=Paper=
{{Paper
|id=Vol-2305/paper07
|storemode=property
|title=Entrepreneurial oriented discussions in smart cities: Perspectives driven from systematic use of social network services data
|pdfUrl=https://ceur-ws.org/Vol-2305/paper07.pdf
|volume=Vol-2305
|authors=Arash Hajikhani,Marja Turunen,Matti Mäntymäki,Andrey Saltan,Ahmed Seffah,Zeena Spijkerman,Slinger Jansen,Teppo Yrjönkoski,Felix Schönhofen,Sixten Schockert,Georg Herzwurm,Andrey Saltan,Slinger Jansen,Kari Smolander,Jan Bosch,Helena H. Olsson,Ivica Crnkovic,Jorge Melegati,Xiaofeng Wang,Jürgen Münch,Stefan Trieflinger,Dominic Lang,Rafael Chanin,Dron Khanna,Kai-Kristian Kemell,Wang Xiaofeng,Afonso Sales,Rafael Prikladnicki,Pekka Abrahamsson,Katariina Yrjönkoski,Anu Suominen,Matthias Gutbrod,Jürgen Münch
|dblpUrl=https://dblp.org/rec/conf/sibw/Hajikhani18
}}
==Entrepreneurial oriented discussions in smart cities: Perspectives driven from systematic use of social network services data==
https://ceur-ws.org/Vol-2305/paper07.pdf
SiBW 2018 89
Entrepreneurial Oriented Discussions in Smart Cities:
Perspectives Driven from Systematic Use of
Social Network Services Data
Arash Hajikhani
Innovations, Economy, and Policy, VTT Technical Research Centre of Finland
arash.hajikhani@vtt.fi
Abstract. The concept of the “smart city” has become popular in scientific liter-
ature and international policies in the past two decades. Smart cities are known
as a system of physical infrastructure, the ICT infrastructure and the social infra-
structure exchanging information that flow between its many different subsys-
tems. The “smart cities” concept has been introduced with various dimensions
among those, the embedded ICT infrastructure in smart cities is playing a deci-
sive role among the functions of the system. One of the important derivatives of
ICT is the new communication mediums known as Social Network Services
(SNSs) which is emerging and introducing additional functionalities to “smart
cities”. This paper seeks to advance the understanding of SNSs in smart cities for
evaluating the effects on the innovation and entrepreneurial ecosystem. This
agenda has been tackled by a rigorous methodological approach in order to cap-
ture and evaluate the presence of entrepreneurial oriented discussion in a popular
SNSs medium (Twitter).
Keywords: Smart Cities, Social Network Services, Start-ups, Content Analysis.
1 Introduction
Population growth and the urbanization associated to that are recognized as the con-
temporary challenges that seeks novel, efficient, effective, and economic approaches to
better governance. Challenges for developing the infrastructures and services needed
to be addressed so to increase communities living standards. The emergence of the
“smart city” concept can be considered as a response to such challenges ensuring that
cities can develop economically, whilst protecting the environment and quality of life
for citizens. Smart technologies is offering cities exciting possibilities for the provision
of new services and integrated city infrastructures, as well as supporting innovation,
digital entrepreneurship, and sustainable city development [10]. According to World
Economic Forum [47], a growing number of cities around the world are implementing
ambitious smart city programs and projects across a range of themes including govern-
ance, local economic development, citizen participation, urban living, the natural and
built environment, and sustainable transport.
SiBW 2018 90
An in-depth analysis of the existing literature revealed that the smart city is a multi-
faceted concept with many elements and dimensions. Descriptions of smart cities are
now including qualities of people and communities as well as ICTs. The smart cities
are known as a system of physical infrastructure, the ICT infrastructure, and the social
infrastructure exchanging information that flows between its many different subsys-
tems [2]. It might even be noticeable that major cities can serve as a good representation
of a nation’s economic success or failure. According to Beattie [4] that’s because the
tricky business of development and urbanization can play a big role in a country’s eco-
nomic prosperity. Entrepreneurship and innovation is the major concern for an econ-
omy consequently within the boundary of a city therefore, the competitiveness of a city
today is determined by its innovativeness and economic strength [3]. While researchers
have realized that smart cities are more entrepreneurial than others [28,34], an analysis
of the detailed characteristics accounting for this higher entrepreneurial activity within
smart cities has not been conducted.
One of the major resources connected to the success of smart cities is the societal
capital or cultural capital within the city boundaries. The emphasis on the role of social
capital in urban development is promoted in parallel to technical aspects of a city [25].
