Understanding collective decision making at a large-scale, and elucidating
how community organization and community dynamics shape collective behavior are
at the heart of social science research. In this work we study the behavior of
thousands of communities with millions of active members. We define a novel
task: predicting which community will undertake an unexpected, large-scale,
distributed campaign.
To this end, we develop a hybrid model, combining textual cues, community
meta-data, and structural properties. We show how this multi-faceted model can
accurately predict large-scale collective decision-making in a distributed
environment. We demonstrate the applicability of our model through Reddit's
r/place - a large-scale online experiment in which millions of users,
self-organized in thousands of communities, clashed and collaborated in an
effort to realize their agenda.
Our hybrid model achieves a high F1 prediction score of 0.826. We find that
coarse meta-features are as important for prediction accuracy as fine-grained
textual cues, while explicit structural features play a smaller role.
Interpreting our model, we provide and support various social insights about
the unique characteristics of the communities that participated in the \r/place
experiment.
Our results and analysis shed light on the complex social dynamics that drive
collective behavior, and on the factors that propel user coordination. The
scale and the unique conditions of the \rp~experiment suggest that our findings
may apply in broader contexts, such as online activism, (countering) the spread
of hate speech and reducing political polarization. The broader applicability
of the model is demonstrated through an extensive analysis of the
WallStreetBets community, their role in r/place and four years later, in the
GameStop short squeeze campaign of 2021.

通过对 Reddit 等社区进行大规模的分析研究，结合文本分析、社区元数据和结构属性，我们提出了一种预测社区集体决策的混合模型，该模型能够准确预测分布式环境下的大规模集体行动，并对社区的特征、社会认同等等可能应用于在线活动、减少极端言辞等领域提出实证性结论。

这是必须的地方：预测大规模分布式活动中在线社区的参与度

This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign

We present a new model of collective decision making that captures important
crowd-funding and donor coordination scenarios. In the setting, there is a set
of projects (each with its own cost) and a set of agents (that have their
budgets as well as preferences over the projects). An outcome is a set of
projects that are funded along with the specific contributions made by the
agents. For the model, we identify meaningful axioms that capture concerns
including fairness, efficiency, and participation incentives. We then propose
desirable rules for the model and study, which sets of axioms can be satisfied
simultaneously. An experimental study indicates the relative performance of
different rules as well as the price of enforcing fairness axioms.

提出了一个新的集体决策模型，并考虑了众筹和捐助协调等重要应用。该模型考虑了公平性、效率性和参与激励等问题，并提出了相应的规则，并通过实验研究表明了规则的相对性能及执行公平性规则的代价。

参与式资金协调：模型、公理和规则

Participatory Funding Coordination: Model, Axioms and Rules

Traditionally, statistical and causal inference on human subjects rely on the
assumption that individuals are independently affected by treatments or
exposures. However, recently there has been increasing interest in settings,
such as social networks, where individuals may interact with one another such
that treatments may spill over from the treated individual to their social
contacts and outcomes may be contagious. Existing models proposed for causal
inference using observational data from networks of interacting individuals
have two major shortcomings. First, they often require a level of granularity
in the data that is practically infeasible to collect in most settings, and
second, the models are high-dimensional and often too big to fit to the
available data. In this paper we illustrate and justify a parsimonious
parameterization for network data with interference and contagion. Our
parameterization corresponds to a particular family of graphical models known
as chain graphs. We argue that, in some settings, chain graph models
approximate the marginal distribution of a snapshot of a longitudinal data
generating process on interacting units. We illustrate the use of chain graphs
for causal inference about collective decision making in social networks using
data from U.S. Supreme Court decisions between 1994 and 2004 and in
simulations.

本文介绍并验证了一种关于干预和传染网络数据的简洁参数化方法（所谓链图模型），通过使用美国最高法院在 1994 年至 2004 年之间的案例数据和模拟数据对社交网络中的集体决策进行因果推断。