Location Based Service in Social Media: An Overview of Application

Journal of Asian Institute of Low Carbon Design, 2017
243
Location Based Service in Social Media: An Overview of Application
Yuyun1
, Bart Julien Dewancker2
1
PhD Student, Graduate School of Environmental Engineering, the University of Kitakyushu
2
Professor, Department of Architecture, the University of Kitakyushu
Abstract
This paper presents a literature review on the use of geolocation data on social media. Geolocation is one
of the feature on the social media which utilize the GPS devices embedded in the smartphones, tablets, or
computers gadget that can show a user’s location map. This is related to a virtual user activity in the parts of
the world, when and where they are. The main objective of this research is to investigate the extent to which
spread of articles related to the application of location-based data on social media, such as problem issues,
techniques applied, problem solved especially in urban environment context, published from 2010 to 2016.
We analyzed 35 references which accordance with this field. The attribute prepared based on the application
area, years, and author's parts to simplify the organizing of geolocation data applications. Then, the data format
summarized in the tabular form for helping a readers. Authors find that three important issues that we have
identified related to this field; distances, locations, and movements. Our research can contribute for the
researchers for them future work regarding to the developments and limitations of each articles.
Keywords: geolocation; social media; urban application; literature review
1. Introduction
Social media has become one of the most important
tools of our daily life. Social media sites is an individual
structure connected which consist of one and more users
to make the social interactive online in a virtual world.
This can provide the space to expand our social
relationship between individuals and the general public.
The rapid development of smartphones technology
device has helped to increase the number of users of
such services.
Almost all smartphones now are equipped with the
GPS feature. Through social media apps, people can
share the virtual activity which can attach a location
map to express happiness, pleasure, or an opinion about
what they see, places they visit and where they are.
Many new features are added to increase the comfort
and convenience of social media users. One of them is
a location-based feature. Location feature can explain
the coordinate point in the form longitude and latitude,
which can report their location when and where it
happened [1]. As a result, social media such as
Facebook, Twitter, Instagram, and Path can inform of
user position is pinned in the status update posted (e.g.
text messages, photos, and videos). Nowadays, almost
all social media have used this element to sharing
information. Thus through that manner, individual
historical trajectories can be detected. With millions of
data and documents is resulted from social media, it
makes the researchers and policy makers to understand
in depth about the advantage resulted. In the literature,
we found many of works have been published based on
the geolocation data such as a recommendation for the
location of physical, choosing potential customers [2,
3], travel and tourist routes identify [4, 5]. As such, a
great opportunity that these data has an effect important
for urban planners to do a planning.
In this paper, we collected a range of methods and
application used to conclude the location of social
media users, with descriptions of urban human activity
and mobility patterns from academic databases. A total
35 references were reviewed from international
journals and proceeding articles which accordance with
the sector from 2010 to 2016, and then authors explain
and identify the latest finding in each category. We
make effort to answer the following two questions: (1)
what is the problem discussed? (2) How is the research
problem resolved (techniques applied)? (3) What was
the conclusion of each article?
2. Data Collection Review
From a review result of geolocation data, authors
find that researchers used two social media applications
to catch the behaviors of individual on the online site,
namely: Twitter and Foursquare applications. The
Reason is that this site is freely available and open
access to download through the REST API application
provided by the developers. Its provide programmatic
access to read and write Twitter data, e.g., record
creates a new tweet, read user profile, follower data,
Contact author: Yuyun, PhD student, the University of
Kitakyushu, 1-1 Hibikino, Wakamatsu-ku Kitakyushu-shi,
Fukuoka-ken 808-0135, Japan. Tel: +81-9086680584. -
e-mail: yuyunwabula@gmail.com

244
time zone, location information and shown it in the
JSON format [41]. As well as the Twitter app, the
Foursquare app also uses the API to collect the
individual profile information such as users, venues,
venue groups, check-ins, photos, events, and result
from Foursquare API are also shown in JSON Format
[42].
