Cross-Context News Corpus for Protest Event-Related Knowledge Base
                    Construction

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

doi:10.1162/dint_a_00092

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…As a usability analysis, no training data were provided for this Task. Namely, the event definition applied for coding the reference event data set is the same as the one adopted for Shared Task 1 (Hürriyetoglu et al, 2021a) and any data utilized for Task 1 and Task 2, such as the one from Hürriyetoglu et al (2021), or any additional data could be used to build a system/model run on the input data.…”

Section: Training Datamentioning

confidence: 99%

Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021

Giorgi¹,

Zavarella²,

Tanev³

et al. 2021

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

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Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently. But, the ability to both (1) extract events "in the wild" from text and (2) properly evaluate event detection systems has potential to support a wide variety of tasks such as monitoring the activity of sociopolitical movements, examining media coverage and public support of these movements, and informing policy decisions. Therefore, we study performance of the best event detection systems on detecting Black Lives Matter (BLM) events from tweets and news articles. The murder of George Floyd, an unarmed Black man, at the hands of police officers received global attention throughout the second half of 2020. Protests against police violence emerged worldwide and the BLM movement, which was once mostly regulated to the United States, was now seeing activity globally. This shared task asks participants to identify BLM related events from large unstructured data sources, using systems pretrained to extract socio-political events from text. We evaluate several metrics, assessing each system's ability to evolution of protest events both temporally and spatially. Results show that identifying daily protest counts is an easier task than classifying spatial and temporal protest trends simultaneously, with maximum performance of 0.745 (Spearman) and 0.210 (Pearson r), respectively. Additionally, all baselines and participant systems suffered from low recall (max.5.08), confirming the high impact of media sourcing in the modelling of protest movements.

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Section: Training Datamentioning

confidence: 99%

Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021

Giorgi¹,

Zavarella²,

Tanev³

et al. 2021

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

Self Cite

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“…In the GLOCON Project, we develop fully automated tools for document classification, sentence classification, and detailed protest event information extraction that will perform in a multisource, multicontext protest event setting with consistent recall and precision for each country context (Hürriyetoğlu et al, 2020). In order to cope with the challenges of developing generalizable tools that can handle source text heterogeneity, we designed the tool development process to incorporate news sources from multiple contexts, which may contain different grammar and diction.…”

Section: The Approach Of the Glocon Projectmentioning

confidence: 99%

Random Sampling in Corpus Design: Cross-Context Generalizability in Automated Multicountry Protest Event Collection

Yörük

Hürriyetoğlu

Duruşan

et al. 2021

American Behavioral Scientist

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What is the most optimal way of creating a gold standard corpus for training a machine learning system that is designed for automatically collecting protest information in a cross-country context? We show that creating a gold standard corpus for training and testing machine learning models on the basis of randomly chosen news articles from news archives yields better performance than selecting news articles on the basis of keyword filtering, which is the most prevalent method currently used in automated event coding. We advance this new bottom-up approach to ensure generalizability and reliability in cross-country comparative protest event collection from international and local news in different countries, languages, sources and time periods, which entails a large variety of event types, actors, and targets. We present the results of comparing our random-sample approach with keyword filtering. We show that the machine learning algorithms, and particularly state-of-the-art deep learning tools, perform much better when they are trained with the gold standard corpus from a randomly selected set of news articles from China, India, and South Africa. Finally, we also present our approach to overcome the major ethical issues that are intrinsic to protest event coding.

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“…The data for this task has been created using the method described in Hürriyetoglu et al (2021). The task is multilingual but the data distribution across languages is not the same.…”

Section: Datamentioning

confidence: 99%

IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

Awasthy¹,

Ni²,

Barker³

et al. 2021

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events From Text (CAS

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In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection -Shared Task 1 at CASE 2021. 1 The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish). To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder. We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages. Our systems achieve an average F 1 score of 81.2. Out of thirteen subtask-language tracks, our submissions rank 1 st in nine and 2 nd in four tracks.

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Cross-Context News Corpus for Protest Event-Related Knowledge Base Construction

Cited by 18 publications

References 23 publications

Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021

Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021

Random Sampling in Corpus Design: Cross-Context Generalizability in Automated Multicountry Protest Event Collection

IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

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