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Article

Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings

by
Bruna Rocha
1,† and
Álvaro Figueira
1,2,*,†
1
Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
2
INESC TEC, Rua Roberto Frias, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Current address: DCC-FCUP, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal.
Informatics 2025, 12(1), 6; https://doi.org/10.3390/informatics12010006
Submission received: 15 October 2024 / Revised: 27 December 2024 / Accepted: 3 January 2025 / Published: 9 January 2025

Abstract

In today’s competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions’ (HEIs’) rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics—engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.
Keywords: higher education institutions; ranking system analysis; text mining; machine learning; topic modeling; prediction analysis higher education institutions; ranking system analysis; text mining; machine learning; topic modeling; prediction analysis

Share and Cite

MDPI and ACS Style

Rocha, B.; Figueira, Á. Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings. Informatics 2025, 12, 6. https://doi.org/10.3390/informatics12010006

AMA Style

Rocha B, Figueira Á. Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings. Informatics. 2025; 12(1):6. https://doi.org/10.3390/informatics12010006

Chicago/Turabian Style

Rocha, Bruna, and Álvaro Figueira. 2025. "Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings" Informatics 12, no. 1: 6. https://doi.org/10.3390/informatics12010006

APA Style

Rocha, B., & Figueira, Á. (2025). Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings. Informatics, 12(1), 6. https://doi.org/10.3390/informatics12010006

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