The importance of human and social capital has been recognized by smart city defini-
tions from previous literature, and it has been seen as a fundamental aspect of any smart
city [2,11,27,40]. Social capital has also been seen as an important dimension for facil-
itation of innovation and entrepreneurship in smart cities. Smart cities have the infra-
structure to bridge and facilitate the connectivity of society for entrepreneurial activity.
Despite the recognition of the importance of the human and social capital aspect in
smart cities, the measurement and assessment of this aspect has remained a challenge.
Performance measurement studies on smart cities dimensions, especially on social and
human capital, are subject to being outcome indicators that, by their nature, involve
medium- to long-term observation and detection times [30]. The results of this issue are
the lack of insight coming from society and incapability to absorb the information com-
ing from society.
In this research, the attempt is to study the smart city social and human capital per-
formance measurement concerning innovation and entrepreneurship oriented activity.
Due to ICT advancements, smart cities have the infrastructure to bridge and facilitate
the connectivity of society. Within the broad spectrum of ICT application, the emerging
presence of the mass media communications such as Social Network Services (SNSs)
and social media has not been taken into account for studying innovation and entrepre-
neurship ecosystem in smart cities. Publicly available data sources such as Twitter have
facilitated massive data collection which can leverage the research at intersection of
social sciences, data sciences, and indicator design, thus informing the research com-
munity of major opinions and topics of interest among the general population [45,48]
that cannot otherwise be collected through traditional means of research (e.g., surveys,
interviews, focus groups) [17]. On the other hand, citizens are empowered to use tech-
nology oriented common platform to communicate among themselves, which resulted
in inclusive use of social network services among citizens. Yet despite this interest,
there seems to be very limited understanding of what the “social networking services”
or “social media” exactly represent and do to societies. In our presented case, we saw
SiBW 2018 91
social media discussion as a curtail pillar in regulating entrepreneurial oriented discus-
sions in smart cities. Therefore, this paper explores the social network services role in
smart cities from the innovation and entrepreneurial ecosystem vantage point. We aim
to address the following research questions:
─ How can smart cities leverage the presence of SNSs for entrepreneurial oriented ac-
tivities in innovation ecosystem?
─ Utilize social network services data to identify the presence of impactful entrepre-
neurial discussion (a methodological approach).
This agenda has been tackled by a rigorous methodological approach in order to
capture and evaluate the presence of entrepreneurial oriented discussion in a popular
SNSs outlet (Twitter). A thorough process of detecting and capturing relevant tweets
was performed to evaluate the usage of SNSs in promoting innovation and entrepre-
neurial oriented discussions. Based on the recognized Smart City Index, London city
has been selected to utilize the methods for capturing social capital on innovation and
entrepreneurial activity.
2 What are smart cities?
Cities are considered as key role players in social and economic aspects in global per-
spectives, and therefore in order to understand the importance of cities as future key
elements, the definitions of “smart cities” will be explored in this section. United Na-
tions Population Fund indicates that in the year 2008 about 3.3 billion people, which is
more than 50 percent of global population, lived in urban areas. This estimation is ex-
pected to increase to 70 percent by 2050 according to a United Nations report [44]. The
urbanization figure in Europe is currently 75 percent of the population and the number
is expected to reach 80 percent by 2020 [44].
The advantage point of smart cities as a structure to enable the pre mentioned move-
ments has been seen on the opportunity for information exchange that flows between
its many different subsystems [20]. A comprehensive definition of smart cities by
Nijkamp and Kourtit [33] “Smart cities are the result of knowledge-intensive and crea-
tive strategies aiming at enhancing the socio-economic, ecological, logistic and com-
petitive performance of cities. Such smart cities are based on a promising mix of human
capital (e.g. skilled labor force), infrastructural capital (e.g. high-tech communication
facilities), social capital (e.g. intense and open network linkages), and entrepreneurial
capital (e.g. creative and risk-taking business activities)”. Hence, a recent classification
by Neirotti et al. [32], define two major domains for the smart city concept with regard
to the exploitation of tangible and intangible urban assets: (1) hard domain, which con-
cerns energy, lighting, environment, transportation, buildings, and health care and
safety issues and (2) soft domain, which addresses education, society, government, and
economy. Shapiro [36] and Holland [24] argue over soft domain aspect of smart cities
such as human capital rather that hard domain aspects like ICT; as the driver of smart
city creation. According to Caragliu et al. [11] a city is smart “when investments in
human and social capital and traditional (transport) and modern (ICT) communication
SiBW 2018 92
infrastructure fuel sustainable economic growth and a high quality of life, with a wise
management of natural resources, through participatory governance” (p. 70). Descrip-
tions of smart cities are now appreciating the soft domain aspects like qualities of peo-
ple and communities as well as ICTs [2,31]. The new perspective that aims to inspire
the sense of community among citizens get insights from the previous bottom-up
knowledge scheme and recognize the importance of factors that emulates the concept
of smart communities where members and institutions work in partnership to transform
their environment [5]. Smart communities makes conscious decisions on technology
use for tackling societal challenges which results not only in the increase of quality life
but also a means to reinventing city’s capabilities for new communal practices [16].
