Big Data and Business Intelligence on Twitter and Instagram for digital inclusion

 Carlos Barroso-Moreno, Madrid (Spain)

 Laura Rayon-Rumayor, Madrid (Spain)

 Antonio Bautista García-Vera, Madrid (Spain)

https://doi.org/10.3916/C74-2023-04

Keywords

Social network analysis, Big Data, education, disability, digital inclusion, influence groups

Abstract

Social media can contribute to an inclusive society, but they are also asymmetrical and polarised communication spaces. This requires competent teachers to build critical digital citizenship. The aim of this article is twofold: to present web scraping and text analytics as tools that define teachers' digital competences, and to investigate which posts on Twitter and Instagram are most viral in relation to education, disability and inclusion. A total of 48,991 publications in Spanish and English were analysed, corresponding to the period from 13 October 2021 to 1 May 2022. The 100 most viral posts were selected, and correlations were identified between the sentiment, gender and influence associated with the content, its temporal and geographic space. The results show that economic and political influence groups are the most viral, relegating non-profit organisations or individuals with altruistic outreach to second place; only on international days is this trend reversed. Bots do not interfere to impose messages; it is artificial intelligence algorithms that overshadow vindictive and humanistic content. The most influential people are predominantly male, associated with institutional accounts in the political sphere. It is concluded that Big Data and Business Intelligence tools help teachers to analyse relevant educational and social issues, and to acquire a collective ethic in the face of new educational challenges.

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Fundref

This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Complutense de Madrid in the line Excellence Programme for university teaching staff, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

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Technical information

Received: 29-05-2022

Revised: 21-06-2022

Accepted: 13-07-2022

OnlineFirst: 30-10-2022

Publication date: 01-01-2023

Article revision time: 23 days | Average time revision issue 74: 40 days

Article acceptance time: 45 days | Average time of acceptance issue 74: 69 days

Preprint editing time: 172 days | Average editing time preprint issue 74: 194 days

Article editing time: 217 days | Average editing time issue 74: 239 days

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