Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/552
Title: A Unified Framework for Decision-Making Process on Social Media Analytics
Authors: Misirlis, Nikolaos
Vlachopoulou, Maro
Editors: Sifaleras, Angelo
Petridis, K.
Type: Book chapter
Subjects: FRASCATI::Social sciences::Economics and Business::Business and Management
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Social media
social media analytics
SMA framework
social data
sma methodologies
decision making
Issue Date: 2019
Publisher: Springer
First Page: 147
Last Page: 159
Volume Title: Operational Research in the Digital Era – ICT Challenges
Part of Series: Springer Proceedings in Business and Economics
Part of Series: Springer Proceedings in Business and Economics
Abstract: Data analysis originated from social media presents huge interest among researchers and practitioners. In order to understand better and clarify notions and methodologies used regarding social media analytics, a framework is needed with clear classification schemes and procedures. The objective of this paper is to develop a unified framework that clusters the possible categories of data and their interactions. Furthermore, the proposed framework indicates the procedures that have to be followed in order to achieve the most optimized choice of social media analytics (SMA) methodology, initiating the 4P’s procedure (People, Purpose, Platform, and Process). Next, the methodologies used on SMA, in specific the structural and content-based analysis, as well as their sub-methodologies (community and influencers’ detection, NLP, text, sentiment, and geospatial analysis) are indicated. The proposed framework will facilitate researchers and marketers on the decision-making process by clarifying each step, regarding the objectives, the involved parties, the social media platform, and the analysis process that can be chosen.
URI: https://doi.org/10.1007/978-3-319-95666-4_10
https://ruomo.lib.uom.gr/handle/7000/552
ISBN: 978-3-319-95665-7
978-3-319-95666-4
ISSN: 2198-7246
2198-7254
Other Identifiers: 10.1007/978-3-319-95666-4_10
Appears in Collections:Department of Applied Informatics

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