The California Institute for Smart Communities could be exemplify among the first to
focus on how communities could become smart and how a city could be designed to
implement information technologies [1].
The vast range of contexts has led to the formation of a diverse and nebulous smart
city design space, where there is little consensus over what smart cities are and what
form they should take. This inhibits communal discourse and slows down the develop-
ment and widespread deployment of smart city technologies and policies [24]. More
crucially, it is a barrier to citizen engagement and bottom-up design. Communities are
unlikely to engage with, identify, and then design solutions for civic problems while
the smart city concept is incoherent, unapproachable, and hard to measure. The agenda
for this research is to study the bridge between the soft and hard domain aspects of
smart cities and smart communities embedded. On one hand, the hard domain side is
where infrastructures such as ICT have a decisive role in the functions of the smart city.
On the other hand, the term has also been applied to soft domains where approaches
towards culture and social inclusion in a smart city that supposed to offer environments
for an entrepreneurship accessible to all citizens. The taken aspect of the smart cities in
this research concerns ICT provided opportunities such as social network services and
therefore social capital utilization for entrepreneurial oriented activities. Data in social
network services as a communication platform will be utilized to study the content and
discussions on the innovation and entrepreneurship in on smart city while the general
procedure to systematically deal with SNS data will be described. Further, with having
the data analyzed and operationalization of the extracted simplified metrics, we attempt
to investigate the influential content in SNS regarding the innovation and entrepreneur-
ial discussions. Therefore, the conceptual framework for approaching smart cities
within the focus of this research should offer insights regarding the operationalization
of social network services data and the effect magnitude of a content in SNS in the
context of innovation and entrepreneurship discussions.
3 Innovation and Entrepreneurial Ecosystems and the Role of
Social Network Services
Innovation and entrepreneurship concepts are highly intertwined and dependent on each
other and are recognized as the core critical components for the wealth and competi-
tiveness of cities and countries [43]. Innovation is an inherently human endeavor, and
SiBW 2018 93
successful innovation happens when people with skills, experience, and capabilities
come together to understand or predict, and then address existing challenges while en-
trepreneurship is the attempt to setting up and scaling the efforts [15].
Smart cites are introduced as the territories that connects the physical, the IT, the
social, and the business infrastructure to leverage the capability of learning and inno-
vation, which is built-in the collective intelligence of the city and its population [23].
The smart infrastructure of cities can tackle the existing challenges in innovation and
entrepreneurship ecosystems. In particular, the role of ICT services as one of the di-
mensions of smart cities can enhance the innovation and entrepreneurship ecosystem.
Smart cities have the infrastructure to bridge and facilitate the connectivity of society
and in general the social capital for entrepreneurial activity. With the emergence of
social network services in the past decade, a new medium has been created to present
the society that has not gotten the proper attention yet. The social infrastructure, such
as intellectual and social capital, presented by SNSs is an indispensable endowment to
the smart cities as it allows, “connecting people and creating relationships” [2]. ICTs
also offer new avenues for openness by providing access to social media content and
interactions that are created through the social interaction of users via highly accessibly
Web-based technologies.
Social media platforms had significant growth over the last decade. According to
online statistics and market research source Statista [39], over 70 percent of internet
users were social network users in the year 2017 and these figures are expected to grow.
It is estimated that the number of social media users will increase from 2.34 billion in
2016 to 2.95 billion in 2020 [39]. Social networking is one of the most popular online
activities with high user engagement rates and expanding mobile possibilities. The
growth of the SNS’s user base is universal and now been increasingly populated and
used by much diverse age groups [25]. The growth of social network services is un-
precedented that are now so well established and considered a major visited services in
internet that doesn't change much from year-to-year [13]. The recent evaluation of ac-
tively used social networking services by Pew Internet indicates Facebook as the dom-
inance platform including the owned service of Instagram by 76 percent of active user’s
login while Twitter is reported to have 42 percent of active user’s login [12].
