Retweet or like? That is the question
| Date | 10 September 2018 |
| Published date | 10 September 2018 |
| DOI | https://doi.org/10.1108/OIR-04-2017-0135 |
| Pages | 562-578 |
| Author | Eva Lahuerta-Otero,Rebeca Cordero-Gutiérrez,Fernando De la Prieta-Pintado |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
Retweet or like? That is
the question
Eva Lahuerta-Otero
Department of Marketing, University of Salamanca, Salamanca, Spain
Rebeca Cordero-Gutiérrez
Faculty of Computer Science, Pontifical University, Salamanca, Spain, and
Fernando De la Prieta-Pintado
Department of Computer Science and Automation, University of Salamanca,
Salamanca, Spain
Abstract
Purpose –Due to the size and importance of social media, user-generated content analysis is becoming akey
factor for companies and brands across the world. By using Twitter messages’content, the purpose of this
paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM.
Design/methodology/approach –In total, 30,082 tweets collected from 10,120 Twitter users were
classified based on four assorted brands. By comparing with multiple regression techniques high vs low
purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic
processing of information, the authors examine the causes of tweet diffusion.
Findings –The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet
length) influence its diffusion and popularity.
Research limitations/implications –This study validated the use of information processing theories in
the social media field. The study showed a picture on how different Twitter elements influence eWOM and
message diffusion under several purchase involvement situations.
Practical implications –The results of this study can help social media brand community managers of all
types of companies on how to write their Twitter messages to obtain greater dissemination and popularity.
Originality/value –The study offers a unique deep brand analysis which helps brands and companies to
understand their social media popularity in detail. Depending on product category, companies can achieve
maximum social impact on Twitter by focusing on the interactivity items that will work best for their
products or brands.
Keywords Twitter, Diffusion, Heuristic-systematic model, Perceived purchased value, Popularity,
Purchase involvement
Paper type Research paper
1. Introduction
The current society 2.0 lives, communicates, reports and interacts through social media.
The importance of virtual communities grew to such an extent that a communication’s plan
cannot be credibly if it only focuses on traditional marketing strategies and does not include
the latest digital marketing advances.
Not only that, but penetration in access to social networks through the mobile phone
will reach 81.9 percent in 2017 (eMarketer, 2016). Brands, companies and organizations
which are aware of the power and potential of the content generated by social network
users, will use diverse 2.0 platforms to approach their target audience. Since many
individuals share their opinions, tastes, interests and preferences daily, new information
systems need to mine enormous amount of data to extract knowledge from it
(López Sánchez et al., 2016; López et al., 2016).
Twitter is a microblogging platform par excellence with 313m active monthly users, out
of which 82 percent access it from their mobile device (Twitter, 2016a). According to the
latest statistics (Internet Live Stats, 2016) users send 7,443 tweets per second, which means
that on this social network more than 643,075,200 messages are generated daily. It is
therefore not surprising that microblogging platforms have a prominent role in information
Online Information Review
Vol. 42 No. 5, 2018
pp. 562-578
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-04-2017-0135
Received 28 April 2017
Revised 4 August 2017
10 April 2018
Accepted 15 May 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
562
OIR
42,5
sharing, whether it be news, travel, brands or other sectors ( Jansen et al., 2009; Parra-López
et al., 2011). Microblogging networks allow users to access information and empower them
to participate in their dissemination (Steyn et al., 2011), with the minimal effort of a single
mouse click. Given the full adoption of these technologies, Twitter turns out to be a valid
source of information in the matters of social exchange and public opinion, moreover, it
keeps readers informed on current worldwide issues (Hoeber et al., 2016).
Since Twitter users can receive information without having to follow others, an essential
aspect is knowing what makes a message attractive and what makes it stand out from the
rest, as suggested in the literature the diffusion of a message varies with its content
(Zhang et al., 2017). A common notion that persists among the virtual community of this
network is that only users with specific qualities can propagate their ideas with success.
However, the messages of these members must include some definite elements that help
them become more widespread. Given that very few empirical researches, using data mining
techniques had been conducted in this field (Ikeda et al., 2013), the focus of our study is to
find out what elements and characteristics of tweets make their propagation and diffusion
successful. The conclusions that will arise from this study will be of benefit to brands,
companies and users; brands and companies will be able to approach their target audiences
more effectively and users will spread their messages more easily, by employing additional
elements (hashtags, mentions, links, etc.) on tweets.
The structure of this work is as follows: the first part is a review of the literature based on
the heuristic-systematic information processing model. In Section 2, we present the study
variables that make up the theoretical model and its associated hypotheses. Methodology is
found in Section 3 and finally, in the discussions and conclusions sections we specify the
theoretical and commercial implications of this work and possible future lines of research.
2. Backgrounds and prior literature
The number of tweets sent in a single second is overwhelming and users face hundreds of
thousands ofmessages containing complexinformation to process. For thisreason, additional
elements in a tweet network will reduce uncertainty and help network users process
information with a minimal effort but in a valid and credible way. The term diffusion is
understood as a process of communication in which the information (news, an advertising
message, a brand tweet, etc.) is spread over time through certain channels, by members of a
social system (such as Twitter whose users form a virtual community) (Rogers, 1995, p. 5).
Microblogging networks allow brands and companies to communicate with users in a
much more personal way than traditional means of communication. The simplicity of
following a user or being followed (following and follower) on the social platform makes it
easier to constructbroadcast networks(Chu and Kim, 2011) through which informationcan be
propagated quickly and effectively. In recent research conducted by (Liu et al., 2012;
Xu and Yang, 2012; Zhang, Peng, Zhang; Wang and Zhu, 2014, Zhang and Watts, 2008), a
heuristic-systematic model of information processing (Chaiken, 1980), HSM from now on, is
used to explain whysome posts are more popular than others,reaching higher diffusion and
popularity rates (understood as having more retweets or likes). When a user retweets a
message, they give it veracity; this shows that after having processed the information they
make a conscious decision to share it (Liu et al., 2012). The model argues that there are two
different types of information processing which are not exclusive, on the contrary they can
complement each other and act simultaneously. The first one is the systematic model of
information processing in which the message plays a fundamental role in forming a critical
judgment. Users who use a systematic strategy to process information will make behavioral
decisions (retweet, comment or like it) basedon their assessment of the quality of information
received (Zhang and Watts, 2008), which is based on message content (Metzger et al., 2010).
Therefore, users should analyze each of the messages they receive and must have sufficient
563
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