Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data

Abstract

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city.

Full text

Citation: Abesinghe, S.;
Kankanamge, N.; Yigitcanlar, T.;
Pancholi, S. Image of a City through
Big Data Analytics: Colombo from
the Lens of Geo-Coded Social Media
Data. Future Internet 2023,15, 32.
https://doi.org/10.3390/fi15010032
Academic Editor: Manuel José
Cabral dos Santos Reis
Received: 16 December 2022
Revised: 2 January 2023
Accepted: 5 January 2023
Published: 9 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
future internet
Article
Image of a City through Big Data Analytics: Colombo from the
Lens of Geo-Coded Social Media Data
Sandulika Abesinghe 1, Nayomi Kankanamge 1, Tan Yigitcanlar 2, * and Surabhi Pancholi 3
1
Department of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka
2City 4.0 Lab, School of Architecture and Built Environment, Queensland University of Technology,
Brisbane, QLD 4000, Australia
3School of Architecture and Built Environment, Deakin University, Geelong, VIC 3220, Australia
*Correspondence: tan.yigitcanlar@qut.edu.au; Tel.: +61-7-31382418
Abstract:
The image of a city represents the sum of beliefs, ideas, and impressions that people have of
that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping
exercises. Such methods consume more time and effort and are limited to a small number of people.
However, recently, people tend to use social media to express their thoughts and experiences of a
place. Taking this into consideration, this paper attempts to explore city images through social media
big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the
image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by
using community sentiments expressed and images posted on social media platforms. For that, this
study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and
geo-coded social media analyses. The study findings revealed that: (a) the community sentiments
toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related
to locating landmarks, paths, nodes, edges, and districts have a significant impact on community
cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach
to capture the image of a city. The study informs urban authorities in their placemaking efforts by
introducing a novel methodological approach to capture an image of a city.
Keywords:
city image; Lynchian elements; image processing; sentiment analysis; social media
analytics; urban analytics; big data analytics; urban informatics; Colombo; Sri Lanka
1. Introduction
Cities are ever-changing environments with new urban elements being introduced
into the built environment on a regular basis. In his seminal book ‘Image of the City’, Kevin
Lynch [
1
] introduced how the city images can be conveniently understood through the five
‘Lynchian’ elements—i.e., landmarks, paths, nodes, edges, and districts. Moreover, people’s
experiences and perceptions about a city are also influenced by the image of the city. An
image of a city provides the community with an orientation, direction, and emotional
security about the city. Moreover, a poor city image, which is non-discernible by the above
five elements and design, leads to psychologically unsatisfying urban environments for
the people living and visiting a city [
1
]. A good city image has an influence on the shaping
up of an overall city identity as well as people’s place-based identities [
2
]. The image of
the city is not only a critical factor having an impact socially on the local community, but it
plays a significant role in boosting a country’s economy by contributing to their branding,
promoting tourism, and in staying competitive for attracting investors [3].
While a good city image positively impacts attractiveness, a poor city image, on the
other hand, leads to many negative impacts, such as limited investment attractiveness,
absence of prospects for the future, and so on [
4
]. In the age of globalization, a city’s
image is a mix of physical and social values, as well as a tool for branding where cities are
competing to identify and establish their unique identities as their competitive edge [
5
–
7
].
Future Internet 2023,15, 32. https://doi.org/10.3390/fi15010032 https://www.mdpi.com/journal/futureinternet

Future Internet 2023,15, 32 2 of 21
Henceforth, in their attempts to create and maintain a good city image to achieve the
highest expectations of the citizens and economy, contemporary cities are increasingly
investing in better and more efficient ways to examine city images as an important tool to
understand the changing perceptions of the citizens [8,9].
In recent years, a new dimension has been added to the attempts of discerning the
idea of an image of the city with the breakneck speed of growth seen in the use of social
media [
10
]. Social media can be identified as one of the main and largest data sources in the
modern era and, more importantly, a potential platform to examine city images accurately
within shorter time periods [
11
]. It holds millions of textures and graphical data related
to every kind of topic. Most importantly, social media data is regarded as crowd-sourced,
which is collected from the public [
11
]. Furthermore, social media data collection methods
are timesaving, cost-effective, and a much more suitable method to avoid physical contact
with the public in this COVID-19 pandemic situation [12].
A huge amount of data is available on social media to capture public opinion, and
many big data analysis tools and algorithms are available to analyze them further. For
instance, there are over two billion active social media users in the Asia–Pacific region, and
it records the world’s highest number of social network users. For example, in November
2022, as an Asian country, 76% of Sri Lankans used Facebook, 13% used YouTube, 5% used
Pinterest, 2% used Instagram, and another 2% used Twitter [
13
]. People use aforesaid social
media networks to share their thoughts, feelings, and observations within their network.
Such information shared in social networks can be considered good reflections about
landmarks (e.g., towers, museums), nodes (e.g., urban squares, train stations), edges (e.g.,
waterfronts, lakes), paths (e.g., nature trials, streets), districts (e.g., villages, neighborhoods),
and their experiences attached to them. This opens a novel research direction and a tool
for scholars to investigate the image of a city by analyzing social media content. The
crowdsourced social media data can be regarded as one of the strongest potential platforms
to examine city images accurately within shorter time periods.
The integration of data analytics with the Lynchian approach for exploring the image
of the city has drawn interest from scholars belonging to diverse fields, including spatial
cognition, urban studies, urban planning, information and communication technologies,
and artificial intelligence [
14
–
16
]. Research studies have been investigating city images
based on different techniques, such as using small-size data analytics—i.e., surveys and
interviews—geographical information system analysis, sketch maps, and so on [
16
,
17
].
Now, with the help of the huge amount of existing social media data (i.e., social media big
data) generated per minute, city images can be examined from a broader perspective and
cover a much larger scale and scope [18].
However, despite its abundant and easy availability, the use of big data, specifically
social media data and its analysis as an approach has found a limited application in the
field of urban studies. Moreover, studies that have specifically used social media data
to comprehensively test the image of the city using Kevin Lynch’s theory base are even
rarer [
17
]. For instance, references [
19
,
20
] have tried to understand one psychogeographical
aspect of cities using crowdsourced data, which they are limited to one or two aspects such
as place identity. Moreover, reference [
21
] have tried to understand the streets of London
through Kevin Lynch’s theory using Flickr and Open Street Map (OSM), which lacks
discussions on how community perceptions on social media influence the development of
the city image.
This paper, therefore, aims to contribute to this understudied area of research. This
study, hence, attempts to address the question of ‘How can the image of a city be examined
by using geo-coded social media data?’ To address the research question, the study adopted
the Capture–Understand–Present (CUP) framework introduced by [22].