It is therefore reasonable to say that social media represent a revolutionary new trend
which have the potential to enhance existing and foster new cultures of openness [6].
Social media empowers its users by the ability to inexpensively publish or broadcast
information as it gives them a platform to effectively democratize information and com-
munication real time. Yet, despite the all facilitation of information creation and dis-
semination, there seems to be very limited understanding of what the “social media” or
“social networking services” exactly represent and eventually do to societies. Mean-
while, smart city programs which have received great publicity, there has been less
discussion about the evaluation and measurement regimes of societal and soft domain
aspects in smart cities. The lack of metric for grasping the societal activities has been
depicted in the ‘Global Innovators: International Case Studies and Smart Cities’ [10]
report that notes the inadequacy of existing evaluation approaches which tended to be
non-standard, and focused on implementation processes and investment metrics rather
than city outcomes and impacts.
SiBW 2018 94
This paper aims to investigate the social capital on innovation and entrepreneurship
within the smart cities by diving to social networking services as the derivative of one
of the major dimensions of smart cities. This research presents utilization of SNSs in
understanding and capturing entrepreneurial oriented discussions and further investi-
gates the various profile type impact on SNSs regarding entrepreneurial oriented dis-
cussions.
4 Methods
In this section, I share the approach on utilizing computational advancement to analyz-
ing social network services data in a systematic process. The approach uses semantic
and linguistics analyses for detecting major topical discussion in the twitter as the SNS
platform under study. The following section will describe a general process on SNSs
data collection, topic discovery and topic-content analysis. Furthermore, the analysis
interpretation discloses insightful characteristics of tweets regarding their topic of dis-
cussion and the characteristics of the content generator.
4.1 Systematic Approach to Analyze Social Network Services Data
The data in SNSs often comes unstructured as information that is not organized in a
pre-defined manner and not necessarily presents a pre-defined data model. Unstruc-
tured information is typically text-heavy, but may contain data such as dates, numbers,
and facts as well. Advancements in data mining and text analytics will be obtained in
this study to analyses the SNSs data for insightful information.
In this paper, the focus is on getting insight from SNSs as a major component in
smart cities regarding entrepreneurial oriented activity. The overall architecture to pro-
cess data in SNSs is composed and presented graphically in Figure 1. The considered
data is collected on Twitter (twitter.com). However, the process has a high extent of
generalizability to most of the data in SNSs platforms. The present process included
three major phases: capture, curate and consume. In addition, each phase has two sub-
phases consequently according for Figure 1.
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Fig. 1. SNSs systematic data analysis.
Capture: This is the process of collecting data, which contains the selection of the
data source, searching for the data and collecting data for other usage. Inputting the
searching query is the primary way to specify the content, which is of any interest to
retrieve. Various specifications can be implemented, such as keywords, length, date,
etc. in order to target the topic of interest. In other words, the required data is obtained
by set of criteria embedded with the search query. Some SNSs platforms such as Twitter
offer the possibility to retrieve data via the live stream.
Curate: Data curation is a broad term used to indicate processes and activities related
to the organization and integration of data collected from various sources. Data retrieval
methods are often loosely controlled, resulting in out-of-range values. The data prepa-
ration task is performed to reduce the irrelevant and redundant data present in the col-
lected set. This task is necessary for the forthcoming steps so to normalize the data for
a better knowledge discovery results. Data analysis can be very subjective to the context
of the study and expected results, but the two primary task in analysis can be mentioned
as data feature extraction and data classification. The intent for feature extraction is to
facilitate the further distinctions and categorization of the data. This task will drive
values (features) from the data regarding the context of the knowledge discovery pro-
cess. Classification of the data occurs in order to reduce the dimensionality of the data.
It’s an approach derived from the general hypothesis of the knowledge discovery task
so to distinguish the best-fit data points from the mass. In this case study, topic model-
ing has been performed in order to understand the major important cluster of discus-
sions regarding their topics.
Consume: This refers to publishing a presentable format of the information derived
from the data. The insights from the results can be provided in visually appealing way
or can be used as a metric to be combined with other data points for further interpreta-
tions. Having the systematic social network services data analysis explained, the next
section, the presented procedure will be applied on a case study.