3. Research Methodology
Our initial goal is to investigate the extent to which
the application of geolocation social media data. To
start the data collection, at first a criterion of
geolocation social media is typed in the search engine
to get the subject targets. In addition, the articles also
were searched from the journal site such as Plos one
journal, Science Direct, IEEE Xplore, Journal of
Information Science in order to add the kinds of
literature. Then the articles grouped into several groups
to obtain the relevant papers according to research topic.
Then the next process was conducted to analyze the
problems discussed, techniques applied and a result of
each paper. To achieve the degree of the research, we
used some considerations in order to determine the type
of papers that will be utilized. Such as considered the
article published in an international journal and an
international conference articles which have the impact
factors criteria, in this case, should be indexed
publication. The research methodology of this research
is illustrated in figure. 1.
4. Analysis of Geolocation Social Media
Geolocation of Social media such as Facebook,
Twitter, Foursquare, Instagram, and Path have provided
new insights to understand the shape and structure of a
real city. This data has the potential to impact many
other areas including travel demand modeling,
ubiquitous computing, epidemiology, urban planning,
security and health monitoring [6]. From the literature
list, authors identified three essential elements which
become a key namely movement, location, and distance.
Almost all of the models that are used to improve these
elements. For this reason, we classify all the papers into
two categories according to the issues that have been
discussed in previous research. These categories are
defined as follows:
4.1 Urban Applications
Various works have been studied to reveal user
behavior on social media sites. In this section explain
how geolocation data of social media can be modeled
to solve the existing problems. Authors found some
literature that relates to the urban application category
as the reference for readers (see Table 1). In bellow, we
provide a summarization of the selected article.
Many of applications and methods have been
proposed to know the distribution of using geolocation
social media data. Mahmud et al. [7] presented a new
algorithm namely heuristic classifiers for prediction the
home location of Twitter user in different places such
as geographic region, city, and state. Their model used
the time zone as the criteria to improve prediction
accuracy. They analyzed movement difference of
Twitter users to predict whether a user was traveling in
a certain period of time. They found that this approach
works well for predicting the twitter users at the home
location with accuracy 0.61 (city), 0.70 (state), 0.80
(time zone), and 0.70 for (region). In other studies,
Eisentein et all. [8] Used a probabilistic model to
predict the Twitter users in the geographical area and
state. They informed 58% classification accuracy for
the area (4-way and 24% for the accuracy of state (49%
way) in the region of the United States and Columbia
District.
Cheng et al. [9] proposed a probabilistic framework
for estimating the Twitter user’s in the city-level
location. This model can resolve the geolocation
feature, and the targeting is local advertisements. They
introduced three kinds of approach for analyzing: (i)
Tweet content, (ii) component classification for
automatically identifying words in the Tweet and (iii)
estimate of user’s location. They reported that only
require 100 Tweets to establish a region, with
measurement 51% of Twitter users within 100 miles of
their actual location. Chang et al. [10] described
Identification of
Subject Target
Online
Searching
Articles related
Associated
To UF
Associated
to HM
Other
applications
Keywords searching criteria
- Urban Fields (UF)
- Human Mobility (HM)
Put Out
NO
NO
YES
YES
YES
Selected
Articles
Review of
content articles
Concluding
summary
Problem
Analysis
Method
Applied
Article
Groups
Arranged
in Table
1,2,&3
NO
Fig. 1 Research methodology flow

245
location estimation for predicting the home location of
Twitter users. Proposed unsupervised methods based
on Non-Localness and Geometric-Localness. They
found that about 5112 Twitter users can predict their
home within 100 miles with accuracy 0.499.
Brown at al. [12] used the location-based data from
the Foursquare social media to analysis the visited
places relationship with the structures of the social
network. They have found that a place where to people
meet has very strong inﬂuence to be a region. Chang
and Sun. [14] this review used k-means clustering to
count the data distribution with distance approach for
calculating a place between the closest centers for each
user. Hasan and Ukkusuri. [19] Developed a topic
model to extract multi-day patterns and individual
activity pattern. Although the model can be combined
with data from traditional surveys, it also contributed to
count the weekly activities pattern, and user-specific
activity patterns.