Future Internet 2023,15, 32 3 of 21
2. Background of the Literature
2.1. Image of a City and Its Elements
The public image of a city is designed by a series of physical and perceptible objects.
There are other influencing factors on developing city images, such as the social meaning
of an area, its related function and history, or even its name. The first descriptive intro-
duction to the public’s perception of the built environment is Kevin Lynch’s study on city
images [
23
]. Lynch explained that in cities, ‘legibility,’ or clarity in visuals, makes it easier
for people to navigate. Based on his case studies in three cities in the USA, he explained that
public image is built on the legibility of five urban elements—so-called Lynchian elements,
which are landmarks, paths, nodes, edges, and districts—described in detail below.
Landmarks are a different type of point-of-reference that the observer does not enter.
Instead, they are external. Additionally, they are usually a simple physical object, such as
a building, a sign, a store, or a mountain [
24
]. Some landmarks are far away, seen from
a variety of angles and distances, and used as radial references over the tops of smaller
elements. Landmarks are often used as identity and even structure clues for the public, and
they seem to be relied on more and more as a journey becomes more familiar. They may be
located within the city or at a distance. Landmarks mostly deliver a constant direction for
all practical purposes [1].
Paths are how the observer travels on a regular, irregular, or potential basis. Streets,
walkways, transit lines, canals, and railroads are examples of paths. People observe the city
as they move through it, and perceive other environmental elements located and related
along such paths [1].
Nodes are points in a city, strategic locations where an observer can enter and exit.
They can be primarily junctions, points where transportation stops, paths cross or converge,
or transition points from one structure to another. These nodes serve as the focal point
and epicenter of a district, radiating their influence and serving as a symbol [
25
]. Nodes
are typically the convergence of paths and events on the journey, and they are related to
the concept of the path. Nodes can be found in almost every image and can even be the
dominant feature in some cases [1].
Edges are the linear elements that the observer does not consider to be a path. They
can be defined as the linear breaks in continuity between two phases: shores, railroad cuts,
development edges, and walls. Such edges may act as permeable barriers that separate one
region from another, or they may be seams, lines that connect two regions. Although not
as prominent as paths, these edge elements are significant organizing features for many
people, particularly in holding together generalized areas, i.e., the outline of a city by water
or wall [1].
Districts can be defined as medium-to-large sections of the city. They are expected to
have a two-dimensional extent, where the observer mentally gets the feeling of entering/
‘inside of’. Districts are recognizable as having a unique and common but identifying
character. Such districts are considered exterior references if visible from the outside and
always identifiable from the inside. Most people organize their city in this way to some
extent, with individual differences in whether paths or districts are the most important
elements. It appears to be dependent not only on the individual but also on the city in
question [1].
The works done by Lynch emphasized the psychological need of people for contact
with other people. Lynch’s ideas about the thoughtful design of spaces to encourage social
contact are acknowledged in the works of many key urban designers from his time and
after [
26
,
27
]. Despite being criticized for more focus on the physicality of spaces, the
underlying thought of the whole body of works that Lynch produced lies in the core idea
of creating aesthetically appealing and psychologically satisfying spaces that satisfy human
needs for social contact. His theory, therefore, stands contemporary and still finds its
applicability in the age of social media—in other words, the contemporary digitized public
space [15].

Future Internet 2023,15, 32 4 of 21
Moreover, the Lynchian approach makes itself distinguished from other urban analysis
approaches as it is a creative qualitative approach based on the analysis of empirical data,
i.e., ‘mental maps’, derived from people based on their perceptions. Rather than simply
reporting surveys, this technique uses qualitative methods, such as observations, interviews,
and conceptual map analyses, to generalize findings into the above higher-order conceptual
categories. This makes Lynch’s theory and approach a perfect base for this study as it
assists in understanding the meaning that people attach with the help of data collected
from social media.
2.2. Image of a City and Social Media Big Data
Meanings that people attach and their perceptions are key contributors to the devel-
opment of an image as it converts an abstract space into place [
28
,
29
]. Lynch [
1
] argues
that the legibility of the city influences the image of the city by shaping up the meaning,
experience, and perceptions of the people. The advent of globalization and digital tech-
nologies has been a strong influencer on people’s perceptions about the real world by
shifting the meanings of time and distance and blurring the distinction between public and
private [
30
–
32
]. With the prolific use of social media becoming common in the 21st Century,
it has altered the working modes and has influenced many aspects of human lives and the
meanings people attach to their surroundings [33].
Additionally, there is an increasing trend in using social media as a source of big data
in urban research [
34
,
35
]. Platforms such as Instagram, Twitter, and Facebook generate
considerably large amounts of geo-coded images, videos, and texts about users’ daily lives,
and much of this data is available in the public domain. The key attributes of social media
data that make it an effective choice are that it is: (a) crowdsourced; (b) diverse in terms of
data produced; (c) efficient in terms of capturing the perceptions of millions of users; (d)
and accessible in the pandemic situation. As an emerging area of research, social media data
has been applied in many disciplines, including: (a) marketing [
36
,
37
]; (b) disaster/crisis
management [
38
,
39
]; (c) business analytics [
40
,
41
]; (d) political science [
42
,
43
]; (e) social
science [
44
–
46
]; and so on. Especially, social media facilitates the inclusion of ordinary
citizens in scientific decision making—i.e., citizen science [47,48].
Despite there being few scholars, such as [
15
], who questioned the relevance of Lynch’s
theory with the advent of digital technologies, arguing that Lynch’s imminent fear of
disorientation would have held no ground after the invention of Google Maps, number of
other research studies have used online content as a tool to study the perceptions of urban
environments. For instance, reference [
49
] mapped user behaviors and sentiments in New
York using geo-coded Twitter data. Although the data sources and underlying algorithms
were not disclosed, references [
50
–
52
] used the Instasights heat map, which is a web-based
social media mapping tool, to monitor the impact of renewed waterfront areas in Spanish
cities.
In their study of 26 cities, reference [
41
] used geo-tagged Panoramio photos to under-
stand the perception of city images. Recently, reference [
17
] in their study, tested the use
of ‘big data’ and ‘small data’ methods together in the Tri-City Region in Poland to explore
the perception of city images. The study has proved the relevance of Lynch’s theory in
the digital age by deriving parallel social media-based indicators. Nonetheless, the use of
social media to understand complex and subjective urban phenomena related to urban
design [
48
,
49
] is an understudied but emerging area of research. Still, it demands more
comprehensive studies to examine the image of a city through social media.
2.3. Social Media Analytics
This study adopted social media analytics as the major technique. Among many social
media analytic framework studies, it is popular to use the Capture–Understand–Present
(CUP) framework [
22
]. Capture is the process of gathering data from relevant social media
sources, archiving the needed, and extracting the necessary [
22
]. Understand is the method
of data processing to obtain meaningful results. It may involve statistical methods such as

Future Internet 2023,15, 32 5 of 21
data mining, machine learning, and natural language processing. Present is the arranging
of obtained results in a meaningful way and showing them as the final output.
3. Research Design
3.1. Case Study
The selected study area is the Colombo city of the western province of Sri Lanka.
Colombo is the commercial capital of Sri Lanka, as well as the country’s main economic
hub. The study was conducted in Colombo not only because it is the commercial capital
of the country but also because it is one of the best examples in the Sri Lankan context
of a rapidly changing urban form influenced by globalization and triggered by economic
changes. Additionally, it is the most densely populated area—over 52,000 per square miles.
This study used the Keywords–Hashtag (K–H) network mining method to identify
popular keywords and hashtags tweeted or circulated together. K–H network mining acts
as a content-based approach that was designed to enable related keywords and hashtags
to discover from the links among keywords and hashtags. Such related K–H networks
were explored algorithmically. Accordingly, this study identified 52 Lynchian elements.
From them, 24 Lynchian elements were removed due to low occurrence (<10). This study
considered the identified 28 Lynchian elements as the main keywords and their associated
hashtags to mine tweets. After, a bounding box was demarcated, covering the location
of the aforesaid 28 Lynchian elements. As in Figure 1, the case study is bounded by the
bounding box coordinates of 6.88, 79.81 (Lower Left) and 6.98, 79.89 (Upper Right).
Future Internet 2023, 15, x FOR PEER REVIEW 6 of 22
Figure 1. Case study area.
The identified 28 elements were: Altair Residential Condominium (ARC), Arcade In-
dependence Square (AIS), Beddagana Wetland Park (BWP), Beira Lake (BL), Bellanvila
Park (BP), Bandaranaike Memorial International Conference Hall (BMICH), Borella Cem-
etery (BC), Colombo City Center (CCC), Diyatha Uyana (DU), Colombo Fort (CF), Galle
Face (GF), Galle Face Hotel (GFH), Gangaramaya Temple (GT), Hilton Hotel (HH), Ke-
laniya Temple (KT), Kingsbury Hotel (KH), Liberty Plaza Building (LPB), Lotus Tower
(LT), Mount Lavinia Beach (MLB), One Galle Face Buildi ng (OGFB), Parliament (P), Petta h
Market (PM), Port City (PC), Savoy Cinema (SC), Shangri La Hotel (SLH), Viharama-
hadevi Park (VP), World Trade Center (WTC), and Zoo (Zoo)
Figure 1. Case study area.

