Citation: Razis, G.; Georgilas, S.;
Haralabopoulos, G.;
Anagnostopoulos, I. User Analytics
in Online Social Networks: Evolving
from Social Instances to Social
Individuals. Computers 2022, 11, 149.
https://doi.org/10.3390/
computers11100149
Academic Editor: Fernando Bobillo
Received: 20 September 2022
Accepted: 5 October 2022
Published: 7 October 2022
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Article
User Analytics in Online Social Networks: Evolving from
Social Instances to Social Individuals
Gerasimos Razis
1,
* , Stylianos Georgilas
1
, Giannis Haralabopoulos
2
and Ioannis Anagnostopoulos
1
1
Computer Science and Biomedical Informatics Department, University of Thessaly, 35131 Lamia, Greece
2
Henley Business School, University of Reading, Reading RG6 6UD, UK
* Correspondence: razis@uth.gr
Abstract:
In our era of big data and information overload, content consumers utilise a variety
of sources to meet their data and informational needs for the purpose of acquiring an in-depth
perspective on a subject, as each source is focused on specific aspects. The same principle applies to
the online social networks (OSNs), as usually, the end-users maintain accounts in multiple OSNs so
as to acquire a complete social networking experience, since each OSN has a different philosophy in
terms of its services, content, and interaction. Contrary to the current literature, we examine the users’
behavioural and disseminated content patterns under the assumption that accounts maintained by
users in multiple OSNs are not regarded as distinct accounts, but rather as the same individual with
multiple social instances. Our social analysis, enriched with information about the users’ social
influences, revealed behavioural patterns depending on the examined OSN, its social entities, and
the users’ exerted influence. Finally, we ranked the examined OSNs based on three types of social
characteristics, revealing correlations between the users’ behavioural and content patterns, social
influences, social entities, and the OSNs themselves.
Keywords:
online social networks; Twitter; Facebook; Instagram; social influence; social analytics;
behavioural patterns; social individuals
1. Introduction
Our big data era is characterised by an overflowing information overload, mainly due
to the abundance of content and resources, which are often of questionable completeness or
quality. Different types of stakeholders, from citizens to decision-makers and from start-ups
to governments, need to utilise a variety of resources [
1
] to fully satisfy their data and infor-
mational needs, as content can be diffused across different OSNs and websites [
2
]. Usually,
each resource is focused on specific aspects and, consequently, information stakeholders
must rely on multiple heterogeneous sources [
3
] to acquire an in-depth perspective on
a subject.
The same principle applies to the online social networks (OSNs), an integral part of
our everyday lives in terms of communication, news discovery and data consumption.
Three of the most popular services are Twitter, Facebook, and Instagram. Nowadays, every
minor or major event is published and is instantly accessible and visible to the world. The
innate human desire for belonging and socialising is reflected by the fact that, in early 2022,
approximately 4.5 billion people worldwide were OSN users (https://www.statista.com/
statistics/454772/number-social-media-user-worldwide-region/, accessed on 29 August
2022). Moreover, according to the Global Web Index [
4
], in 2020, the average number of
OSN accounts maintained by Millennials or Generation Z (born between 1981 and 2012)
was 8.9, an increase of 44% from the 6.2 accounts in 2015. This growth of multi-networking
is attributed to the specialisation of the individual OSNs. Instagram specialises in the
sharing of photos, YouTube in videos, Twitter in short textual messages, and LinkedIn in
work and business-related content. However, OSNs constantly upgrade, improve, and
Computers 2022, 11, 149. https://doi.org/10.3390/computers11100149 https://www.mdpi.com/journal/computers
