Argument Mining of Attack and Support Patterns in Dialogical Conversations with Sequential Pattern Mining

Argument mining usually operates on short, decontextualized argumentative units such as main and subordinate clauses, or full sentences as proxies for arguments. Argumentation in digital media environments, however, is embedded in larger contexts. Especially on social media platforms, argumentation unfolds in dialog threads or tree structures where users interact with each other. To reveal patterns of such interactions, we transform 2.5 million tweets from 38k German Twitter conversations concerning nuclear energy from 2017, 2019, and 2021 into an abstract representation encoding their stance, and aspects. We then apply Sequential Pattern Mining, a common method for finding patterns in large databases, and explore its capabilities to investigate typical argumentation schemes in user debates. The approach reveals distinct patterns of support and attack relations between pro and contra arguments about nuclear energy in conversational threads when comparing different time slices of our corpus. For example, we are seeing an increasing relevance of the climate aspect in attacks on anti-nuclear arguments. However, the pro arguments are increasingly being countered by cost aspects. Analyzing this diachronic change of patterns allows us to describe the discursive processes of argumentation on a macro level that drive the slow but steady transformation of a society’s social and political convictions.