The role of social media in long-running live events: The case of the Big Four fashion weeks dataset.

Brand Fashion Instagram Live events Popularity Social media Social network

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 01 10 2020
revised: 29 01 2021
accepted: 02 02 2021
entrez: 8 3 2021
pubmed: 9 3 2021
medline: 9 3 2021
Statut: epublish

Résumé

The advent of social media platforms has caused many changes in humans' daily lifestyle. One of the most significant changes is the way in which people participate in social and cultural events. Users' participation in social media platforms is continuously increasing. This has provided brands with new opportunities such as enhancing brand influence and understanding online users' reactions through user-generated content (UGC) analysis. We provide and describe a large-scale hashtag-based dataset of social media posts published on Instagram about the Big Four international fashion weeks in New York, Paris, Milan, and London. The dataset provides the data of the 2018 events and has a periodic and well-established structure. Moreover, we designed a two-stage platform for collecting such large-scale datasets related to long-running events based on relevant hashtags: In the first stage, the platform extracts all the posts, and in the second stage, it extracts the information about the authors of the posts.

Identifiants

pubmed: 33681433
doi: 10.1016/j.dib.2021.106840
pii: S2352-3409(21)00124-4
pmc: PMC7910511
doi:

Types de publication

Journal Article

Langues

eng

Pagination

106840

Informations de copyright

© 2021 The Authors. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

The authors declare no conflict of interest in this article.

Auteurs

Marco Brambilla (M)

Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Giuseppe Ponzio, 34, I-20133 Milano, Italy.

Alireza Javadian Sabet (A)

Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Giuseppe Ponzio, 34, I-20133 Milano, Italy.

Marjan Hosseini (M)

Computer Science and Engineering Department, University of Connecticut, 369 Fairfield Way, Storrs, CT 06268, United States.

Classifications MeSH