We develop a simulation framework for studying misinformation spread within
online social networks that blends agent-based modeling and natural language
processing techniques. While many other agent-based simulations exist in this
space, their ability to provide actionable insights in in part limited by their
lack of fidelity and generalizability to existing networks. To partially
address these concerns, we create a 'digital clone' of a known misinformation
sharing network by downloading social media histories for over ten thousand of
its users. We parse these histories to both extract the structure of the
network and model the nuanced ways in which information is shared and spread
among its members. Unlike many other agent-based methods in this space,
information sharing between users in our framework is sensitive to topic of
discussion, user preferences, and online community dynamics. To evaluate the
fidelity of our method, we seed our cloned network with a set of posts recorded
in the base network and compare propagation dynamics between the two, observing
reasonable agreement across the twin networks over a variety of metrics.
Lastly, we explore how the cloned network may serve as a flexible, low-cost
testbed for misinformation countermeasure evaluation and red teaming analysis.
We hope the tools explored here augment existing efforts in the space and
unlock new opportunities for misinformation countermeasure evaluation, a field
that may become increasingly important to consider with the anticipated rise of
misinformation campaigns fueled by generative artificial intelligence.

我们开发了一个模拟框架，用于研究在线社交网络中的虚假信息传播，结合基于代理的建模和自然语言处理技术。

基于语言敏感的代理模型用于在线社交网络的数字克隆和虚假信息传播研究

Digital cloning of online social networks for language-sensitive  agent-based modeling of misinformation spread

Ranking algorithms play a crucial role in online platforms ranging from
search engines to recommender systems. In this paper, we identify a surprising
consequence of popularity-based rankings: the fewer the items reporting a given
signal, the higher the share of the overall traffic they collectively attract.
This few-get-richer effect emerges in settings where there are few distinct
classes of items (e.g., left-leaning news sources versus right-leaning news
sources), and items are ranked based on their popularity. We demonstrate
analytically that the few-get-richer effect emerges when people tend to click
on top-ranked items and have heterogeneous preferences for the classes of
items. Using simulations, we analyze how the strength of the effect changes
with assumptions about the setting and human behavior. We also test our
predictions experimentally in an online experiment with human participants. Our
findings have important implications to understand the spread of
misinformation.

本文研究了基于流行度的排序算法所导致的少数热门效应以及其在信息传播方面的重要影响。通过理论和实验分析，发现人们点击排名靠前的物品，并具有不同的物品偏好，导致物品流量不平衡，进而影响了信息的传播。