Ali Hürriyetoğlu, Erdem Yörük, Deniz Yüret, Osman Mutlu, Çağrı Yoltar, Fırat Duruşan, Burak Gürel.

doi:10.24432/C5D59R

TL;DR

We describe a gold standard corpus of protest events that comprise of various local and international sources from various countries in English. The corpus contains document, sentence, and token level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event related information, constructing databases which enable comparative social and political science studies. For each news source, the annotation starts on random samples of news articles and continues with samples that are drawn using active learning. Each batch of samples was annotated by two social and political scientists, adjudicated by an annotation supervisor, and was improved by identifying annotation errors semi-automatically. We found that the corpus has the variety and quality to develop and benchmark text classification and event extraction systems in a cross-context setting, which contributes to generalizability and robustness of automated text processing systems. This corpus and the reported results will set the currently lacking common ground in automated protest event collection studies.

Citation

@inproceedings{
h{\"u}rriyeto{\u{g}}lu2020crosscontext,
title={Cross-context News Corpus for Protest Events related Knowledge Base Construction},
author={Ali H{\"u}rriyeto{\u{g}}lu and Erdem Y{\"o}r{\"u}k and Deniz Y{\"u}ret and Osman Mutlu and {\c{C}}a{\u{g}}r{\i} Yoltar and F{\i}rat Duru{\c{s}}an and Burak G{\"u}rel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=7NZkNhLCjp},
doi={10.24432/C5D59R}
}