we investigate how these active individuals mobilize their community to action. Through our analysis, we uncover that the most active employed distinct discursive strategies to mobilize participation, and deployed technical tools like bots to create a shared identity and sustain engagement. We conclude by providing data-backed design implications for designers of civic media.

cs.SI↗

arXiv2016

Black Lives Matter in Wikipedia: Collaboration and Collective Memory around Online Social Movements

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise pre-existing knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

cs.SI↗

arXiv2015

The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games

Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a \textit{proficiency-congruency dilemma} between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how users negotiate this dilemma. Using data from \textit{League of Legends}, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million users to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that player proficiency increases team performance more than team congruency. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly prioritize individuals' expertise and teams' compatibility.

cs.HC↗

arXiv2015

Actually, It's About Ethics in Computational Social Science: A Multi-party Risk-Benefit Framework for Online Community Research

Managers regularly face a complex ethical dilemma over how to best govern online communities by evaluating the effectiveness of different social or technical strategies. What ethical considerations should guide researchers and managers when they employ causal research methods that make different community members bear different risks and benefits, under different levels of consent? We introduce a structural framework for evaluating the flows of risks and benefits in social systems with multiple interacting parties. This framework has implications for understanding the governmentality of managing socio-technical systems, for making research ethics discussions more commensurable, and for enumerating alternative goals researchers might pursue with interventions.

cs.CY↗

arXiv2015

Analyzing Organizational Routines in Online Knowledge Collaborations: A Case for Sequence Analysis in CSCW

Research into socio-technical systems like Wikipedia has overlooked important structural patterns in the coordination of distributed work. This paper argues for a conceptual reorientation towards sequences as a fundamental unit of analysis for understanding work routines in online knowledge collaboration. We outline a research agenda for researchers in computer-supported cooperative work (CSCW) to understand the relationships, patterns, antecedents, and consequences of sequential behavior using methods already developed in fields like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique editors to 96 Wikipedia articles as a case study, we analyze the prevalence and significance of different sequences of editing patterns. We illustrate the mixed method potential of sequence approaches by interpreting the frequent patterns as general classes of behavioral motifs. We conclude by discussing the methodological opportunities for using sequence analysis for expanding existing approaches to analyzing and theorizing about co-production routines in online knowledge collaboration.

cs.SI↗

arXiv2015

Supporting Instructors in Collaborating with Researchers using MOOClets

Most education and workplace learning takes place in classroom contexts far removed from laboratories or field sites with special arrangements for scientific research. But digital online resources provide a novel opportunity for large scale efforts to bridge the real world and laboratory settings which support data collection and randomized A/B experiments comparing different versions of content or interactions [2]. However, there are substantial technological and practical barriers in aligning instructors and researchers to use learning technologies like blended lessons/exercises & MOOCs as both a service for students and a realistic context to conduct research. This paper explains how the concept of a MOOClet can facilitate research-practitioner collaborations. MOOClets [3] are defined as modular components of a digital resource that can be implemented in technology to: (1) allow modification to create multiple versions, (2) allow experimental comparison and personalization of different versions, (3) reliably specify what data are collected. We suggest a framework in which instructors specify what kinds of changes to lessons, exercises, and emails they would be willing to adopt, and what data they will collect and make available. Researchers can then: (1) specify or design experiments that compare the effects of different versions on quantifiable outcomes. (2) Explore algorithms for maximizing particular outcomes by choosing alternative versions of a MOOClet based on the input variables available. We present a prototype survey tool for instructors intended to facilitate practitioner researcher matches and successful collaborations.

cs.CY↗