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4.2 Evaluating Entrepreneurial Oriented Activity in Twitter : London City
Case Experiment
The background literature discusses the importance of emerging social network ser-
vices in smart cities and the need for investigating the effect of entrepreneurial discus-
sions in innovation ecosystem. In this section, we utilize the systematic approach on
analyzing SNSs data and emphasize on the new ways of benchmarking for social capital
by focusing on social network services. In order to solidify the objective, an experiment
has been condicted so to detect and capture entrepreneurial discussions on one of the
dominant social network services called Twitter. A popular microblogging tool Twitter,
has seen a lot of growth since it was launched in October 2006; is an online news and
social networking service where users post and interact with messages called ”tweets”,
restricted to 140 characters. Twitter users can post their opinions or share information
about a subject to the public. Twitter has 316 million users worldwide [14], providing
a unique opportunity to understand societal discussions and in this study case a way to
comprehend entrepreneurial oriented discussion.
The initial interest of the study was to capture innovation and entrepreneurial ori-
ented discussion from social network services as one of the major themes that needs
studying in smart cities. Start-ups are considered as a good representation of societal
practice of entrepreneurship. Start-ups are increasingly seen as significant contributors
to national job-creation [38]; employment and gross national product data demonstrated
the shift to an innovative start-up dominated economy [38]. Therefore, fostering the
start-up ecosystem is seen as the measure for improving national economy [35]. The
study case experiment has been conducted to collect the activity related to the start-up
ecosystem in the studied country so to capture the relevant societal discussions oriented
towards innovation and entrepreneurship.
Twitter is an SNS platform, which well represents and acts as support infrastructure
for start-ups, which organically are socially active. The study took the initiative to col-
lect a sample of tweets from a region (country) and extract features (words and
hashtags) related to start-up activities; we have applied techniques to decompose
hashtags, analyze them, and reuse the information extracted for classification purposes.
Twitter provides application programming interface (APIs) to access tweets and infor-
mation about posted content and users. The potential bias of Twitter APIs was discussed
by a recent research [20]. Twitter data has been used for a wide range of studies such
as stock market [8], brand analysis [22] and election analysis [41]. The unique charac-
teristics and features of Twitter as a microblogging service are illustrated in Figure 2.
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Fig. 2. Twitter Meta data illustration.
With respect to Twitter’s characteristics, a multi-component semantic and linguistic
framework was developed to collect Twitter data, prepare and analyze the data and
discover insightful information. In order to demonstrate the steps for utilizing SNSs
data for valuable insights, a high ranked smart city has been selected. London consid-
ered as one of the top smart city in global scale [18,21] and as the English is the domi-
nant language; this will facilitates the text analytics tasks. With respect to Twitter’s
characteristics, the search queries were constructed in a way that captures the most rel-
evant content regarding start-up scene and the entrepreneurial activity.
4.3 Data collection (Capture)
This phase attempt was to collect relevant tweets using Twitter's Application Program-
ming Interfaces (API) [42]. We have benefited from popular hashtag recommender
toolkits such as http://hashtagify.me, “https://ritetag.com” and “https://www.trends-
map.com” to discover the relevant hashtags and their proximities to the innovation and
entrepreneurial oriented discussions. Figure 3 is illustrating the hashtags proximity with
the subject of our initial search (#startup #startups #entrepreneur #tech #sme #innova-
tion #entrepreneurship #startuplife # hackathon) which obtained for detecting the ex-
tended hashtags and relevant discussions.
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Fig. 3. Twitter hashtag proximity distance.
Twitter's API provides both historic and real-time data collections. The latter method
randomly collects 1 percent of publicly available tweets. We used the real-time method
to randomly collect 10 percent of publicly available English tweets using several pre-
defined hashtags related queries mentioned previously within a specific period. We
used the extended query to collect approximately 4 thousand related tweets between
06/01/2017 and 08/30/2017. The data will be available in the following link
“https://goo.gl/mZumDp”. Table 2 shows a sample of collected tweets textual content,
users and overall interaction (sum of likes and retweets) for each tweet in this research.
4.4 Curate
This phase, the analysis of tweets by data feature extraction and data classification has
been advanced. Regarding the SNSs data which is collected from twitter. The investi-
gations began with an empirical analysis of the dynamics of the discussions in the Twit-
ter. The topical structure of discussions has been studied. Further, we will investigate
the characteristics of the major content producers. The Twitter analytic process was
facilitated by Azure cloud computing platform (azure.microsoft.com) which the pipe-
line of the process can be seen in Figure 4.
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Fig. 4. Twitter content analysis with Azure Cloud Computing Platform.
