Do Twitter phenomena check-in popular venues on Foursquare too?

Publication Date20 August 2018
DOIhttps://doi.org/10.1108/IDD-04-2018-0012
Pages137-146
Date20 August 2018
AuthorAysun Bozanta,Birgul Kutlu
SubjectLibrary & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia
Do Twitter phenomena check-in popular
venues on Foursquare too?
Aysun Bozanta and Birgul Kutlu
Department of Management Information Systems, Bogazici University, Istanbul, Turkey
Abstract
Purpose The purpose of this study is to gure out the visiting behaviors of the users who have different characteristics on Twitter.
Design/methodology/approach The visit history of users who share their Foursquare check-ins on Twitter and the characteristics of visited
venues (category, check-in count, tip count, like count, rating, and price tier) was collected with Foursquare API. In addition, the number of
followers, friends, tweets and favorite-count were collected via Twitter API. First, users were clustered according to their Twitter related attributes.
After that, proling was applied on clusters according to the characteristics of the venues that were visited by the users.
Findings Clustering analysis generated three clusters, namely, ordinary, talkative and popular. For each cluster, the visited venues were
investigated according to the price classication, check-in, like, tip counts and the categories. The users in ordinary class prefer cheaper venues
rather than talkative and popular users. On the other hand, popular users prefer the venues with the highest average numbe r of check-ins, likes and
tip counts. The top two categories for all clusters are cafe and shopping mall.
Originality/value This study differentiates from the other studies in the literature by examining the data from Twitter with clustering and
proling these clusters with Foursquare data to understand venue preferences of Twitter users having various characteristics. The ndings of
this study will provide new insights for business owners to understand the customers more comprehensively and design better marketing
strategies.
Keywords Proling, Twitter, Clustering, Social media analytics, Social media analysis, Foursquare
Paper type Research paper
Introduction
Social media has become a very crucial and inevitable part of
business and especially marketing. However, it is quite hard to
take a detailed and complete snapshot of existing social media
platforms because of their autonomousstructure, huge scale of
data and lack of propertools. Even so, they are very fruitful data
sources for understanding various customer proles. For
instance, location-basedsocial networks are the combination of
mobile, location-based services and social media, on the other
hand microblogging sites includesthe ideas or daily routines of
users.
Foursquare is one of the most popular location-based social
network platform where average number of daily check-in is 8
million and there are 50 million monthly active users.
Foursquare allows users to check in at their current
locations[1], leave tips about the venues, explore new venues
around the current location, prepare to-do-listfor venues to go
and add other people as friends. These characteristics of
Foursquare foster users to interact with other users and share
their experiences with each other. On theother hand, business
owners can also benet from the features of Foursquare. For
instance, they can examine the tips about their businesses and
evaluate these feedbacks and improve themselves accordingly.
Thus, they can benet from this information and improve their
business strategies.
Twitter is a microbl ogging site where people can sha re their
ideas about topics , follow each other an d like otherstweets.
Nowadays, Twitter is used as a source for instant news, a
media for reaching to t he companies and giving feedbac k, and
a platform that each u ser can express their feelings and view s
about anything. It h as 336 million monthly active user s in the
rst quarter of 2018 (Number of monthly active Twitter users
worldwide from 1st quarter 2010 to 1st quarter 2018 (in
millions) 2018), and 500 million tweets are shared daily in
2017 (Aslam, 2018). In total, 65.8 per cent of US companies
with 1001employees use Twitter for marketing and 80
per cent of Twitter users have mentioned a brand in a Tweet
(Smith, 2017). Therefore, it is ob vious that Twitter is a
platform which embeds a deep insight about customers and
very useful tool for com munication.
Although some social media platforms have been trying to
integrate different characteristics in one complete platform,
users generally prefer using different ones according to the
various purposes. Thosesocial media platforms contain very
valuable data about customers. However, it is important to
process this data properly and extract usefulinformation for
each company. As each platform includes different customer
data such as location-based social networks or
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
46/3 (2018) 137146
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-04-2018-0012]
This study was supported by the Bogazici University Scientic Research
Projects Fund (Project #11463).
Received 24 April 2018
Revised 23 May 2018
13 June 2018
Accepted 13 June 2018
137

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