Future Internet 2023,15, 32 6 of 21
The identified 28 elements were: Altair Residential Condominium (ARC), Arcade In-
dependence Square (AIS), Beddagana Wetland Park (BWP), Beira Lake (BL), Bellanvila Park
(BP), Bandaranaike Memorial International Conference Hall (BMICH), Borella Cemetery
(BC), Colombo City Center (CCC), Diyatha Uyana (DU), Colombo Fort (CF), Galle Face
(GF), Galle Face Hotel (GFH), Gangaramaya Temple (GT), Hilton Hotel (HH), Kelaniya
Temple (KT), Kingsbury Hotel (KH), Liberty Plaza Building (LPB), Lotus Tower (LT), Mount
Lavinia Beach (MLB), One Galle Face Building (OGFB), Parliament (P), Pettah Market (PM),
Port City (PC), Savoy Cinema (SC), Shangri La Hotel (SLH), Viharamahadevi Park (VP),
World Trade Center (WTC), and Zoo (Zoo)
3.2. Social Media Analytics with the CUP Framework
As in Figure 2, this study adopted the Capture–Understand–Present (CUP) frame-
work [22] to analyze and synthesize the data.
Future Internet 2023, 15, x FOR PEER REVIEW 7 of 22
3.2. Social Media Analytics with the CUP Framework
As in Figure 2, this study adopted the Capture–Understand–Present (CUP) frame-
work [22] to analyze and synthesize the data.
Figure 2. The CUP framework.
3.2.1. Capture: Twitter and Instagram Data
The first stage of the framework involves ‘capturing’ Twitter social media infor-
mation. However, Twitter has certain merits and constraints. The main merits include: (a)
Twitter is one of the most rapidly growing social media microblogging services; (b) Twit-
ter allows researchers to use a free Twitter ‘application programming interface’ (API); (c)
unlikely in Facebook and Instagram, Twitter data can be considered ‘open data’, which
delivers succinct real-time data to the public [53]; (d) Twitter search and streaming APIs
allow researchers to write queries based on certain keywords and/or hashtags to down-
load information [54]; (e) and analyzing Twitter data is considered a novel approach of
harvesting dispersed community knowledge [55].
Restricted API-based data accessibility can be considered the main limitation of Twit-
ter, where APIs provide access to only 1% of publicly available Twitter data. Additionally,
from a collected sample, only around 10% is either geo-located or geo-tagged [56]. Even
the geo-coded tweets are becoming harder to collect due to not sharing personal mobile
location information. As well, there are ethical barriers, as such information consists of the
exact location (x,y coordinates of a location) information of the people.
For instance, only 22% of tweets consisted of geo-coded information from the total
harvested data. Therefore, geo-tagged information is often collected through data provid-
ers—i.e., DataSift—with 100% access. This is a costly approach [38]. However, since this
Figure 2. The CUP framework.
3.2.1. Capture: Twitter and Instagram Data
The first stage of the framework involves ‘capturing’ Twitter social media information.
However, Twitter has certain merits and constraints. The main merits include: (a) Twitter is
one of the most rapidly growing social media microblogging services; (b) Twitter allows
researchers to use a free Twitter ‘application programming interface’ (API); (c) unlikely

Future Internet 2023,15, 32 7 of 21
in Facebook and Instagram, Twitter data can be considered ‘open data’, which delivers
succinct real-time data to the public [
53
]; (d) Twitter search and streaming APIs allow
researchers to write queries based on certain keywords and/or hashtags to download infor-
mation [
54
]; (e) and analyzing Twitter data is considered a novel approach of harvesting
dispersed community knowledge [55].
Restricted API-based data accessibility can be considered the main limitation of Twitter,
where APIs provide access to only 1% of publicly available Twitter data. Additionally,
from a collected sample, only around 10% is either geo-located or geo-tagged [
56
]. Even
the geo-coded tweets are becoming harder to collect due to not sharing personal mobile
location information. As well, there are ethical barriers, as such information consists of the
exact location (x,y coordinates of a location) information of the people.
For instance, only 22% of tweets consisted of geo-coded information from the to-
tal harvested data. Therefore, geo-tagged information is often collected through data
providers—i.e., DataSift—with 100% access. This is a costly approach [
38
]. However, since
this study related to location-specific keywords, such as Lotus Tower, Pettah, and so on,
the aforesaid limitation did not affect the results of this study. As another limitation, refer-
ence [
57
] highlighted the bias of age groups of the Twitter data. Except for these limitations,
an increasing number of studies use tweets as the main data source [58,59].
The obtained raw data consists of user ID, text body, available images and videos,
time stamp, global positioning system (GPS) coordinates, and the language of the tweet.
Then, this study adopted the five-step data-cleaning process presented by [
60
]. Those are
time zone filter, date filter, bot filter, relevance filter, and text filter.
Time-zone filtering, date filtering, and bot Filtering are done through the Twitter APIs
while collecting data. The UTC/GMT+5:30 time zone is used for the time-zone filter. These
filter out the tweets tweeted within the given time zone. Date filtering is used to filter
out tweets tweeted within a given time frame. This study contains tweets starting from 1
January 2015 to 31 December 2020. Bot filter is used to filter automated tweets. After these
three filtering processes were completed, the extracted data was saved to ‘.CSV’ formatted
files.
Each file was then opened using Macro-enabled MS Excel for further cleaning. Rel-
evance filtering was done to remove any irrelevant messages to the studying context or
interests. All the conjunctions, Be-verbs, links, and special characters were removed using
text filtering. After all these steps, the cleaned data was finally saved as a ‘.XLSX’ formatted
file for further processing.
Secondly, capturing Instagram social media information was done. Hashtag filtering
and location filtering are functions offered by the Instagram app. Only the relevant images
were downloaded at this stage. All the portraits, group photos, and advertisements were
filtered out. Including Twitter images, altogether, 762 images were selected to create the
final data set covering the 28 selected locations of Colombo city.
3.2.2. Understand and Present
Descriptive Analysis
Twitter/Instagram data consist of much information, such as ‘created_date’, ‘user-
name’, ‘user-screen name’, ‘text’, ‘photo/video’, and ‘user-location’. This study used a
descriptive analysis (DA) to provide a larger view of the collected data. This study used
three descriptive statistics, namely, Twitter statistics, user analysis, and web-link (URL)
analysis. Identifying widely used hashtags is especially useful for urban planners as they
reflect the emotive and evaluative reflections toward the places they visit [
61
]. Twitter
statistics provide information about the number of users, number of retweets, and number
of hashtags used. This study considered all ‘retweets’ as new tweets.
Image Processing
Image processing (IP) was conducted to understand the content of Instagram images
and their importance to perceive Lynchian elements. With the computational limitations

