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@article{Drivas2022,

title = {Social Media Analytics and Metrics for Improving Users Engagement},

author = {Drivas, Ioannis and Kouis, Dimitrios and Kyriaki-Manessi, Daphne and Giannakopoulou, Fani},

editor = {MDPI},

url = {https://www.mdpi.com/2673-9585/2/2/14},

doi = {https://doi.org/10.3390/knowledge2020014},

isbn = {2673-9585},

year  = {2022},

date = {2022-05-12},

urldate = {2022-05-12},

journal = {Knowledge},

volume = {2},

issue = {2},

pages = {18},

abstract = {Social media platforms can be used as a tool to expand awareness and the consideration of cultural heritage organizations and their activities in the digital world. These platforms produce daily behavioral analytical data that could be exploited by the administrators of libraries, archives and museums (LAMs) to improve users’ engagement with the provided published content. There are multiple papers regarding social media utilization for improving LAMs’ visibility of their activities on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’ engagement with the content that LAMs post to social network platforms. In this paper, we propose a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, (b) examining a more extended set of LAMs social media pages compared to other prior investigations with limited samples as case studies, and (c) understanding which are the administrators’ actions that increase the engagement of users. The results of this study constitute a solid stepping-stone both for practitioners and researchers, as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs services on the Social Web.},

keywords = {analytics, behavioral analytics, facebook, social data analysis, social media, social networks, users interaction, web analytics},

pubstate = {published},

tppubtype = {article}

}