After importing the retrieved tweets as the input data, a filtering process applies to
structure and reduce the noise of the data. The data feature extraction distinguishes the
valuable data points such as number of retweets, likes, profile identifications and the
textual content of the tweets as we will leverage these data point for further insights.
One classification task for analyzing tweets; topic modeling has been utilized in order
to reveal the topical formation of the discussion. Topic modelling can be described as
a method for finding a group of words (i.e. topic) from a collection of documents (in
our case tweets) that best represents the information in the collection. It can also be
thought of as a form of text mining – a way to obtain recurring patterns of words in
textual material [37]. There are many techniques that are used to obtain topic models
in this study we leveraged Latent Dirichlet Allocation (LDA) and the consequent visu-
alization toolkit developed for that (LDAviz) so to visually show the major twitter dis-
cussion topics [7]. The next section we will represent the classification calculation re-
sults visually.
4.5 Results (consume)
So far, we were able to encapsulate the entrepreneurial oriented activity via focusing
on start-up scene in the smart city of London. The dynamic relevant discussions in so-
cial network services (in this study Twitter) were captured and curated to transform the
SNSs data into insightful information. The dynamic discussions and interactions on
SNSs regarding entrepreneurial oriented matters can represent the social capital as ex-
plained in earlier sections. In this section, we will dive deeper into SNSs data in order
to detect the most influential content and type of content generator profiles associated.
A categorization analysis task will be performed into the textual content of the SNSs
data in order to get a broad overview and distinguish the general topic of discussions.
The analysis of topical structure of SNSs discussion with LDA is visualized in Figure
5, which illustrates the general topical theme of discussions. The six major clusters are
SiBW 2018 100
named based on the major keywords mentioned under each topic. The visualization also
revels the size of the discussion proportional to other topics via their circle size and
indicates the distance of topics in two dimensional distance map.
Fig. 5. Intertopic Distance Map.
As part of data consumption and insight generation task, with having the meta data
of each posted tweet and the associated profile under each of the topics, the influential
profiles based on their overall interaction (Number of retweet and likes received for the
post) can be detected. This information will reveals how contents (tweets) gets atten-
tions in different topics regarding their content generators. The motivation for content
generators in twitter profile categorization stems largely from the fact that humans as
intelligent individuals impose complex factors on the consumption and dissemination
of information on SNSs [26,29]. Therefore, as the different profile types have different
purposes and cater to different needs, the categorization of content generators in each
of the six topical discussions will help us to measure the impact and influence each
category is making. The categorization definitions and process inspired from Uddin et
al. [43] and due to the study intentions, three major different types of Twitter profile
defined and were developed which are as follows:
Personal profiles: These accounts contain personal content, have no ties to business,
and do not mention corporate or brand information. They are created by individuals
who do not wish to be identified with their employer. Technically, the accounts have
been created to acquire news, learn, have fun, etc. Generally, these individuals exhibit
low to mild behaviour in their social interaction. Professional profiles: Personal users
who communicate their professional views on Twitter. They share useful information
on specific topics and are involved in healthy discussion related to their specialist in-
terests and expertise. Professional users tend to be highly interactive; they follow many
and are followed by many. Corporate and business profiles: Different to personal and
professional users in that they follow a marketing and business agenda on Twitter. Their
SiBW 2018 101
profile description accurately describes their motives, and similar behaviour can be ob-
served in their tweeting patterns. Frequent tweeting and less interaction are the two key
factors that separate business users from both personal and professional users. The type
of content will be primarily corporate. Such accounts are often managed by company
teams working under a specific brand name related to the company, providing corporate
news and support.
Under each of the six discussion topics, profiles ranked based on their tweet interac-
tion ratio (number of retweets + number of likes) were manually looked and categorized
according to the three major profile descriptions. Figure 6 is an illustration of the man-
ual categorization of the top content generator profiles.
Fig. 6. Categorization of tweets based on topic and generator.
As it can be observed from Figure 6, professional users have more influence in over-
all. In topical content categories, professional users are generating the highest influence
in educational, motivational, promotion and events type of topics. Corporate and busi-
ness profiles tend to be more influential in news category, educational, and promotional
after professional users. Counting the likes, the calculation reveals that professional
users have more interaction, especially in educational and motivational content cate-
gory, while business profiles have the higher interaction in the news category and mo-
tivational category in second order. Personal profiles have the lowest influence among
the other two profile categories in both retweets and count of likes. The difference in
distribution of interaction is that motivational and educational receives the highest re-
tweets and in the calculation of like counts, the high-interacted categories will shift to
events and news.
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