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Quantification of Productivity of the Brands on Social Media with Respect to
Their Responsiveness
Article in IEEE Access · January 2019
DOI: 10.1109/ACCESS.2019.2891081
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Quantification of Productivity of the Brands on Social Media with Respect to their Responsiveness
Basit Shahzad1, Kinza Mehr Awan2, Abdullatif M. Abdullatif3, M. IkramUllah Lali4, M. Saqib Nawaz5, Ume Ayesha6 and Muzafar Khan1
1Department of Engineering, Faculty of Engineering & Computer Science, National University of Modern Language, Islamabad, Pakistan. 2 Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan. 3College of Computer & Information Sciences, King Saud University, Riyadh, Saudi Arabia. 4Department of Computer Science, Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan. 5LMAM & Department of Informatics, School Mathematical Sciences, Peking University, Beijing, China. 6Department of Computer Science and IT, University of Sargodha, Sargodha, Pakistan.
Corresponding author: M. Saqib Nawaz (e-mail: [email protected]).
ABSTRACT Social online marketing is expanding fast with evolution and recent development in
Information and Communication Technology (ICT). Investigating how companies are exploiting social
media for marketing, advertisement and consumer’s engagement is gaining more and more interest. In this
article, brands/companies data on Twitter is collected and analyzed to compute the overall company response
on Twitter. Responsiveness of a company is inferred from three features: company popularity, average
company replies and average followers’ replies. Twitter network features are used in calculating posting
frequency for companies and their followers. It is shown that the proposed approach can be used in finding
the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is
extracted and the posting behavior of brands and their followers is determined with the help of Twitter
network features such as retweet count and geolocation. This study contributes to the literature on how Twitter
data and its network structure features can be exploited in finding responsiveness and posting behavior of
companies and their followers. We believe that this approach can be used effectively in developing prediction
and information-filtering systems, particularly the personalized-recommendation systems.
INDEX TERMS Twitter; Brands; Followers; Company response; Retweet count
I. INTRODUCTION
In social networking theory, human behavior is influenced
by the interpersonal relations. With the evolution and rapid
development in World Wide Web (WWW), this terminology
can also be applied to social networking as it is influencing
Internet users’ behavior. This perception becomes more
evident with the increasing number of active users on social
network sites and quantity of their time spent online [1-2].
Online social networking provides a platform for community
interaction to exchange and share ideas and information. It is
also used for negotiation and collaboration in a community.
Internet applications such as YouTube, Twitter, Facebook,
Myspace, etc came under the definition of social network
sites. These sites permit users to generate online
communities to exchange and share content, such as personal
messages, videos, pictures, and other information. As of
April 2018 [4], 4.2 billion people use Internet, in which 3.03
billion actively use social media and spent 116 minutes on
average each day. Approximately 90% retail brands has
more than 2 social media channels and 80% of all small and
medium size businesses use social platform. In such
circumstance social media transform consumers (users) from
silent and isolated persons to worth noting and
unmanageable collective [6].
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This online community interaction highly revolutionized
business in last decade. The rapid popularity and unique
aspects of online community interaction renaissance
marketing trends like advertising, promotions and customer
relationship [7]. The communities on social media help
consumers in increasing their knowledge and improving
their companionship with the brand. Due to worldwide
emergent accessibility of Internet, in the last two decades,
the consumer’s communities have experienced huge
improvement in quality and relationship. Further, online
consumer communities are paying a lot to the
collectivization. Recent success rate depicts that such sort of
online interactions are creating a huge influence in business
sector [8]. The most special category of online consumer
communities is “Brand Communities” [9]. Brand community
serves as a platform for building strong relationship between
the consumer and the business brand. “Brand communities”
in [9] are defined as a special non-geographical communities
constructed by the admirers of a specific brand. Brand
communities provide facilities of information sharing,
logging the history of brand and build the loyalty for the
brand. It also fulfills the customer needs to social interactions
and assists in activities like shopping, entertainment,
researching and updates. Hence the brand communities
develop the social structure for customer-investor
relationship.