Future Internet 2023,15, 32 8 of 21
and the long process of training a large “Places” dataset, the study used a publicly available
pre-trained model introduced by [
62
]. Zhou et al. [
62
] used wide residual networks in his
process to train over 10 million image datasets. This model can predict each image with its
content, whether it is a “Skyscraper”, “Tower”, “Park”, “Beach”, etc. It also provides its
prediction probability. This probability was used as a weight to measure the final output.
The formula used to calculate the probability is given in Equation (1) below.
Probability Sum (class i)=∑k=n
k=1Probability (class i)(1)
where kis the index of the sum, nis the number of input images and i is the index of the
class. Accordingly, the class with the highest probability sum value was selected as the
predicted class for the relevant image. Each predicted class was then categorized into the
suitable Lynchian component based on Kevin Lynch’s definition of city image components.
Sentiment Analysis
Sentiment analysis (SA) can be identified as the most widely used text classification
method to analyze a given textual phase and tell whether the selected phase is positive,
negative, or neutral [
63
]. This study aims to classify how each tweet describes the respective
place depending on the positive and negative factors/words of the tweet. Accordingly, a
value ranging between (
−
4) and +4 was given to each tweet. To do so, it is required to make
a Word Bag of positive and negative words. To make this Word Bag, this study extracted
a portion of Positive and Negative words from the already downloaded filtered tweets.
Using the obtained data, the positive sentiment values were measured as the ratio of the
positive tweets to the total number of tweets, i.e., Positive sentiments = (Positively classified
tweets/Total number of tweets) x 100. The study used Weka 2.0 open-source machine
learning software and ArcGIS to perform and present the sentiment analysis [47,64].
Popularity Analysis
Popularity analysis (PA) was conducted by counting the number of tweets per year
posted related to a place name. Popularity increases with the number of tweets tagged.
Referring to this, the study analyzed the popularity of each Lynchian element among
Twitter communities during the past six years. The results of these four analyses are
presented in the next section.
4. Results
4.1. Descriptive Analysis
Of the 6805 tweets, 2586 (38%) were original and 4219 (62%) were retweeted with
supportive content, reflecting the highly interactive nature of users. All Twitter discussions
developed around 237 hashtags. The hashtag analysis identified (excluding 51 hashtags
used for data mining) 86 key hashtags among them as the most strongly associated ones
with the Lynchian elements. Among them, the most used 50 hashtags were: #selfie,
#blue, #seaview, #skyporn, #Sky, #ocean, #sea, #coastline, #wonderofasia, #view, #train,
#viewfromthetop, #cityview, #luxury, #luxuryhotel, #urbandesign, #greenbuilding, #econ-
omy, #green, #blue, #ScenicsNature, #Cloud, #Rooftop, #arialview, #discovercolombo, #de-
velopment, #project, #waves, #randomshot, #roadride, #ship, #sand, #beach, #lankabeach,
#naturalbeach, #Beauty, #weekend, #trave, #AwesomeMoments, #cappuccino #cappuc-
cinoArt, #convocation, #sunset, #shopping, #fireworks, #restaurent, #foodlover, #foodie,
#family, #dayout. Most of these hashtags were used in relation to the user experiences
abiding with the Lynchian elements identified.
In total, 2190 users contributed to the creation of the dataset of 6805 tweets. About 79%
of the tweets were circulated by individual users and 21% by institutions. However, 75% of
the top 20 most active users were individuals. There were 576 tweets with informative URLs
in the dataset. Within the period of 2015–2016, the total number of places identified for the

Future Internet 2023,15, 32 9 of 21
analysis was 22. During the next two years of time (2017–2018), this number increased to
25. This number further increased to 28 from 2019–2020.
4.2. Image Processing
This study selected a unique number of random non-portrait Twitter and Instagram
images from each place and tested them on Wide-resnet architecture [
65
–
67
]. The process
of Image Processing can be divided into two main parts. They are the prediction of the class
and the identification of the relevant Lynchian category. An exemplified demonstration
of the image processing exercise is given in Figure 3. Accordingly, all 762 images were
classified.
Future Internet 2023, 15, x FOR PEER REVIEW 10 of 22
(a) (b)
Figure 3. Exemplified image processing. (a) Input image: inserted Image; (b) output image: heat
map of the features used to make the correct predictions.
As given in Table 1, each prediction was saved with its relevant probability value.
The obtained probabilities for each class were summed together to get the most probable
prediction of the output class. Using the behavior of the predictions obtained for each
place, this study sorted each Lynchian element into its right Lynchian category by refer-
ring to the Lynchian definition, i.e., Prediction Class—Skyscraper, Lynchian element—
Landmark/Prediction Class: Ocean, Lynchian element: Edge.
Table 1. Identifying Lynchian categories.
Place Name and Code Number of Pho-
tos Prediction Class Probability Lynchian Cate-
gory
Altair Residential Condominium (ARC) 25 Skyscraper 51.98% Landmark
Arcade Independence Square (AIS) 30 Mosque/Outdoor 31.34% Landmark
Beddagana Wetland Park (BWP) 28 Broadwalk 72.48% District
Beira Lake (BL) 30 Canal 42.81% Edge
Bellanvila Park (BP) 28 Park 32.46% District
Bandaranaike Memorial International
Conference Hall (BMICH) 30 Elevator lobby 22.94% Landmark
Borella Cemetery (BC) 25 Cemetery 75.40% Landmark
Colombo City Center (CCC) 30 Department Store 47.91% Landmark
Diyatha Uyana (DU) 25 Park 25.36% District
Colombo Fort (CF) 25 Library 21.52% District
Galle Face (GF) 30 Ocean 22.62% Edge
Galle Face Hotel (GFH) 30 Ballroom 30.78% Landmark
Gangaramaya Temple (GT) 25 Temple 40.98% Landmark
Hilton Hotel (HH) 28 Hotel 42.35% Landmark
Figure 3.
Exemplified image processing. (
a
) Input image: inserted Image; (
b
) output image: heat
map of the features used to make the correct predictions.
As given in Table 1, each prediction was saved with its relevant probability value.
The obtained probabilities for each class were summed together to get the most prob-
able prediction of the output class. Using the behavior of the predictions obtained for
each place, this study sorted each Lynchian element into its right Lynchian category by
referring to the Lynchian definition, i.e., Prediction Class—Skyscraper, Lynchian element—
Landmark/Prediction Class: Ocean, Lynchian element: Edge.

Future Internet 2023,15, 32 10 of 21
Table 1. Identifying Lynchian categories.
Place Name and Code Number of Photos Prediction Class Probability Lynchian Category
Altair Residential
Condominium (ARC) 25 Skyscraper 51.98% Landmark
Arcade Independence Square
(AIS) 30 Mosque/Outdoor 31.34% Landmark
Beddagana Wetland Park
(BWP) 28 Broadwalk 72.48% District
Beira Lake (BL) 30 Canal 42.81% Edge
Bellanvila Park (BP) 28 Park 32.46% District
Bandaranaike Memorial
International Conference Hall
(BMICH)
30 Elevator lobby 22.94% Landmark
Borella Cemetery (BC) 25 Cemetery 75.40% Landmark
Colombo City Center (CCC) 30 Department Store 47.91% Landmark
Diyatha Uyana (DU) 25 Park 25.36% District
Colombo Fort (CF) 25 Library 21.52% District
Galle Face (GF) 30 Ocean 22.62% Edge
Galle Face Hotel (GFH) 30 Ballroom 30.78% Landmark
Gangaramaya Temple (GT) 25 Temple 40.98% Landmark
Hilton Hotel (HH) 28 Hotel 42.35% Landmark
Kelaniya Temple (KT) 15 Temple 57.16% Landmark
Kingsbury Hotel (KH) 30 Hotel/Outdoor 45.87 Landmark
Liberty Plaza Building (LPB) 30 Department Store 36.96% Landmark
Lotus Tower (LT) 30 Tower 42.21% Landmark
Mount Lavinia Beach (MLB) 30 Beach 27.41% Edge
One Galle Face Building
(OGFB) 30 Skyscraper 33.44% Landmark
Parliament (P) 15 Legislative Chamber 59.2% Landmark
Pettah Market (PM) 28 Bazaar 43.13% District
Port City (PC) 30 Harbor 28.07% District
Savoy Cinema (SC) 25 Movie Theatre 32.53% Landmark
Shangri La Hotel (SLH) 30 Hotel 45.64% Landmark
Viharamahadevi Park (VP) 30 Park 55.60% District
World Trade Center (WTC) 25 Skyscraper 60.06% Landmark
Zoo (Zoo) 25 Aquarium 31.99% District
Total 762
As of Table 1, from the identified Lynchian categories, 71% of places were identified as
landmarks, 18% were Districts, and 11% were Edges. No Paths and Nodes were identified.
The distribution of the aforesaid place names according to the Lynchian category is given
in Figure 4.

