Identifying journalistically relevant social media texts using human and automatic methodologies. (3rd December 2019)

• Record Type: Journal Article
• Authors: Guimarães, Nuno; Miranda, Filipe; Figueira, Álvaro
• Is Part Of: International journal of grid and utility computing. Volume 11:Number 1(2020)
• Page Start: 72
• Page End: 83
• Publication Date: 2019-12-03
• Subjects: relevance detection -- machine learning -- text mining -- crowdsourcing task -- data mining -- human annotation -- automatic labelling -- natural language processing -- supervised models -- event detection; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.