Abstract
Abstract In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
Highlights
In recent years, the discipline of argument mining (AM), which concentrates on the intersection of computational linguistics and computational argumentation, has grown notably [28]
Interim conclusion We conclude that most studies focused on the core components of argumentation: claim and evidence (AB2016, PVV2013, SS2020)
Only little work has been done on relation detection (BCV2016b) and Argumentative Discourse Units (ADU) level argument component detection (SS2020)
Summary
The discipline of argument mining (AM), which concentrates on the intersection of computational linguistics and computational argumentation, has grown notably [28]. Work on tweet-based AM provides tools for extracting and analysing a crucial sub-group of argumentative texts. It is of interest for the broader AM community, as innovative approaches are tested during the development of Twitter-specific AM systems. The examples show that identifying argumentation in tweets is far from trivial and AM researchers need to decide how to deal with peculiarities typical for usergenerated data.
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