publications | Katherine Van Koevering

2024

arXiv

How Random is Random? Evaluating the Randomness and Humanness of LLMs’ Coin Flips

Katherine Van Koevering and Jon Kleinberg

arXiv preprint arXiv:2406.00092, 2024

Abs arXiv HTML

One uniquely human trait is our inability to be random. We see and produce patterns where there should not be any and we do so in a predictable way. LLMs are supplied with human data and prone to human biases. In this work, we explore how LLMs approach randomness and where and how they fail through the lens of the well-studied phenomenon of generating binary random sequences. We find that GPT-4 and Llama 3 exhibit and exacerbate nearly every human bias we test in this context, but GPT-3.5 exhibits more random behavior. This dichotomy of randomness or humanness is proposed as a fundamental question of LLMs and that either behavior may be useful in different circumstances.
arXiv

What’s in a Niche? Migration Patterns in Online Communities

Katherine Van Koevering, Meryl Ye, and Jon Kleinberg

arXiv preprint arXiv:2407.11794, 2024

Abs arXiv HTML

Broad topics in online platforms represent a type of meso-scale between individual user-defined communities and the whole platform; they typically consist of related communities that address different facets of a shared topic. Users often engage with the topic by moving among the communities within a single category. We find that there are strong regularities in the aggregate pattern of user migration, in that the communities comprising a topic can be ordered in a partial order such that there is more migration in the direction defined by the partial order than against it. Ordered along this overall direction, we find that communities in aggregate become smaller, less toxic, and more linguistically distinctive, suggesting a picture consistent with specialization. We study directions defined not just in the movement of users but also by the movement of URLs and by the direction of mentions from one community to another; each of these produces a consistent direction, but the directions all differ from each other. We show how, collectively, these distinct trends help organize the structure of large online topics and compare our findings across both Reddit and Wikipedia and in simulations.
arXiv

An abundance of Katherines: The game theory of baby naming

Katy Blumer, Kate Donahue, Katie Fritz, and 5 more authors

arXiv preprint arXiv:2404.00732, 2024

Abs arXiv HTML

In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world. We then extend our investigation with numerical experiments, as well as analysis of large language model tools. We conclude by discussing avenues for future research.

2022

arXiv

Exporting Geography Into A Virtual Landscape: A Global Pandemic Locally Discussed

Katherine Van Koevering, Yiquan Hong, and Jon Kleinberg

arXiv preprint arXiv:2210.07187, 2022

Abs arXiv HTML

The COVID-19 pandemic has been a global health crisis playing out in the age of social media. Even though the virtual environment makes interaction possible regardless of physical location, many of the most pressing issues during the pandemic – case counts, lockdown policies, vaccine availability – have played out in an intensely local fashion. Reflecting this locality, many of the online COVID communities that formed have been closely tied to physical location, at different spatial scales ranging from cities to countries to entire global platforms. This provides an opportunity to study how the real-world geography of the pandemic translates into a virtual landscape. By analyzing almost 300 geographically-linked COVID discussion communities on Reddit, we show how these discussions were organized geographically and temporally in three aspects: what were people talking about, who were they talking about it with, and how did they self-organize these conversations?

2021

WWW

Random graphs with prescribed k-core sequences: A new null model for network analysis

Katherine Van Koevering, Austin Benson, and Jon Kleinberg

In Proceedings of the Web Conference 2021, 2021

Abs arXiv HTML

In the analysis of large-scale network data, a fundamental operation is the comparison of observed phenomena to the predictions provided by null models: when we find an interesting structure in a family of real networks, it is important to ask whether this structure is also likely to arise in random networks with similar characteristics to the real ones. A long-standing challenge in network analysis has been the relative scarcity of reasonable null models for networks; arguably the most common such model has been the configuration model, which starts with a graph G and produces a random graph with the same node degrees as G. This leads to a very weak form of null model, since fixing the node degrees does not preserve many of the crucial properties of the network, including the structure of its subgraphs. Guided by this challenge, we propose a new family of network null models that operate on the k-core decomposition. For a graph G, the k-core is its maximal subgraph of minimum degree k; and the core number of a node v in G is the largest k such that v belongs to the k-core of G. We provide the first efficient sampling algorithm to solve the following basic combinatorial problem: given a graph G, produce a random graph sampled nearly uniformly from among all graphs with the same sequence of core numbers as G. This opens the opportunity to compare observed networks G with random graphs that exhibit the same core numbers, a comparison that preserves aspects of the structure of G that are not captured by more local measures like the degree sequence. We illustrate the power of this core-based null model on some fundamental tasks in network analysis, including the enumeration of network motifs.

2020

WWW

Frozen binomials on the web: Word ordering and language conventions in online text

Katherine Van Koevering, Austin R Benson, and Jon Kleinberg

In Proceedings of The Web Conference 2020, 2020

Abs arXiv HTML

There is inherent information captured in the order in which we write words in a list. The orderings of binomials — lists of two words separated by ’and’ or ’or’ — have been studied for over a century. We expand this analysis to large scale using online text, examining both frozen binomials (those with a fixed conventional ordering) and non-frozen ones. We demonstrate that most previously proposed rules for predicting binomial ordering are largely ineffective at scale, and by tracking orderings across time and online communities, we identify new dimensions central to these predictions. We also explore the global structure of binomials across communities, establish a new model for these expressions, and analyze trinomials, finding that binomial principles do not extend directly to three-word lists.

2018

ICWSM

Ecosystem or echo-system? Exploring content sharing across alternative media domains

Kate Starbird, Ahmer Arif, Tom Wilson, and 3 more authors

In Proceedings of the International AAAI Conference on Web and Social Media, 2018

Abs HTML

In this paper, we explore the information ecosystem of alternative media — outlets that operate outside of mainstream journalism norms. We examine the structure of content sharing across a network of alternative media domains and the role of social media in amplifying their content. Through analysis of thousands of articles and their social media traces, we find that these outlets form a coordinated, interconnected ecosystem that leverages social media for distribution in distinctive ways. We discuss implications for understanding the spread of misinformation and for designing interventions.