Initially brand communities were started on the web 1.0
platform through the companies’ portals. However, with the
emerging popularity of the social media, many companies
are shifted to social networking sites for their brand
communities [12]. They are joining this space because of
easy and vast reach to consumers through low cost and high
used social networks. These websites allow user the freedom
to converse in any language on any topics or issues for the
flow of information. Thus consumer conversation over these
social website facilitates brand communities to collect
individual’s perspectives from different sources. As these
consumers are valuable source of information for a brand’s
reputation, social networking sites are supporting a lot for
propagation of brand communities.
On the other hand, investors are willing to acquire,
manage and facilitates the brand communities by connecting
to the people who admire their brand [14-15]. Through brand
communities, investors get multiple advantages such as
consumers’ likings, their perspectives to new arrivals and an
opportunity to build a loyal customer relationship. Further,
consumers may have their own intentions to join brand
communities. They may not only fulfill individual’s social
but also their psychological needs. It is believed that
sometimes consumer joined the communities to get
themselves recognized with the brand and get an identity.
Due to high adoption rate of social media, business
communities are also using it for their branding activities.
Investors and marketers have deep insight in these branding
communities. In social media, Twitter is a microblogging
site that offers its users’ to write short messages called
‘tweets’. The accessibility, speed and ease-of-use of Twitter
have made it an invaluable communication tool. Twitter is
used now as a business promotion platform by companies as
well as investors. Further, consumers also follow company
pages to keep themselves up to date about the latest news of
companies and products.
As companies’ presence can be found on social media, it
is significant to investigate that how effectively this forum is
being used for customer satisfaction and responsiveness. In
this work, efforts are made to identify how Twitter is used
efficiently and successfully by companies which are more
responsive to their customers. We first collect 81 companies’
profiles and tweets posted by companies, users and their
followers. These companies/brands were chosen on the basis
of their popularity in Pakistan and their presence on Twitter.
Tweets are then analyzed to find the responsiveness of
companies. A company is considered responsive when it or
their followers responds positively to the user by using
twitter-provided means of responsiveness, including the
comment, mentions etc. The responsiveness outside the
Twitter boundary is not included in this study. Further,
Twitter network features are exploited to extract informative
links for users and to find the posting patterns of companies
and their followers. Since the network size of Twitter is
much smaller than Facebook [16-17], we analyze our
methodology with Twitter for simplicity and to lower the
complexity.
The remaining article is organized as follows: In Section II,
we discussed the related work. Proposed approach to
investigate the responsiveness of companies on the Twitter
platform is explained in Section III. Experiments are
performed in Section IV, where the obtained results are also
discussed to access the feasibility of proposed approach.
Finally, the article is concluded in Section V with some
observations.
II. LITERATURE REVIEW
Social media is merging within routine actives of human
lives quickly and vastly in various ways. Kaplan & Haenlein
[12] commented that while discussing the origin and essence
of social media, it is important to recognize the Web 2.0 as a
platform for the evolution of social media. Thus we can
define social media as “Internet-based applications that carry
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user generated content which can be archived and shared
online for other users” [18]. As the information sharing
standards are changing, markets have also acknowledged
their power to encourage consumer’s sharing and opinions
about brands, known as Social Media Marketing. It is the
great transition of the present era from traditional marketing
techniques. Flagler [19] suggested that social marketing
should be opted parallel with the existing plans and this
integration in combine usage will lead to better outcomes.
Now it is the role of marketers to merge up and organize
consumer communities, and identify the most suitable social
network.
Researchers are paying attention towards the impact of
social media on consumers’ attitudes towards the brand,
his/her decision making, brands sales and market strategies
[20]. Social media is also becoming most trustworthy and
promising approach for brands to target new consumers.
