TweEvent: A dataset of Twitter messages about events in the Ukraine conflict

Samyo Rode-Hasinger, Matthias Häberle, Daniel Racek, Anna Kruspe, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events.

Original languageEnglish
Title of host publicationProceedings - 20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023
PublisherInformation Systems for Crisis Response and Management, ISCRAM
Pages407-416
Number of pages10
ISBN (Electronic)9798218217495
StatePublished - 2023
Event20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023 - Omaha, United States
Duration: 28 May 202331 May 2023

Publication series

NameProceedings of the International ISCRAM Conference
Volume2023-text
ISSN (Electronic)2411-3387

Conference

Conference20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023
Country/TerritoryUnited States
CityOmaha
Period28/05/2331/05/23

Keywords

  • Conflict
  • Dataset
  • NLP
  • Social Media
  • Ukraine

Fingerprint

Dive into the research topics of 'TweEvent: A dataset of Twitter messages about events in the Ukraine conflict'. Together they form a unique fingerprint.

Cite this