Andrew Halterman


2023

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Detecting and Geocoding Battle Events from Social Media Messages on the Russo-Ukrainian War: Shared Task 2, CASE 2023
Hristo Tanev | Nicolas Stefanovitch | Andrew Halterman | Onur Uca | Vanni Zavarella | Ali Hurriyetoglu | Bertrand De Longueville | Leonida Della Rocca
Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text

The purpose of the shared task 2 at the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) 2023 workshop was to test the abilities of the participating models and systems to detect and geocode armed conflicts events in social media messages from Telegram channels reporting on the Russo Ukrainian war. The evaluation followed an approach which was introduced in CASE 2021 (Giorgi et al., 2021): For each system we consider the correlation of the spatio-temporal distribution of its detected events and the events identified for the same period in the ACLED (Armed Conflict Location and Event Data Project) database (Raleigh et al., 2010). We use ACLED for the ground truth, since it is a well established standard in the field of event extraction and political trend analysis, which relies on human annotators for the encoding of security events using a fine grained taxonomy. Two systems participated in this shared task, we report in this paper on both the shared task and the participating systems.

2022

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Political Event Coding as Text-to-Text Sequence Generation
Yaoyao Dai | Benjamin Radford | Andrew Halterman
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)

We report on the current status of an effort to produce political event data from unstructured text via a Transformer language model. Compelled by the current lack of publicly available and up-to-date event coding software, we seek to train a model that can produce structured political event records at the sentence level. Our approach differs from previous efforts in that we conceptualize this task as one of text-to-text sequence generation. We motivate this choice by outlining desirable properties of text generation models for the needs of event coding. To overcome the lack of sufficient training data, we also describe a method for generating synthetic text and event record pairs that we use to fit our model.

2021

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Few-Shot Upsampling for Protest Size Detection
Andrew Halterman | Benjamin J. Radford
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence
Andrew Halterman | Katherine Keith | Sheikh Sarwar | Brendan O’Connor
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2019

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Geolocating Political Events in Text
Andrew Halterman
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event–location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.