Laroche et al. [21] suggested that online brand communities
are mostly shared by consumers with mutual consciousness,
rituals, and obligations in a society. Later in another study,
[22] developed a structural model to depict that brand
communities have an impact upon customer and its relation
with brands, its products which builds a positive effect on
brands loyalty thus help in value creation. Munnukka et al.
[23] developed a conceptual model to elaborate the
importance of brand commitment. This study revealed that a
connection exists between brands commitment and social
behavior of community which influence the brands loyalty,
customer repurchase intention and word-of-mouth. Further,
Dessart et al. [24] presented a structured survey where they
interviewed 21 different members of various shortlisted
online brands. The authors executed the empirical research
to bring new concepts of consumer’s engagement. They also
worked to find out relationship between consumer
engagement and other related concepts. The study identified
three key social factors of consumers engagements
(behavior, affect and cognition), which add to enhance the
brand loyalty.
Social media is also used by many companies to identify
and respond to negative feedback about product from the
consumers [25]. Nikolova [26] performed a survey based
upon well-structured questionnaire to study the effectiveness
of social media towards building positive brand attitude.
Prior to a detail survey, a preliminary research was executed
to list some important platform for social media. Later
questionnaire was online sent and respond by 151
individuals of multiple nations. The results were measured
with a conceptual model. The study concludes that social
media are turning into branding platform through which
consumers get into conversion about brand. Mostly
consumers become fans and conveys positive attitude
towards the brand. It was also emphasized that social media
platform chosen for branding must works smartly and in a
professional manner. A 3-M (Megaphone-Magnet-Monitor)
framework was proposed in [34] to study the use of social
media platforms for online interaction between brands with
their customer and vice-versa. Firm-to-customer commun-
ication in the framework is represented with megaphone,
customer-to firm communication with magnet and customer-
to-customer interaction with monitor. A case study of
Starbucks described how it is using these online platforms
for better communication with the customers and for social
media based marketing. Twitter is used by many researchers
in e-commerce, e-business and online brand communities.
On Twitter, users can post any content via “tweets” from any
geographical location and broadcast these to anyone
connected “followers” with them through social network.
These tweets can be forwarded by any follower through
“retweeting” [27]. Likewise, users (consumers) can follows
the brand pages and read the tweets about the products [28].
Chung & Darke [29] called such consumers engaged in
“brand-consumer conversations” as “brands followers” on
Twitter. These brand pages’ act as instructive and revealing
venue for the consumers [30]. Twitter facilities the brands to
develop an interpersonal communication on one-to one basis
with their consumers. A stochastic based point process
framework is used in [35] to study crowdsourcing in Twitter
as a marketing mechanism that can enhance the online brand
popularity and awareness.
Kwon et al. [31] discussed that to build a close
relationship with consumers, companies often utilize
interpersonal messages in their tweets post. Conversely, user
also post tweets related to brands or companies to express
their feelings and to provide their feedback. Thus making
twitter as social channel for mutual beneficial relationship
between the consumer and the brand [32]. Heaps [13] also
explores that twitter may also increase traffic to the brands
websites, providing the opportunity to tell their story and
connect more deeply with consumers by providing them
events updates and alerts and ultimately increase sales. For
Instance, in 2009, Dell has earned an extra $3 million in sales
by “tweeting” about its refurbished computers outlet.
Similarly, Kim et al. [27] surveyed 400 brands followers
constructed upon the consumer socialization framework to
figure out the causes which affect brands community on
twitter. As scientists are working on different factors to
improve brands and consumer relationship, it is
acknowledged that responsiveness of brands towards its
consumer could be an import factor to increase consumer
loyalty and bonding with the brand. Zhang et al. [15] also
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figured out some quality factors which impact upon brands
and consumer relationship to escalate the brand loyalty.
These quality factors include self-congruence, social norms,
information quality and interactivity. Vries et al [36] studied
the popularity of 355 brand posts on Facebook. Results
indicate that characteristics such as vividness, interactivity,
information, entertainment and valence of brand posts are
important for consumers to like and comment on firm-
generated brand stories on brand fan pages.
