可视化 Twitter（X.com）对话的演化：一种应用于 ChatGPT 人工智能训练讨论的综合方法
Visualizing the Evolution of Twitter (X.com) Conversations: A Comprehensive Methodology Applied to AI Training Discussions on ChatGPT
AI总结：
本文提出一种结合 Python 与 R 的综合方法，用于提取、情感分析、关键词编码并可视化 X.com 对话，并以 ChatGPT 相关 AI 发展暂停讨论为例，揭示社交网络动态与影响力演化。
摘要：
随着社交媒体平台的兴起，尤其是 X.com（原 Twitter），人们对理解数字社交网络和人类数字互动的兴趣日益增长。本文提出了一种从 X.com 中提取、处理和可视化分析数据的综合方法，该方法结合使用 Python 和 R 软件包，并通过我们公开可访问、可定制的代码加以增强。我们的方法构建了一个动态数据集，用于捕获各种互动：回复、转发和提及。为了探索更深层次的见解，数据经过情感分析和关键词编码处理，以揭示话语随时间的转变。我们的方法分为三个主要阶段。首先，使用 R 来拉取数据并构建社交网络数据集。随后，结合使用 Python 和 R 进行情感分析和关键词编码，旨在揭示讨论主题中潜在的情绪变化和语言转换。最后一个阶段使用 R 来可视化这些社交网络中的动态变化。这些可视化工具突出显示了用户互动和影响力模式的变化。作为实际演示，我们分析了 X.com 上关于暂停人工智能发展这一有争议提议的对话，特别聚焦于有关 ChatGPT 的讨论。通过关键词搜索，识别出了辩论中的主要声音。我们对情感和关键词的分析揭示了情绪和语言的模式，而可视化工具则展示了网络连接及其影响力的发展。本研究强调了可视化工具在理解数字时代在线社会动态中的重要作用。
英文摘要：
With the rise of social media platforms, especially X.com (formerly Twitter), there is a growing interest in understanding digital social networks and human digital interactions. This paper presents a comprehensive methodology for extracting, processing, and visually analyzing data from X.com, using a combination of Python and R packages, enhanced by our publicly accessible, customizable code. Our approach compiles a dynamic dataset that captures various interactions: replies, retweets, and mentions. To explore deeper insights, the data is subjected to sentiment analysis and keyword coding, indicating shifts in discourse over time. Our method is structured in three primary phases. Initially, R is employed for pulling data and the formation of social network datasets. Following this, the combination of Python and R is utilized for sentiment analysis and keyword coding, aiming to uncover the underlying emotional shifts and language transitions within topics of discussion. The final phase employs R to visualize the dynamic shifts within these social networks. These visualization tools highlight changes in user interactions and patterns of influence. For a practical demonstration, we analyzed conversations on X.com regarding the controversial proposal to halt AI development, focusing specifically on discussions about ChatGPT. By using keyword searches, leading voices in the debate were identified. Our analysis of sentiment and keywords revealed patterns in emotions and language, while visual tools illustrated the development of network connections and their influence. This study emphasizes the vital role of visual tools in understanding online social dynamics in the digital age.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