Future Internet 2023,15, 32 11 of 21
Future Internet 2023, 15, x FOR PEER REVIEW 12 of 22
Figure 4. Distribution of place names according to the Lynchian category.
4.3. Sentiment Analysis
To proceed with the sentiment analysis, a bag of 720 words (482 words that give a
positive meaning, i.e., good, happy; 238 words that give a negative meaning, i.e., unpleas-
ant, dirty, garbage) were identified from the tweets. Table 2 represents the ‘Place Names’,
‘Lynchian Category’ derived from the image processing exercise, temporal distribution of
the derived ‘Sentiment Category–Positive/Negative’– per each place name and the posi-
tive sentiment percentages, and ‘Composite Percentage values of Positive Sentiments’ per
each place name.
Table 2. Sentiment classification of tweets.
Place Name and Code Lynchian
Category
2015/16 2017/18 2019/20 C%P
P N T % P P N T % P P N T %P
Altair Residential Condomin-
ium (ARC) Landmark 2 1 3 66.67
% 40 3 43 93.02
% 7 0 7
100.
00
%
86.56%
Arcade Independence Square
(AIS) Landmark 16 0 16 100.0
0% 20 4 24 83.33
% 21 7 28 75.0
0% 86.11%
Beddagana Wetland Park
(BWP) District 15 0 15
100.0
0% 22 0 22 100.0
0% 7 1 8 87.5
0% 95.83%
Figure 4. Distribution of place names according to the Lynchian category.
4.3. Sentiment Analysis
To proceed with the sentiment analysis, a bag of 720 words (482 words that give a
positive meaning, i.e., good, happy; 238 words that give a negative meaning, i.e., unpleasant,
dirty, garbage) were identified from the tweets. Table 2represents the ‘Place Names’,
‘Lynchian Category’ derived from the image processing exercise, temporal distribution of
the derived ‘Sentiment Category–Positive/Negative’– per each place name and the positive
sentiment percentages, and ‘Composite Percentage values of Positive Sentiments’ per each
place name.

Future Internet 2023,15, 32 12 of 21
Table 2. Sentiment classification of tweets.
Place Name and Code Lynchian
Category
2015/16 2017/18 2019/20 C%P
P N T % P P N T % P P N T % P
Altair Residential Condominium
(ARC) Landmark 2 1 3 66.67% 40 3 43 93.02% 7 0 7 100.00% 86.56%
Arcade Independence Square (AIS) Landmark 16 0 16 100.00% 20 4 24 83.33% 21 7 28 75.00% 86.11%
Beddagana Wetland Park (BWP) District 15 0 15 100.00% 22 0 22 100.00% 7 1 8 87.50% 95.83%
Beira Lake (BL) Edge 12 7 19 63.15% 13 7 20 65% 11 6 17 64.75% 64.30%
Bellanvila Park (BP) District 122 1 123 99.19% 47 2 49 95.92% 20 8 28 71.43% 88.85%
Bandaranaike Memorial International
Conference Hall (BMICH) Landmark 268 10 278 96.40% 80 7 87 91.95% 59 7 66 89.39% 92.58%
Borella Cemetery (BC) Landmark 13 3 16 81.25% 2 1 3 66.67% 4 1 5 80.00% 75.97%
Colombo City Center (CCC) Landmark 8 1 9 88.89% 13 1 14 92.86% 25 0 25 100.00% 93.92%
Colombo Fort (CF) District 202 31 233 86.70% 183 21 204 89.71% 101 12 113 89.38% 88.60%
Diyatha Park (DP) District 5 0 5 100.00% 6 2 8 75.00% 4 2 6 66.67% 80.56%
Galle Face (GF) Edge 106 37 143 74.13% 128 15 143 89.51% 256 13 269 95.17% 86.27%
Galle Face Hotel (GFH) Landmark 169 4 173 97.69% 257 19 276 93.12% 153 11 164 93.29% 94.70%
Gangaramaya Temple (GT) Landmark 225 14 239 94.14% 168 11 179 93.85% 24 1 25 96.00% 94.66%
Hilton Hotel (HH) Landmark 122 18 140 87.14% 164 16 180 91.11% 41 5 46 89.13% 89.13%
Kelaniya Temple (KT) Landmark 34 7 41 82.93% 22 7 29 75.86% 39 8 47 82.98% 80.59%
Kingsbury Hotel (KH) Landmark 322 19 341 94.43% 217 12 229 94.76% 314 11 325 96.62% 95.27%
Liberty Plaza Building (LPB) Landmark 118 2 120 98.33% 298 3 301 99.00% 41 14 55 74.55% 90.63%
Lotus Tower (LT) Landmark 102 25 135 75.55% 135 17 152 88.81% 100 16 116 86.2% 83.52%
Mount Lavinia Beach (MLB) Edge 78 5 83 93.98% 65 7 72 90.28% 36 2 38 94.74% 93.00%
One Galle Face Building (OGFB) Landmark 0 0 0 N/A 0 0 0 N/A 77 6 83 92.77% 30.92%
Parliament (P) Landmark 14 7 21 66.66% 6 3 9 66.7% 3 2 5 60.00% 64.45%
Pettah Market (PM) District 218 80 298 73.15% 130 23 153 84.97% 27 8 35 77.14% 78.42%
Port City (PC) District 22 6 28 78.57% 47 12 59 79.66% 28 13 41 68.28% 75.50%
Savoy Cinema (SC) Landmark 261 7 268 97.39% 108 3 111 97.30% 15 1 16 93.75% 96.15%
Shangri La Hotel (SLH) Landmark 0 0 0 N/A 24 3 27 88.89% 38 3 41 92.68% 60.52%
Viharamahadevi Park (VP) District 2 0 2 100.00% 3 1 4 75.00% 2 1 3 66.67% 80.56%
World Trade Center (WTC) Landmark 3 3 100.00% 7 0 7 100.00% 9 2 11 81.82% 93.94%
Zoo (Zoo) District 5 1 6 83.33% 3 2 5 60.00% 8 5 13 61.54% 68.29%
Total 2758 2410 1637
Note: P: Positive|N: Negative|T: Total|% P: Percentage of Positively classified Tweets|C%P: Composite Percentage values of Positive sentiments.

