This chapter aims to demonstrate how natural language processing and machine learning, combined with sequential analysis, can be used to study and model the social dynamics underlying social interactions. The application of these tools and methods is illustrated in a study that examined the effects of politeness on discourse and argumentation in online debates. Natural language processing (NLP) and machine learning techniques offer powerful tools for analyzing conversational dynamics in online collaborative argumentation. NLP involves the use of algorithms to process and analyze large amounts of natural language data, enabling the extraction of meaningful patterns and insights. This study leverages ConvoKit, a comprehensive toolkit for conversational analysis, to measure linguistic influence and power dynamics in online debates. Student postings from 20 online debates—comprising 2008 messages across five semesters of a graduate-level course on distance education—were coded and scored for politeness and impoliteness. The analysis aimed to understand how these linguistic strategies impact students’ engagement and the sustainability of argumentative exchanges. The findings reveal the effects of specific behaviors on student argumentation, guiding behavioral standards for group debates. By doing so, this chapter offers a framework for future research and the development of netiquette guidelines with clear linguistic markers.