REMOD: Relation Extraction for Modeling Online Discourse

TL;DR

This paper introduces REMOD, a supervised learning approach combining graph embeddings and dependency graph traversal to improve relation extraction in semi-structured online discourse data, aiding misinformation detection.

Contribution

The paper presents a novel relation extraction method that leverages semantic dependency graphs and path traversal, enhancing modeling of online discourse and misinformation analysis.

Findings

Effective relation extraction on semi-structured online data

Improved reasoning about misinformation claims

Integration into discourse modeling pipelines

Abstract

The enormous amount of discourse taking place online poses challenges to the functioning of a civil and informed public sphere. Efforts to standardize online discourse data, such as ClaimReview, are making available a wealth of new data about potentially inaccurate claims, reviewed by third-party fact-checkers. These data could help shed light on the nature of online discourse, the role of political elites in amplifying it, and its implications for the integrity of the online information ecosystem. Unfortunately, the semi-structured nature of much of this data presents significant challenges when it comes to modeling and reasoning about online discourse. A key challenge is relation extraction, which is the task of determining the semantic relationships between named entities in a claim. Here we develop a novel supervised learning method for relation extraction that combines graph embedding techniques with path traversal on semantic dependency graphs. Our approach is based on the intuitive observation that knowledge of the entities along the path between the subject and object of a triple (e.g. Washington,_D.C.}, and United_States_of_America) provides useful information that can be leveraged for extracting its semantic relation (i.e. capitalOf). As an example of a potential application of this technique for modeling online discourse, we show that our method can be integrated into a pipeline to reason about potential misinformation claims.