Future Internet 2023,15, 32 13 of 21
Using Table 2, Figure 5a–c was mapped based on the distribution of the percentages
of positively classified tweets per place over the three-time lapse considered—2015/2016,
2017/2018/2019/2020.
Future Internet 2023, 15, x FOR PEER REVIEW 14 of 22
Viharamahadevi Park (VP) District 2 0 2 100.0
0% 3 1 4 75.00
% 2 1 3 66.6
7% 80.56%
World Trade Center (WTC) Landmark 3 3 100.0
0% 7 0 7 100.0
0% 9 2 11 81.8
2% 93.94%
Zoo (Zoo) District 5 1 6 83.33
% 3 2 5 60.00
% 8 5 13 61.5
4% 68.29%
Total 2758 2410 1637
Note: P: Positive|N: Negative|T: Total|% P: Percentage of Positively classified Tweets|C%P: Com-
posite Percentage values of Positive sentiments.
Using Table 2, Figure 5a–c was mapped based on the distribution of the percentages
of positively classified tweets per place over the three-time lapse considered—2015/2016,
2017/2018/2019/2020.
(a)
(b)
(c)
Figure 5. Sentiment analysis with distribution of positively classified tweets: (a) sentiment analysis
2015–2016; (b) sentiment analysis 2017–2018; (c) sentiment analysis 2019–2020.
As of Figure 5a from 2015 to 2016, VP (District), AIS (Landmark), BWP (District), BL
(Edge), DP (District), and WTC (Landmark) have received 100% positive comments from
analyzed social media messages. BP (District; 99.19%), LPB (Landmark; 98.33%), GFH
(Landmark; 97.69%), SC (Landmark; 97.39%), BMICH (Landmark; 94.4%), KH (Land-
mark; 94.43%), GT (Landmark; 94.14%), and MLB (Edge; 93.98%) were the other places
which received over 90% of positive perceptions from the analyzed social media mes-
sages.
Nonetheless, apart from BWP and the WTC, the positive sentiments shared for other
landmarks have significantly reduced from 2017 to 2018. For instance, AR, DP, and VP,
which received totally positive (100%) tweets and Instagram posts between 2015 and 2016,
received 83.33%, 75%, and 75% positive perceptions, respectively. Most significantly, all
the other places which received over 90% of positive sentiments between the years 2015
and 2016 were either received lower, i.e., BP—95.92%, GFH—93.12%, BMICH—91.95%,
GT—93.85%, and MLB—90.28%, or with a slight increment, i.e., LPB—99% and KH—
94.76% from 2017 to 2018. Further, CCC and HH have moved up to the category with over
90% positive sentiments by 2017 and 2018.
Commented [JYH?1]: We moved Figure 5 after its
first citation. Please confirm.
Commented [TY2R1]: Ok
Figure 5.
Sentiment analysis with distribution of positively classified tweets: (
a
) sentiment analysis
2015–2016; (b) sentiment analysis 2017–2018; (c) sentiment analysis 2019–2020.
As of Figure 5a from 2015 to 2016, VP (District), AIS (Landmark), BWP (District), BL
(Edge), DP (District), and WTC (Landmark) have received 100% positive comments from
analyzed social media messages. BP (District; 99.19%), LPB (Landmark; 98.33%), GFH
(Landmark; 97.69%), SC (Landmark; 97.39%), BMICH (Landmark; 94.4%), KH (Landmark;
94.43%), GT (Landmark; 94.14%), and MLB (Edge; 93.98%) were the other places which
received over 90% of positive perceptions from the analyzed social media messages.
Nonetheless, apart from BWP and the WTC, the positive sentiments shared for other
landmarks have significantly reduced from 2017 to 2018. For instance, AR, DP, and VP,
which received totally positive (100%) tweets and Instagram posts between 2015 and 2016,
received 83.33%, 75%, and 75% positive perceptions, respectively. Most significantly, all the
other places which received over 90% of positive sentiments between the years 2015 and
2016 were either received lower, i.e., BP—95.92%, GFH—93.12%, BMICH—91.95%, GT—
93.85%, and MLB—90.28%, or with a slight increment, i.e., LPB—99% and KH—94.76%
from 2017 to 2018. Further, CCC and HH have moved up to the category with over 90%
positive sentiments by 2017 and 2018.
When compared to the 2015–2016 and 2017–2018 categories, percentages of positively
classified tweets have significantly declined in 2019–2020. For instance, BP has significantly
lost the positive perceptions received around 2015 (99.19%) compared to 2019–2020 (71.43%).
Similarly, LPB, ARC, DP, and VP experienced the same scenario. This is mainly due to the
commencement of many city-beautification projects such as the AIS renovation project and
DP construction projects around 2012. However, such projects lost community attractions
due to low maintenance, the existence of expensive shops, and so on. In contrast, CCC,
which received comparatively low positive perceptions around 2016 (88.89%) compared
to other landmarks, has increased up to 100% around 2019 and 2020. CCC is a 47-story
mixed-use development that opened in 2018. According to the analyzed social media
data, CCC is so far perceived positively as a landmark due to its building architecture and
facilities. Exemplary tweets posted sharing positive sentiments about ARC and DP are
shown in Figure 6.

Future Internet 2023,15, 32 14 of 21
Future Internet 2023, 15, x FOR PEER REVIEW 15 of 22
When compared to the 2015–2016 and 2017–2018 categories, percentages of positively
classified tweets have significantly declined in 2019–2020. For instance, BP has signifi-
cantly lost the positive perceptions received around 2015 (99.19%) compared to 2019–2020
(71.43%). Similarly, LPB, ARC, DP, and VP experienced the same scenario. This is mainly
due to the commencement of many city-beautification projects such as the AIS renovation
project and DP construction projects around 2012. However, such projects lost community
attractions due to low maintenance, the existence of expensive shops, and so on. In con-
trast, CCC, which received comparatively low positive perceptions around 2016 (88.89%)
compared to other landmarks, has increased up to 100% around 2019 and 2020. CCC is a
47-story mixed-use development that opened in 2018. According to the analyzed social
media data, CCC is so far perceived positively as a landmark due to its building architec-
ture and facilities. Exemplary tweets posted sharing positive sentiments about ARC and
DP are shown in Figure 6.
Figure 6. Exemplary tweets posted sharing positive sentiments about ARC and DP.
4.4. Popularity Analysis
Popularity analysis counted the number of tweets/Instagram messages distributed
per each place name irrespective of the date tweeted. Figure 7 shows the color density
matrix of the popularity analysis.
Figure 6. Exemplary tweets posted sharing positive sentiments about ARC and DP.
4.4. Popularity Analysis
Popularity analysis counted the number of tweets/Instagram messages distributed
per each place name irrespective of the date tweeted. Figure 7shows the color density
matrix of the popularity analysis.
Future Internet 2023, 15, x FOR PEER REVIEW 16 of 22
Figure 7. Color density matrix: Twitter popularity analysis.
The frequency of the posts shared on Twitter and Instagram shows the community
perceptions toward making memories with the places. According to Figure 7, GF, AIS, CF,
KH, HH, LPB, PM, and BMICH can be considered the places which have received high
attention on Twitter and Instagram. Table 3 shows the exemplary positively and nega-
tively classified tweets, where Table 4 lists the frequently used word in the Twitter and
Instagram posts shared per selected locations with increasing popularity.
Table 3. Exemplary positively and negatively classified tweets.
Place
Code
Lynchian Cate-
gory Date Text
Geo-coordi-
nate Sentiment
ARC Landmark
13 March
06:18:32 +0000
2021
Altair Colombo #cmb Sri Lanka
next best thing architecture #Al-
tair
6.91862,
79.8541 Positive
CCC Landmark
23 November
16:13:07 +0000
2021
We do not remember days, we
remember moments 🎏🎎
friends happy Sunday movies
Scope Cinema Gold Class at
CCC 😊🙆👌🎹🎧🍫
6.9176001,
79.85552449 Positive
BWP District
12 January
11:30:35 +0000
2017
Beautiful greenish naturepho-
tography Beddagana Wetland
Park
6.89136398,
79.90899324 Positive
DP District
9 August
14:54:01 +0000
2020
Diyatha Uyana then and now
low maintenance, fading 3D
arts
6.9045,
79.9098 Negative
BL Edge
18 May 03:23:00
+0000 2017
The remote and picturesque
view of Colombo from the his-
toric beiralake
6.93333333,
79.85 Positive
Zoo District
1 February
03:56 00 +0000
2022
Reality of Sri Lanka’s National
Zoological Gardens at Dehi-
wala. Elephants being trained
6.85680556,
79.87288889 Negative
Figure 7. Color density matrix: Twitter popularity analysis.
The frequency of the posts shared on Twitter and Instagram shows the community
perceptions toward making memories with the places. According to Figure 7, GF, AIS, CF,
KH, HH, LPB, PM, and BMICH can be considered the places which have received high