Frias-Martines et al. [21] the paper is focused on
two issues: one crow modeling and the other applied
urban computing for urban planning. The research is
proposed the use geolocation Tweet for urban planning
application. System created with used the spectral
clustering method. Tree cities are presented in the
experiments (Manhattan, London, and Madrid). The
technique proposed to identify the land segmentation
and land use. They have found that geolocation
information to be very useful for urban planners for
land use modeling. The result shows that a density
comparison each city were 84.13 tweets/km2, 42.51
tweets/km2, and 10.88 tweets/km2
. Frias-Martines at al.
[24] used Twitter data for identifying urban land scape.
On these terms, both of them located on using the
clustering technique to classify the area. Noulas et al.
[22] used the same method as mentioned above to
model user activity pattern in New York and London
cities. With the geolocation data provided by
Foursquare, places information can be detected such as
home, food, park and others. Wakamiya et al. [23] they
proposed models of aggregation to characterize urban
area by extracting Twitter geolocation data. To
understand the form of a city, they used daily activity
pattern through number of user tweet from the crowd’s
movement.
Pavalanathan and Eisenstein. [25] Developed a
model to analyze the text Twitter based on the user’s
position. They found that the best accuracy of text-
based location for men above the 40 years old.
Khanwalka et al. [26] demonstrated a distance model
for content based geolocation Tweet. They tested their
approach for 17 million multilingual tweets and
distributed in into 85% Arabic, and 15% English from
2.6 million Twitter users. They reported the method
provides an effective way for grouping the trending
news topics, geo-political entities and Hashtags.
Chandra et al. [27] used a probabilistic framework to
estimate the city-level user location on the contents of
tweet. This research wanted to know whether there was
a relation the location information of the user with their
environments. Zhang et al. [29] in this worked using
the geographic information from Foursquare check-ins
data. The main objective was for modeling of urban
neighborhoods. They devised an optics algorithm for
exploring and extracting neighborhood boundaries in
cities. There main points resulted from developing of
model such as, (1) Activity hotspot detection, (2)
Measuring area homogeneity and, (3) neighborhood
detection. Ryoo and Moon. [38] Proposed a
probabilistic generative model to infer a city user`s
geolocation in Twitter using their textual contents. As a
result was 60% of users are identiﬁed within 10 km of
their locations in the Korean city. Kotzias et al. [39]
proposed a social graph to model the geo-location of
Twitter social media to decide the user interests from
Table 1. References on the topic of urban applications
Ref Authors Areas Techniques Used
[7] Mahmud et all (2014) Home identification Heuristic classifiers
[8] Eisentein et al (2010) Region and city Probabilistic model
[9] Cheng et al (2010) Location estimate Probabilistic framework
[10] Chang et al (2012) Home location estimate Unsupervised methods
[12] Brown et al (2013) Physical spaces Unknown
[14] Chang and Sun (2012) Individual location K-means clustering
[19] Hasan & Ukkusuri (2014) Urban activity pattern Topic model
[21] Frias-Martines et al (2014) Urban planning Spectral clustering
[22] Noulas et al (2011) Region Spectral clustering
[23] Wakamiya et al (2011) Urban area Aggregation model
[24] Frias-Martinez et al (2012) Urban land scape Unsupervised neural network
[25 Pavalanathan & Eisenstein (2015) City population Latent variable model
[26] Khanwalkar et al (2013) Region Distance measurement
[27] Chandra et al (2011) City area Probabilistic framework
[29] Zhang et al (2013) Urban neighborhoods Unknown
[38] Ryoo & Moon (2014) City area Probabilistic generative
[39] Kotzias et al (2015) City area Social graph

246
the locations visited. This model to identify the users at
a location of a city-level granularity and showed the
geographical coordinates to individual tweets within a
city. Lists of table 1 is the references that explain the
application of geolocation of social media for the
process of urban fields. On the column 1 and 2
representing number and authors of the article, then the
others column is areas and techniques applied.
