The large scale usage of social media, combined with its significant impact,
has made it increasingly important to understand it. In particular, identifying
user communities, can be helpful for many downstream tasks. However,
particularly when models are trained on past data and tested on future, doing
this is difficult.
In this paper, we hypothesize to take advantage of Large Language Models
(LLMs), to better identify user communities. Due to the fact that many LLMs,
such as ChatGPT, are fixed and must be treated as black-boxes, we propose an
approach to better prompt them, by training a smaller LLM to do this. We devise
strategies to train this smaller model, showing how it can improve the larger
LLMs ability to detect communities. Experimental results show improvements on
Reddit and Twitter data, on the tasks of community detection, bot detection,
and news media profiling.

利用大规模社交媒体的使用和其重大影响，本研究旨在通过训练较小的大语言模型来提高大型语言模型识别用户社区的能力，并在 Reddit 和 Twitter 数据上展示其改进了社区检测、机器人检测和新闻媒体描述的结果。

利用强化学习识别分裂观点提高社交媒体社群识别能力

Using RL to Identify Divisive Perspectives Improves LLMs Abilities to  Identify Communities on Social Media

How do we design an AI system that is intended to act as a communication
bridge between two user communities with different mental models and
vocabularies? Skillsync is an interactive environment that engages employers
(companies) and training providers (colleges) in a sustained dialogue to help
them achieve the goal of building a training proposal that successfully meets
the needs of the employers and employees. We used a variation of participatory
design to elicit requirements for developing AskJill, a question-answering
agent that explains how Skillsync works and thus acts as a communication bridge
between company and college users. Our study finds that participatory design
was useful in guiding the requirements gathering and eliciting user questions
for the development of AskJill. Our results also suggest that the two Skillsync
user communities perceived glossary assistance as a key feature that AskJill
needs to offer, and they would benefit from such a shared vocabulary.

我们设计了一个 AI 系统，它作为一个沟通桥梁连接两个具有不同心理模型和词汇的用户社区。我们采用参与式设计的方式，提取 AskJill 的需求，作为一个问答代理，解释 Skillsync 的工作原理，实现公司和大学用户之间的沟通桥梁。研究结果表明，参与式设计对指导需求收集和获取用户问题，开发 AskJill 非常有用。同时，研究结果还显示，Skillsync 的两个用户社区认为共享词汇是 AskJill 需要提供的关键特性，并能从中受益。

设计一个沟通桥梁：参与式设计用于问答 AI 代理

Designing a Communication Bridge between Communities: Participatory  Design for a Question-Answering AI Agent

The rise of social media platforms has facilitated the formation of echo
chambers, which are online spaces where users predominantly encounter
viewpoints that reinforce their existing beliefs while excluding dissenting
perspectives. This phenomenon significantly hinders information dissemination
across communities and fuels societal polarization. Therefore, it is crucial to
develop methods for quantifying echo chambers. In this paper, we present the
Echo Chamber Score (ECS), a novel metric that assesses the cohesion and
separation of user communities by measuring distances between users in the
embedding space. In contrast to existing approaches, ECS is able to function
without labels for user ideologies and makes no assumptions about the structure
of the interaction graph. To facilitate measuring distances between users, we
propose EchoGAE, a self-supervised graph autoencoder-based user embedding model
that leverages users' posts and the interaction graph to embed them in a manner
that reflects their ideological similarity. To assess the effectiveness of ECS,
we use a Twitter dataset consisting of four topics - two polarizing and two
non-polarizing. Our results showcase ECS's effectiveness as a tool for
quantifying echo chambers and shedding light on the dynamics of online
discourse.

社交媒体平台的崛起促进了回声室的形成，这是一种在线空间，用户主要遇到强化他们现有信念的观点，同时排斥异议。本文提出了回声室分数（ECS），这是一种评估用户社区内聚和分离程度的新指标，通过衡量用户在嵌入空间中的距离来实现。为了测量用户之间的距离，我们提出了 EchoGAE，一种基于自监督图自编码器的用户嵌入模型，利用用户的帖子和相互作用图将其嵌入到反映其意识形态相似性的方式中。通过使用包含两个极化和两个非极化主题的 Twitter 数据集，我们评估了 ECS 的有效性。我们的结果展示了 ECS 作为量化回声室和揭示在线讨论动态的工具的效果。