Future Internet 2023,15, 32 15 of 21
attention on Twitter and Instagram. Table 3shows the exemplary positively and negatively
classified tweets, where Table 4lists the frequently used word in the Twitter and Instagram
posts shared per selected locations with increasing popularity.
Table 3. Exemplary positively and negatively classified tweets.
Place Code Lynchian
Category Date Text Geo-coordinate Sentiment
ARC Landmark 13 March 06:18:32
+0000 2021
Altair Colombo #cmb Sri Lanka
next best thing architecture
#Altair
6.91862, 79.8541 Positive
CCC Landmark 23 November
16:13:07 +0000 2021
We do not remember days, we
remember moments
Future Internet 2023, 15, x FOR PEER REVIEW 16 of 22
Figure 7. Color density matrix: Twitter popularity analysis.
The frequency of the posts shared on Twitter and Instagram shows the community
perceptions toward making memories with the places. According to Figure 7, GF, AIS, CF,
KH, HH, LPB, PM, and BMICH can be considered the places which have received high
attention on Twitter and Instagram. Table 3 shows the exemplary positively and nega-
tively classified tweets, where Table 4 lists the frequently used word in the Twitter and
Instagram posts shared per selected locations with increasing popularity.
Table 3. Exemplary positively and negatively classified tweets.
Place
Code
Lynchian Cate-
gory Date Text
Geo-coordi-
nate Sentiment
ARC Landmark
13 March
06:18:32 +0000
2021
Altair Colombo #cmb Sri Lanka
next best thing architecture #Al-
tair
6.91862,
79.8541 Positive
CCC Landmark
23 November
16:13:07 +0000
2021
We do not remember days, we
remember moments 🎏🎎
friends happy Sunday movies
Scope Cinema Gold Class at
CCC 😊🙆👌🎹🎧🍫
6.9176001,
79.85552449 Positive
BWP District
12 January
11:30:35 +0000
2017
Beautiful greenish naturepho-
tography Beddagana Wetland
Park
6.89136398,
79.90899324 Positive
DP District
9 August
14:54:01 +0000
2020
Diyatha Uyana then and now
low maintenance, fading 3D
arts
6.9045,
79.9098 Negative
BL Edge
18 May 03:23:00
+0000 2017
The remote and picturesque
view of Colombo from the his-
toric beiralake
6.93333333,
79.85 Positive
Zoo District
1 February
03:56 00 +0000
2022
Reality of Sri Lanka’s National
Zoological Gardens at Dehi-
wala. Elephants being trained
6.85680556,
79.87288889 Negative
friends happy Sunday movies
Scope Cinema Gold Class at
CCC
Future Internet 2023, 15, x FOR PEER REVIEW 16 of 22
Figure 7. Color density matrix: Twitter popularity analysis.
The frequency of the posts shared on Twitter and Instagram shows the community
perceptions toward making memories with the places. According to Figure 7, GF, AIS, CF,
KH, HH, LPB, PM, and BMICH can be considered the places which have received high
attention on Twitter and Instagram. Table 3 shows the exemplary positively and nega-
tively classified tweets, where Table 4 lists the frequently used word in the Twitter and
Instagram posts shared per selected locations with increasing popularity.
Table 3. Exemplary positively and negatively classified tweets.
Place
Code
Lynchian Cate-
gory Date Text
Geo-coordi-
nate Sentiment
ARC Landmark
13 March
06:18:32 +0000
2021
Altair Colombo #cmb Sri Lanka
next best thing architecture #Al-
tair
6.91862,
79.8541 Positive
CCC Landmark
23 November
16:13:07 +0000
2021
We do not remember days, we
remember moments 🎏🎎
friends happy Sunday movies
Scope Cinema Gold Class at
CCC 😊🙆👌🎹🎧🍫
6.9176001,
79.85552449 Positive
BWP District
12 January
11:30:35 +0000
2017
Beautiful greenish naturepho-
tography Beddagana Wetland
Park
6.89136398,
79.90899324 Positive
DP District
9 August
14:54:01 +0000
2020
Diyatha Uyana then and now
low maintenance, fading 3D
arts
6.9045,
79.9098 Negative
BL Edge
18 May 03:23:00
+0000 2017
The remote and picturesque
view of Colombo from the his-
toric beiralake
6.93333333,
79.85 Positive
Zoo District
1 February
03:56 00 +0000
2022
Reality of Sri Lanka’s National
Zoological Gardens at Dehi-
wala. Elephants being trained
6.85680556,
79.87288889 Negative
6.9176001,
79.85552449 Positive
BWP District
12 January 11:30:35
+0000 2017
Beautiful greenish
naturephotography Beddagana
Wetland Park
6.89136398,
79.90899324 Positive
DP District 9 August 14:54:01
+0000 2020
Diyatha Uyana then and now
low maintenance, fading 3D arts
6.9045, 79.9098 Negative
BL Edge 18 May 03:23:00
+0000 2017
The remote and picturesque
view of Colombo from the
historic beiralake
6.93333333, 79.85 Positive
Zoo District
1 February 03:56 00
+0000 2022
Reality of Sri Lanka’s National
Zoological Gardens at Dehiwala.
Elephants being trained for any
performance is cruel. This is
slavery. #DehiwalaZooCruelty
#SayNoToCaptivity
#CaptivityIsCruel
#SayNoToElephantSlavery
#DehiwalaZoo #ShermilaOut
#ElephantAbuse
6.85680556,
79.87288889 Negative
Table 4. Frequently used words attached to selected Lynchian elements.
Place Code Frequently Used Words
GF (Landmark) Photo (39), sunset (35), beautiful (14), time (14), night (11), view (11), evening (11)
AIS (Landmark) Time (24), night (23), evening (17), love (15), selfie (15), life (14), friends (13), good (12), architecture (11),
lounge (11), tea (10)
CF (District) Station (278), railway (272), café(34), train (18), Dutch (13)
KH (Landmark) Sky (32), night (30), love (21), view (21), time (19), good (18), dinner (16), life (14), party (14), sunset (14),
family (13), happy (13), harbor (13), good times (12), evening (11), travel (10)
HH (Landmark)
Photo (125), night (57), dinner (51), ballroom (41), good (29), Christmas (24), life (21), party (21), happy (19),
poolside (17), love (15), #beautiful (14), beautiful (14), graze (14), thank (14), #love (13), #oktoberfest (13),
#weddings (13), best (13), grand (13), view (13), #family (12), #life (12), #travel (12), tower (11), video (11),
MLB (Edge) Beach (53), hotel (27), sunset (21), family (15), #mountleviniabeach (15), #mountlaviniahotel (15), sea (10)
Landmarks such as GF and AIS attract people due to their location and building
architecture. KH and HH are two prestigious hotels located facing the main road of
Colombo 01. Especially, OGFB (Landmark), DP (District), and MLB (Edge) have gained
more popularity in recent years compared to 2015. Nonetheless, places such as BL (Edge),
LT (Landmark), and P (Landmark) have gradually lost popularity. Figure 8demonstrate
the sceneries created by the KH and the MLB, and Figure 9shows temporal changes of the
sentiment values by zones.

