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has been cited by the following article:

• TITLE: GatherTweet: A Python Package for Collecting Social Media Data on Online Events

  AUTHORS: Claudia Kann, Sarah Hashash, Zachary Steinert-Threlkeld, R. Michael Alvarez

  KEYWORDS: Data Science, Movements, Social Media Data, Twitter, Network Science, Data Mining, Python

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.2, February 28, 2023

  ABSTRACT: Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.