In literature, some work has been done on online social
branding and its multi-dimension relationship with
consumers. However, to the best of our knowledge, we were
unable to find any significant work on investigating the
responsiveness of brands on social media towards their
consumers. Our focus is to investigate the influence of online
social network features on the popularity of brands and how
brands respond to users through conversation of tweets and
replies. Furthermore, Twitter network features such as
retweet count and geolocation are exploited to extract useful
links and posting patterns of brands and their followers.
III. METHODOLOGY
The proposed methodology for this work is shown in Figure
1. It consists of three main stages: (1) Data Collection from
Twitter, (2) Data Modeling, and (3) Data Exploration.
Brands/companies tweets are first collected and they are
stored in a separate document. Collected tweets are then pre-
processed and some important features are selected to infer
the popularity and responsiveness of companies. Tweets are
also analyzed to find top responsive companies in Pakistan,
response time of company and their followers towards its
user’s post, informative links present in tweets and
companies and their followers’ replies pattern. Responsive
companies are those that exploit the social media for constant
communication with its consumers through posts and replies.
Each stage is further elaborated next.
A. DATA COLLECTION FORM TWITTER
Tweets from Twitter official accounts of companies and
organizations are collected from December 1, 2017 to March
12, 2018 with Twitter API [11]. With Twitter API, relevant
tweets that match with a specific query provided by us is
collected. Twitter Search API and MySQL database are used
to develop a data collection server. In the database, some of
the attributes of a tweet are used for sorting and some
attributes for tracking geo-location such as time zones. It is
found that tweet database contains tweets written in other
languages, which are removed. Tweets written in English are
kept in the database. We selected twitter as our data source
due to the following reasons [5, 10, 33].
1. Due to high social impact, Twitter draws more research
consideration for social factors.
2. Search options in twitter provide the ease to extract the
conversation occurred on the platform.
3. Google search results also indexed the tweets results.
4. Twitter API are more effective and efficient for data
gathering in contrast to any other social media such as
YouTube, LinkedIn and Facebook.
5. Tweets also contain the time zone of their generation.
6. Information regarding the retweets and followers of
each tweet are also maintained.
Name of brands/companies from which tweets were
collected is listed in Appendix A. These brands / companies
are from different service and product categories that
includes cosmetics, clothes, electronics, technology,
automotive, beverages, airlines, etc. Initially, we selected
100 companies for data collection. However, total tweets (in
English) collected in time period of over three months (100
days) for 19 companies were less than 60. Thus, we omit the
tweets of these companies for further analysis. In total,
approximately 40K tweets were collected from official
accounts of 81 companies/brands. Following information
(listed in Table 1) regarding each brand’s tweet are extracted:
FIGURE 1. Block diagram of methodology
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1. Total number of tweets collected for each brand.
2. Total number of the followers.
3. The context of the tweet, its time and date.
4. Company’s and followers total replies.
TABLE I
COMPANIES TWEETS, FOLLOWERS AND REPLIES INFORMATION
Company/Orga
nization Name
No of
tweets
No of
followers
Company
replies
Followers
replies
Geonews_urdu 6891 739000 451 810
CNN 2762 32900000 211 549 NASA 456 22400000 08 27
Mercedes-Benz 415 1740000 415 674
ARYnewsofficial 1989 851000 673 1458
PTVnewsofficial 1445 214000 46 527
iPhone_news 1437 769000 17 130
LinuxDotCom 361 103000 104 134 Hootsuite 979 8005000 28 445
BBCurdu 668 167004 63 2041
B. PRE-PROCESSING AND FEATURE SELECTION
For each company/brand, one document is created and all the
collected tweets from the same trending company/brand is
saved in its respective document. For example, all tweets
related to a company “Mercedes-Benz” is first collected and
stored in a document with the name “mercedes”. For the
scenario when a tweet has more than one company/brand
name then that particular tweet is stored in both of its
respective documents. For example, a tweet that contains
hashtags from two companies such as “Mercedes-Benz” and
“toyota” is saved in the documents “mercedes” and
“Toyota”.
