  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.