4.2 Mobility Applications
This part aims to analyze and discover human
mobility and activity pattern used the geolocation
Twitter data. Such article made by Kinsella et al. [11]
two issues applied in this research: (1) Individual
location predicting and (2) predicting of location.
Proposed a language model of location using
coordinates extracted from geolocation social media.
They created a geographical model to predict the Tweet
individual location. They found that Twitter user in the
country, state and city level and achieving a three-to
ten-fold increase in accuracy at the zip code level. Gao
et al. [13] proposed a social-historical model to study
the relationship between the social ties and the user’s
check-in behavior. This model showed how historical
effect could help in location prediction.
Sun and Li. [15] Utilized Foursquare check-in data
to investigate the gender in New York City. Used
ellipse-based and spatial distribution models to
categorize the activities of individuals. The research
resulted that there was a gender difference in the travel
and activity pattern of users. This data can be exploited
to produce the gender daily travel data. Hasan at al. [20]
used the same model and data to understand the
lifestyle choice, related to the social relationship and
individual level patterns. They found that the same
check-ins with two users will have the same lifestyle in
the social media structure. Chua et al. [28] Proposed
a system for spatial planners to analyses human
distribution used geo-location social media data. Three
contributions from the system namely, (1) used a graph-
based approach for constructing the system, (2) visual
analytic to identify the movement between the
locations and (3) Functional and scalability
Sun et al. [30] compared three different methods to
detect a city center: (1) Cluster detection using LGOG,
(2) Cluster detection using DBSCAND to counts points,
and (3) Cluster detection using GN. Then three cities
(Berlin, Munich, and Cologne cities) were used as the
experiment for modeling the system. From their
discussion result, they found (1) models can identify
the city center with a precise boundary and, (2) more
flexible for the polycentric city. Hasan & Ukkusuri.
[31] Explored the life-style pattern from Foursquare
geolocation data. By used Probabilistic topic model to
solve two issues, (1) Patterns of user interests in the
different places types and (2) User visits patterns in
different neighborhoods. They found that the exact
probability can provide useful information for user
choices and interests.
Phithakkitnukoo and Olivier. [32] Used data from
Foursquare to analyze the user distribution pattern of
three different cities (London, Paris, and New York).
They found that the distribution of social activity in the
tree cities was running is non-linear, especially in the
Food and nightlight part which have the strongest
social activity. Cheng et al [33] this study presented a
paper that explains the use of check-ins data to model
the user mobility patterns. They proved that geo-
location Twitter can be used as a potential data to
understand human mobility, and found that
metropolitan areas have a large number of Twitter user
rather than the rural areas. Noulas [34]; Scellato [35]
introduced a rank-based model to studies the human
mobility and urban space in cities. The model
calculates the number of places between origins to
destination. The research concluded that this data has a
Table 2. References on the topic of mobility application
[11] Kinsella et al (2011) Individual location Language model
[13] Gao et al (2013) User’s social behavior Social-historical model
[15] Sun & Li (2015) Travel and activity patterns Ellipse-based and spatial distribution
[20] Hasan et al (2016) Lifestyle choice Topic model
[28] Chua et al (2015) Human mobility Flow sampler
[30] Sun et al (2016) Mobility / city center Unknown
[31] Hasan & Ukkusuri (2015) Human mobility Probabilistic topic
[33] Cheng (2011) Human mobility Unknown
[32] Phithakkitnukoon & Olivier (2011) Human mobility Unknown
[34] Noulas et al(2011) Human movement Rank-based
[35] Noulas et al (2010) Human mobility Unknown
[36] Luo et al (2016) Human mobility Space-time trajectory
[37] Cho et al (2011) Human mobility Unknown
[40] Comito et al (2016) Human movement Sequential pattern mining
[43] Yuyun & Dewancker (2016) Human mobility Frequency distribution

247
potential applications for the urban planning and
location-based advertisement.
