Efficient Large-Scale Stance Detection in Tweets

View Sample PDF

Author(s): Yilin Yan (University of Miami, USA), Jonathan Chen (Miami Palmetto Senior High School, USA)and Mei-Ling Shyu (University of Miami, USA)
Copyright: 2020
Pages: 17
Source title: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch037

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

Purchase

View Efficient Large-Scale Stance Detection in Tweets on the publisher's website for pricing and purchasing information.

Abstract

Stance detection is an important research direction which attempts to automatically determine the attitude (positive, negative, or neutral) of the author of text (such as tweets), towards a target. Nowadays, a number of frameworks have been proposed using deep learning techniques that show promising results in application domains such as automatic speech recognition and computer vision, as well as natural language processing (NLP). This article shows a novel deep learning-based fast stance detection framework in bipolar affinities on Twitter. It is noted that millions of tweets regarding Clinton and Trump were produced per day on Twitter during the 2016 United States presidential election campaign, and thus it is used as a test use case because of its significant and unique counter-factual properties. In addition, stance detection can be utilized to imply the political tendency of the general public. Experimental results show that the proposed framework achieves high accuracy results when compared to several existing stance detection methods.

The IRMA Community

Research IRM

Efficient Large-Scale Stance Detection in Tweets

Purchase

Abstract

Related Content

IRMA Sponsors