Explainability of Text Clustering Visualizations—Twitter Disinformation Case Study

Autor:	Jiri, Zarsky, Gaetan, Lopez, Tomas, Kliegr, Mike, Potel
Rok vydání:	2022
Předmět:	Politics Cluster Analysis Humans Disinformation Social Media Computer Graphics and Computer-Aided Design Algorithms United States Software
Zdroj:	IEEE Computer Graphics and Applications. 42:8-19
ISSN:	1558-1756 0272-1716
Popis:	While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c564ac53ad4a14baef4a0c6e84e289e https://doi.org/10.1109/mcg.2022.3179914 Zobrazit plný text záznamu