Luo et al. [36] proposed a novel method to
characterize of human mobility and focused on the
impact of demography. Their methodology utilized a
concept of space-time trajectory to estimate each
Twitter user in the Chicago urban area. The data
focused on the user at the home location with the
demographics information such as ethnicity, gender,
and age. They found that user distributions of at home
location still follow power law distribution. Then
between these three demographic factors, ethnicity
shows have the largest influence on human mobility.
Cho et al. [37] used the geolocation data from Gowala
and Brightside to analyze the social relationship of
human mobility. The research concluded that social
relationships can explain 10% to 30% of all human
movements, while periodic behavior explains 50% to
70%. Yuyun and Dewancker. [43] Them paper aim to
explore the relation between geolocation Twitter with
the existence of people in the urban area. They
combined Twitter and questionnaire data to catch the
Twitter profile and validated it with the population
statistic data. Comito et al. [40] Utilized of large data
sets from the Tweet geolocation to analyze the people
movements in real life. They proposed a novel
methodology for detecting relevant semantic locations
from geo-tagged posts. The model to calculate the stay
duration, daytime and places popularity of people
visited. The main objective of the study was to provide
the top location or popular tourist destination such as
shopping malls, streets, restaurants, and cinemas. Table
2 lists the references in this category.
4.3 Implementation of Location-Based Social
Media
Beside the urban and mobility application, authors
also found three articles related to the use of
geolocation social media data such as the medical
information, traffic incident, and rainfall monitoring.
As discussed by Dredze et al. [16] develop a model to
identify the location information made by a patient’s
candidate when the tweet posted. This data using a
Carmen geolocation data shared by Twitter. Gu et al.
[17] this paper presented the geolocation data for
monitoring the traffic incidents in two regions, the
Pittsburgh and Philadelphia city. Used natural language
processing model to extract the Twitter data. This paper
focused on two categories namely geo-location to know
the scene position and incidents can be identified
through analyzing tweets text. From this data accounts
for approximately 5% of traffic accidents. While other
information about 60%-70% Twitter accounts come
from public agencies and media. Eduardo et al. [18]
analyzed the content of Twitter data for monitoring of
rainfall in Brazil. Table 3 lists the references in this
group.
4.4. Articles Distribution
Figure 2 presents the distribution of the articles
based on category and publication years. The article
associated to the urban, mobility and other application.
From this result, we acknowledge that urban
applications were used more than mobility application.
Meanwhile, the quantity of article was dominated in the
years 2011 and 2016 for mobility application, while in
2014 was urban applications
5. Conclusion
This paper provided a literature review on the use of
location based social media data on article published
from 2010 to 2016. Authors aim to analyze the article
collection from two perspectives: urban and mobility
applications. A total of 35 articles was collected and
categorized with various methods. 17 articles related to
the urban fields, 15 articles for the urban area, and 3
articles each associated with the medical information,
traffic incident, and rainfall monitoring. From the result
of analysis, we found three essential issues was
considered of researchers in this sector: distance,
movement, and location aspects. Almost all of that
article exists in order to improve these elements. On the
distance factor, they analyzed how far it is between
users in two places. Then, where they were going,
related to the movement category. Meanwhile, the
location type indicating what places they visited and
how often do people tend to visit a new places. At the
Table 3. References on the application in other fields
[16] Dredze et al (2012) Medical information Unknown
[17] Gu et al (2016) Traffic incidents Natural language processing
[18] Eduardo (2016) Rainfall monitoring Unknown
Fig. 2 Article distributions
2010 2011 2012 2013 2014 2015 2016
Urban 2 3 3 3 4 2 0
Mobility 1 4 0 3 0 2 5
Other 0 0 1 0 0 0 2
0
1
2
3
4
5
Articlenumbers
Urban Mobility Other

248
same time, authors also found that researchers used two
social media applications to catch the behaviors of
individual on the real world, namely: Twitter and
Foursquare app.
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