Global Contentious Politics Database (GLOCON) Annotation Manuals

TL;DR

This paper introduces the GLOCON annotation manuals, which standardize the coding of protest events in news articles to support automated detection and extraction for social science research.

Contribution

It provides detailed annotation guidelines adapted from linguistic standards for coding protest events, enhancing the quality and consistency of the GLOCON Gold Standard Corpus.

Findings

Manual ensures high annotation accuracy and consistency.

Guidelines are adapted from established linguistic annotation standards.

The corpus supports automated protest event detection.

Abstract

The database creation utilized automated text processing tools that detect if a news article contains a protest event, locate protest information within the article, and extract pieces of information regarding the detected protest events. The basis of training and testing the automated tools is the GLOCON Gold Standard Corpus (GSC), which contains news articles from multiple sources from each focus country. The articles in the GSC were manually coded by skilled annotators in both classification and extraction tasks with the utmost accuracy and consistency that automated tool development demands. In order to assure these, the annotation manuals in this document lay out the rules according to which annotators code the news articles. Annotators refer to the manuals at all times for all annotation tasks and apply the rules that they contain. The content of the annotation manual is built on the general principles and standards of linguistic annotation laid out in other prominent annotation manuals such as ACE, CAMEO, and TimeML. These principles, however, have been adapted or rather modified heavily to accommodate the social scientific concepts and variables employed in the EMW project. The manual has been molded throughout a long trial and error process that accompanied the annotation of the GSC. It owes much of its current shape to the meticulous work and invaluable feedback provided by highly specialized teams of annotators, whose diligence and expertise greatly increased the quality of the corpus.