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Advanced Methods: Operationalizing Social Network Services Data — Deep Content Analysis to Comprehend Brand Presence


Publication Details
Authors: Hajikhani Arash, Porras Jari
Editors of book: Daim, Tugrul; Pilkington, Alan
Publishing place: The University of Sussex Falmer, Brighton BN1 9RF UK
Publication year: 2018
Language: English
Title of parent publication: Innovation Discovery: Network Analysis of Research and Invention Activity for Technology Management
Volume number: 30
ISBN: 978-1-78634-405-2
eISBN: 978-1-78634-407-6
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

Businesses
are moving beyond traditional industry silos and coalescing into richly
networked ecosystems. The co-evolution in social and economic systems and
enhancement of technology that facilitates communication is creating new
opportunities for innovation but also new challenges for many incumbent
enterprises [16]. Social media
platforms are currently gaining in popularity and becoming dominant landscapes to
be used when developing company strategy. User engagement in social network services
(SNSs) platforms continues to rise. Statista, an online portal for market data,
estimates there will be some 2.67 billion social media users around the globe
in 2018, up from 1.91 billion in 2014 [27]. Another Web
analytics company, Compete, reports that among the top 10 websites in 2010, SNSs
accounted for some 75% of total page views in the US, up from 31% in 2001 and 40%
in 2006 [1].



The
increased worldwide usage of smartphones and mobile devices is leading to high
user engagement rates with SNSs platforms [15]. SNSs employ
mobile and web-based technologies to create highly interactive platforms enabling
individuals and communities to share, co-create, discuss and modify user generated
content (UGC) [17]. SNSs such as
Twitter and Facebook are currently dominant and such technologies have changed
how people lead their social lives – and fundamentally altered marketing and
communications. User trust in SNSs is being built and formed, as evidenced by people’s
faith in social media content regarding health and wellness issues [6]. SNS
technologies have also transformed traditional marketing communication models. In
the transition to Web 2.0, the rise in interactive digital media has radically
transformed company and consumer contact from passive to interactive. Consumers
are simultaneously the initiators and recipients of information exchange. The
combination of both traditional and social mediums allows companies to develop
integrated communication strategies to reach consumers on a myriad of
platforms, enabling a wide sphere of influence [11].



Companies
are utilizing SNS platforms due to their immense popularity, which also delivers
an economical and highly efficient way to reach large audiences [17]. The earlier
studies show that many major businesses have already made the move [35]. Consumers are
increasingly adopting active roles in co-creating marketing content with
companies and their respective brands. In turn, companies and organizations are
looking to online social marketing programs and campaigns in an effort to reach
consumers [11] The emergence
of SNSs is creating another frontline in which companies need to be innovative through
the social media channels they maintain. Interacting on such channels has
become an essential task for firms, giving them a holistic view of their user
base and helping them be accountable and responsive to the requirements of
their customers.



SNS
usage is creating a huge volume of data resulting from interactions on SNSs. Hence,
operationalizing practices to extract valuable information from multiple data
points in SNSs is a future challenge for companies. The wisdom of the crowd,
documented on the web, plays a key role in major decisions in almost any
context [24]. Therefore,
investigating a practical way to explore and mine valuable information and data
from user-generated content (UGC) is important. Particularly, the UGC developed
on social media is capable of capturing features, such as public opinion on the
products and services offered by firms and the development of innovative
marketing strategies. Companies and managers have long attempted to identify
and track key performance indicators to measure the success of a strategy. This
is no less a priority in SNS [11].



Operationalizing
social media data is a challenge that can be tackled from multiple perspectives
by researchers and practitioners. Among the mainstream SNS outlets, Twitter,
which is centered around exchanging short messages, has exponentially increased
its popularity, resulting in the investigation of a wide variety of research
issues regarding the mining of Twitter data, especially studies exploring the
extracting of public sentiment [4, 10, 28, 29]. In this study,
the content generator’s impact on attracting public (user) attention is
examined in greater detail because some criteria play a more important role in a
tweet being read and distributed. In this study, the aim is not only to
investigate the overall sentiment polarity of the public regarding a specific
product category, but also to look deep into the top SNS content that can help
to form the public image of a company. To evaluate such content, a survey was
designed and a crowd -based quality evaluation was conducted regarding the top SNS
content related to five manufacturers and their competing products during a period
of investigation.



The
chapter is organized as follows: First, the emergence of SNSs is
discussed from various perspectives, including a discussion on the availability
of the valuable large data provided by SNS platforms. The importance of the operationalization
of SNS data is then discussed by highlighting advancements in the computer
sciences and text analysis techniques that enable the making of meaningful
assumptions from SNS content. Furthermore, as the intention is to contribute to
the ongoing challenge of operationalizing SNS data for a more intelligent
understanding of user reactions and their perceptions of brands, a case study
of the top US smartphone manufacturers is conducted in Section 2 and the
research questions are formed accordingly. In Section 3, the methods for retrieving
data from Twitter and making search queries are presented. More specifically,
the top tweets were identified based on the impact they generated. After that,
the types of profile were categorized based on the five Twitter profile types
defined in this study. A sentiment analysis is also utilized to understand the
polarity of the tweets (positive or negative) and the survey designed to
perform a crowd intelligence evaluation regarding the quality content of the tweets
is introduced. Finally, multiple variables were constructed and a correlation
analysis was executed to explore the possible relationships. Based on the findings,
several suggestions for social media marketing strategies are proposed.


Research Areas

Last updated on 2019-13-03 at 12:00