Future Internet 2023,15, 32 16 of 21
Future Internet 2023, 15, x FOR PEER REVIEW 17 of 22
for any performance is cruel.
This is slavery. #Dehiwala-
ZooCruelty #SayNoToCaptivity
#CaptivityIsCruel #SayNoTo-
ElephantSlavery #DehiwalaZoo
#ShermilaOut #ElephantAbuse
Table 4. Frequently used words attached to selected Lynchian elements.
Place Code Frequently Used Words
GF (Landmark) Photo (39), sunset (35), beautiful (14), time (14), night (11), view (11), evening (11)
AIS (Landmark) Time (24), night (23), evening (17), love (15), selfie (15), life (14), friends (13), good (12), archi-
tecture (11), lounge (11), tea (10)
CF (District) Station (278), railway (272), café (34), train (18), Dutch (13)
KH (Landmark) Sky (32), night (30), love (21), view (21), time (19), good (18), dinner (16), life (14), party (14),
sunset (14), family (13), happy (13), harbor (13), good times (12), evening (11), travel (10)
HH (Landmark)
Photo (125), night (57), dinner (51), ballroom (41), good (29), Christmas (24), life (21), party
(21), happy (19), poolside (17), love (15), #beautiful (14), beautiful (14), graze (14), thank (14),
#love (13), #oktoberfest (13), #weddings (13), best (13), grand (13), view (13), #family (12), #life
(12), #travel (12), tower (11), video (11),
MLB (Edge) Beach (53), hotel (27), sunset (21), family (15), #mountleviniabeach (15), #mountlaviniahotel
(15), sea (10)
Landmarks such as GF and AIS attract people due to their location and building ar-
chitecture. KH and HH are two prestigious hotels located facing the main road of Co-
lombo 01. Especially, OGFB (Landmark), DP (District), and MLB (Edge) have gained more
popularity in recent years compared to 2015. Nonetheless, places such as BL (Edge), LT
(Landmark), and P (Landmark) have gradually lost popularity. Figure 8 demonstrate the
sceneries created by the KH and the MLB, and Figure 9 shows temporal changes of the
sentiment values by zones.
(a)
(b)
Figure 8. Sceneries of: (a) KH shared on Instagram; (b) MLB shared on Twitter.
Figure 8. Sceneries of: (a) KH shared on Instagram; (b) MLB shared on Twitter.
Future Internet 2023, 15, x FOR PEER REVIEW 18 of 22
Figure 9. Temporal changes of the sentiment values by zones.
5. Discussion
The image of a city is a cognitive representation of space that emerges from cognitive
processes that strongly interact with the community’s perceptions and observations of a
city [68–70]. Mostly, city images were evaluated through small data using mental maps,
walking tours, interviews, questionnaires, and sketch maps [71,72]. Still, the study find-
ings revealed that the use of social media data act as a promising data source to be used
to examine city images in detail with more facts in this digital age.
Supported by the descriptive analysis and the image processing exercise, this study
conducted two main analyses—sentiment and popularity analyses. Although both anal-
yses reflected the positive perceptions borne by the public, the temporal changes of the
community’s sentiments have changed significantly with the emergence of new land-
marks, paths, nodes, edges, and districts. For further discussion, the case study area was
divided into three hypothetical zones (Figure 9)—Zones A, B, and C—to better under-
stand the temporal changes of community sentiments.
Zone A consists of the highest number of Lynchian elements compared to Zone B
and C each year. For instance, from 2015 to 2016, 45% (10 out of 22) of the total places
identified were from Zone A. This number continuously and gradually increased from
2017 to 2018 to 48% (12 out of 24). Within the period of 2019–2020, the total Lynchian
elements identified were 57% (16 out of 28). Especially, most of the highly perceived Land-
marks, such as CF, GF, and PM, were in Zone A. Even the newly emerged Lynchian ele-
ments of ARC (Landmark), CCC (Landmark), and OGFB (Landmark) were from Zone A.
Zone B has the second-highest number of Lynchian elements. Unlike Zone A, the
number of Lynchian elements (n = 9) has not changed since 2015 in Zone B. Zone C has
the lowest number of Lynchian elements (n = 9). Like Zone C, the number of Lynchian
elements in Zone C have also not changed over time.
Additionally, the popularity is agglomerate in this respected area. This can be iden-
tified as a major concern when considering the whole city’s image. As of Figure 9, Zone A
is enriched with many Lynchian elements—GF, CF, and KH, with an increasing number
of tweets over the years. This reflected that the community has perceived such Lynchian
elements with increasing popularity in a positive manner.
Figure 9. Temporal changes of the sentiment values by zones.
5. Discussion
The image of a city is a cognitive representation of space that emerges from cognitive
processes that strongly interact with the community’s perceptions and observations of a
city [
68
–
70
]. Mostly, city images were evaluated through small data using mental maps,
walking tours, interviews, questionnaires, and sketch maps [
71
,
72
]. Still, the study findings
revealed that the use of social media data act as a promising data source to be used to
examine city images in detail with more facts in this digital age.
Supported by the descriptive analysis and the image processing exercise, this study
conducted two main analyses—sentiment and popularity analyses. Although both anal-
yses reflected the positive perceptions borne by the public, the temporal changes of the
community’s sentiments have changed significantly with the emergence of new landmarks,
paths, nodes, edges, and districts. For further discussion, the case study area was divided
into three hypothetical zones (Figure 9)—Zones A, B, and C—to better understand the
temporal changes of community sentiments.

Future Internet 2023,15, 32 17 of 21
Zone A consists of the highest number of Lynchian elements compared to Zone B
and C each year. For instance, from 2015 to 2016, 45% (10 out of 22) of the total places
identified were from Zone A. This number continuously and gradually increased from 2017
to 2018 to 48% (12 out of 24). Within the period of 2019–2020, the total Lynchian elements
identified were 57% (16 out of 28). Especially, most of the highly perceived Landmarks,
such as CF, GF, and PM, were in Zone A. Even the newly emerged Lynchian elements of
ARC (Landmark), CCC (Landmark), and OGFB (Landmark) were from Zone A.
Zone B has the second-highest number of Lynchian elements. Unlike Zone A, the
number of Lynchian elements (n = 9) has not changed since 2015 in Zone B. Zone C has
the lowest number of Lynchian elements (n = 9). Like Zone C, the number of Lynchian
elements in Zone C have also not changed over time.
Additionally, the popularity is agglomerate in this respected area. This can be identi-
fied as a major concern when considering the whole city’s image. As of Figure 9, Zone A
is enriched with many Lynchian elements—GF, CF, and KH, with an increasing number
of tweets over the years. This reflected that the community has perceived such Lynchian
elements with increasing popularity in a positive manner.
This has created an agglomeration of Lynchian elements into Zone A, while other
Zones remain unchanged in quantity. For better utilization, the city elements should be
evenly distributed along the city and, hence, these ‘Poles’ must be avoided. The advantages
of well-distributed Lynchian elements, especially with positive community perceptions,
are much higher than proximity spatially concentrated Lynchian elements. Additionally, it
can be observed that the city branding elements like ‘Lotus Tower’ are adding advantages
to the city’s popularity. To achieve an evenly distributed city image throughout the area,
adding these branding elements can be an option to consider.
For new forms of territorial governance, the objective of place branding is no more
limited only to economic gains but also to the development of a positive image of the
place that facilitates a sense of place and satisfies the potential desires and needs of the
public [
73
]. In the purview of place branding, the socio–cultural and anthropological
differences contribute to highlighting as well as promoting uniqueness as a tool for place
branding [
74
]. In recent years, there has been a growing consensus amongst scholars that
considers the objective of place branding in terms of linking place image to aspects of place
identity and developing a sense of pride and belonging within the spatio–temporal global
context [75,76].
6. Conclusions
The image of a city changes over time due to social, environmental, economic, and
political changes that take place in an urban environment [
77
,
78
]. According to [
79
,
80
],
social media plays a key role in place marketing activities. Therefore, analyzing and
understating the changing city image is important as it affects a city in different aspects. For
instance, a popular and positively perceived city image attracts tourists and investors to a
city, which could ultimately lead to branding the cities locally and internationally [81–83].
As this study emphasized, in this digital era, researchers or policymakers do not have
to adopt time-consuming methodologies to examine the city image. Instead, the use of
social media messages with or without images and emojis act as an effective and accurate
method to understand community perceptions of the city’s image.
Especially, by examining the sentiment or the emotional values hidden in the tweets
and the images shared, the study was able to emphasize the validity of using community-
generated social media messages to examine the city image often. This study suggested
the lasting value of the city image theory amidst the prevalence of novel technologies
such as social media, hand-held mobile devices, and Google Maps. Further technological
innovation will also help in the accuracy and ease of undertaking social media big data
analytics and planning accordingly for smarter urban environments [84–86].
Lastly, the following limitations of this study should be considered when interpreting
the findings. This study only used open API to access the Twittersphere which provides

Future Internet 2023,15, 32 18 of 21
limited access to the Twitter database. Secondly, this study did not conduct a ground survey
to validate its findings through social media analytics, which further researchers can be
focused on. Further, this study did not conduct an analysis to investigate the differences
between the results obtained via Twitter and those obtained via Instagram, which future
studies can extend on. Additionally, these research findings create a new platform for more
research that needs to understand the use of social media data toward city branding.
Author Contributions:
Conceptualization, S.A. and N.K.; Methodology, S.A.; Software, N.K.; Val-
idation, N.K.; Formal analysis, S.A.; Investigation, S.A.; Resources, N.K. and S.A.; Data curation,
S.A.; Writing—original draft preparation, S.A.; Writing—review and editing, N.K., T.Y. and S.P.;
Supervision, N.K. and T.Y.; Project administration, N.K. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research did not receive any specific grant from funding agencies in the public,
commercial or not-for-profit sectors.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors thank the managing editor and anonymous referees for their con-
structive comments on an earlier version of this paper.
Conflicts of Interest:
The authors declare no known competing financial interest or personal rela-
tionship that could have appeared to influence the study reported.
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