Work

Simple Open Stance Classification for Rumour Analysis

Year: 2017

Type: conference-paper

Abstract: Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.

Source: White Rose Research Online (University of Leeds, The University of Sheffield, University of York)

DOI: 10.48550/arxiv.1708.05286

Authors: Aker, Ahmet, Derczynski, Leon, Bontcheva, Kalina

Institutions: University of Duisburg-Essen, University of Sheffield

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Engineering and Physical Sciences Research Council

Awards: G1745319749, WELCOME - Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Co-morbidities, Collective Platform for Community Resilience and Social Innovation during Crises, Machine Learning Methods for Personalised, Abstractive Summarisation of Consumer-Generated Media