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A Three-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions' Communication on Facebook

Published: 24 October 2018 Publication History

Abstract

Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.

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  • (2024)Application of New Technologies in Social Media Analytics: Enhancing User Engagement at Global UniversitiesNew Technologies, Development and Application VII10.1007/978-3-031-66271-3_45(413-422)Online publication date: 28-Jul-2024

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  1. A Three-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions' Communication on Facebook

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      cover image ACM Other conferences
      TEEM'18: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality
      October 2018
      1072 pages
      ISBN:9781450365185
      DOI:10.1145/3284179
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      • University of Salamanca: University of Salamanca

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 24 October 2018

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      Author Tags

      1. Automatic Content Analysis
      2. Data Mining
      3. Social Media
      4. Text Mining
      5. Top World-Ranked Higher Education Institutions

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      TEEM'18

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      TEEM'18 Paper Acceptance Rate 151 of 243 submissions, 62%;
      Overall Acceptance Rate 496 of 705 submissions, 70%

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      • (2024)Application of New Technologies in Social Media Analytics: Enhancing User Engagement at Global UniversitiesNew Technologies, Development and Application VII10.1007/978-3-031-66271-3_45(413-422)Online publication date: 28-Jul-2024

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