Abstract
Social media are of paramount importance to public discourse. RANT aims to contribute methods and formalisms for extracting, representing, and processing arguments from noisy text found in social media discussions, using a large corpus of pre-referendum Brexit tweets as a running case study. We identify recurring linguistic argumentation patterns in a corpus-linguistic analysis and formulate corresponding corpus queries to extract arguments automatically. Given the huge amount of social media data available, our approach aims at high precision at the possible price of low recall. Argumentation patterns are directly associated with logical patterns in a dedicated formalism and accordingly, individual arguments are directly parsed as logical formulae. The logical formalism for argument representation features a broad range of modalities capturing real-life modes of expression. We cast this formalism as a family of instance logics in the generic framework of coalgebraic logic and complement it by a flexible framework to represent relationships between arguments; including standard relations like attack and support but also relations extracted from metadata. Some relations are inferred from the logical content of individual arguments. We are in the process of developing suitable generalizations of various extension semantics for argumentation frameworks combined with corresponding algorithmic methods to allow for the automated retrieval of large-scale argumentative positions.
Highlights
Introduction to the RANT projectNatalie Dykes1 · Stefan Evert1 · Merlin Göttlinger2 · Philipp Heinrich1 · Lutz Schröder2 AbstractSocial media are of paramount importance to public discourse
Public discourse is becoming increasingly dominated by social media, creating a need for robust analysis of arguments raised in computer-mediated communication
The objective of RANT is to explore the possibility of conducting such an analysis in a formally grounded manner despite the high level of both syntactic and semantic variability inherent in the exchanged arguments, based on a large corpus of Twitter messages on the Brexit referendum
Summary
Public discourse is becoming increasingly dominated by social media, creating a need for robust analysis of arguments raised in computer-mediated communication. Arguments in day to day conversations tend to feature a high degree of implicitness because users normally assume common knowledge on the side of the listeners It is possible for whole premises, conclusions, and any intermediate steps of arguments to remain implicit [5]. This effect is amplified by the fast pace of social media communication and especially Twitter’s limit of 140 characters1 Such incomplete arguments are referred to as enthymemes in the literature [28] and making the missing information explicit is difficult even for human annotators [4]. We plan to utilise a logic reasoner as part of our argument map construction pipeline for automatically drawing some inferences from illocutions alone For this aim we incorporate ideas from structured argumentation – such as labelling nodes with logic formulae. Only very few formalisms have a convenient representation of enthymemes
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