eprintid: 49705 rev_number: 9 eprint_status: archive userid: 1482 importid: 105 dir: disk0/00/04/97/05 datestamp: 2024-09-20 08:25:30 lastmod: 2024-09-20 08:26:07 status_changed: 2024-09-20 08:25:30 type: article succeeds: 48162 metadata_visibility: show creators_name: Abbas, Yasser creators_name: Daouia, Abdelaati creators_idrefppn: 280414994 creators_idrefppn: 076657000 creators_affiliation: Toulouse School of Economics creators_affiliation: Toulouse School of Economics creators_halaffid: 1002422 creators_halaffid: 1002422 title: Understanding World Economy Dynamics Based on Indicators and Events ispublished: pub subjects: subjects_ECO abstract: Studying the content and impact of news articles has been a recurring interest in economics, finance, psychology, and political and media literature over the last 20 years. Most of these offerings focus on specific qualities or outcomes related to their textual data, which limits their applicability and scope. Instead, we use novel datasets that adopt a more holistic approach to data gathering and text mining, allowing texts to speak for themselves without shackling them with presupposed goals or biases. Our data consists of networks of nodes representing key performance indicators of companies, industries, countries, and events. These nodes are linked by edges weighted by the number of times the concepts were connected in media articles between January 2018 and January 2022. We study these networks through the lens of graph theory and use modularity-based clustering, in the form of the Leiden algorithm, to group nodes into information-filled communities. We showcase the potential of such data by exploring the evolution of our dynamic networks and their metrics over time, which highlights their ability to tell coherent and concise stories about the world economy. date: 2024-08 date_type: published publisher: International Association for Statistical Computing id_number: 10.52933/jdssv.v4i5.95 official_url: http://tse-fr.eu/pub/129727 faculty: tse divisions: tse keywords: Dynamic clustering keywords: graph theory metrics keywords: influential economic actors keywords: written media analysis keywords: R, Gephi language: en has_fulltext: FALSE doi: 10.52933/jdssv.v4i5.95 view_date_year: 2024 full_text_status: none publication: Journal of Data Science, Statistics, and Visualisation volume: vol..4 number: n°5 place_of_pub: The Hague refereed: TRUE issn: 2773-0689 oai_identifier: oai:tse-fr.eu:129727 harvester_local_overwrite: number harvester_local_overwrite: volume harvester_local_overwrite: pending harvester_local_overwrite: creators_idrefppn harvester_local_overwrite: creators_halaffid harvester_local_overwrite: issn harvester_local_overwrite: publisher harvester_local_overwrite: place_of_pub harvester_local_overwrite: hal_id harvester_local_overwrite: hal_version harvester_local_overwrite: hal_url harvester_local_overwrite: hal_passwd oai_lastmod: 2024-09-19T07:22:31Z oai_set: tse site: ut1 hal_id: hal-04703431 hal_passwd: nhzsa3&k hal_version: 1 hal_url: https://hal.science/hal-04703431 citation: Abbas, Yasser and Daouia, Abdelaati (2024) Understanding World Economy Dynamics Based on Indicators and Events. Journal of Data Science, Statistics, and Visualisation, vol..4 (n°5).