Raw tweets that obtained during the collection process
need to be addressed in proper format so that meaningful
information can be extracted from them. Main focus in pre-
processing is on data cleaning, normalization and filtering.
From tweets some attributes that do not play any role in
finding interest such as User ID, Statuses Count, Friends
Count, Display URL (Uniform Resource Locator) and
Screen Name etc, are first removed in pre-processing.
Tweets are then pre-processed with three steps:
1. Stop-Words Removal: In Natural Language
Processing (NLP), stop-words such as “a,” “an,” “the”,
“of”, etc are considered noisy. Stop-words are deleted
from the tweets with the simple rule-based approach.
2. Term Normalization: Non-standard words such as
‘f9’, “lmao”, “cuz”, “lol” etc are normalized with the
Chat Word dictionary (chatworddictionary.com). Other
non-standard words are converted to standard form with
the PyEnchant library (www.pythonhosted.org/py-
enchant/). By using the “check function,” in the library,
the correction of spelling can be confirmed by “True”
and “False.” In case the spelling is incorrect, correct
spelling can be recommended by using the “suggest
function”. However, in order to simplify the whole
procedure, the first suggested word is selected as the
most relevant standard word.
3. Tagging the Normalized Words: Python offers a
Natural Language Toolkit (NLTK) (www.nltk.org/) that
can be used to distinguish the type of words, which is
also known as Part of Speech (POS) tagging. With
NLTK, we obtained the noun type of words only from
the normalized words.
C. DATA MODELING
Feature selection is an important stage in the classification
and data mining [3]. Features selection in tweets for
company response investigation is made on the basis of TF-
IDF score (i.e. term frequency, inverse document frequency)
per class bases. In a document, TF-IDF score is used to
investigate the relevance and importance of a feature (term)
and this score depends on the overall occurrence of a term in
the document. The popularity of a company is calculated as:
CPm = 𝑈𝑚𝑡
𝑡𝑚 (1)
Where CPm indicates the popularity of mth company, tm
is total number of tweets for mth company and 𝑈𝑚𝑡 is a count
of users tweets for mth company.
Two other features such as average companies’ replies
(ACR) and average followers replies (AFR) for each user
post/tweet are used to compute the overall company response
(OCR). OCR is calculated as follows:
OCRm = ACR × AFR × CPm (2)
IV. EXPERIMENTS AND RESULTS
In experiments, first we calculated the CP, ACR, AFR and
OCR respectively for each brand. Results for top 15
companies are shown in Table 2.
For brand Geonews_urdu, 810 tweets were from users
and followers out of 6891 total tweets. So CP is calculated to
be 0.117. From December 1, 2016 to March 12, 2017,
Geonews_urdu posted new useful information on Twitter
214 times, and its total replies were 415. So ACR, AFR and
OCR is 1.939, 3.785 and 0.858 respectively. Calculated ACR
for four brands (PTVnewsofficial, iPhone-News, BBCurdu
and NASA) is less than one as total company’s replies were
less then total post. High OCR (150.1) for Mercedes-Benz is
due to the fact that total post by companies were less (55)
then company and followers total replies (1089 in total). For
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QZ and WSJ, number of replies from companies were greater
than replies from followers. We also observed that most of
the posts and comments from followers were a reply to a use
questions or concerns. In 11 brands (out of 81 brands),
replies from companies were greater than followers’ replies.
For other 70 companies, number of followers’ replies were
greater than company replies. In Pakistan, top 10 companies
with maximum OCR is listed in Table 3.
TABLE 2
TOP 15 RESPONSIVE BRANDS/COMPANIES WITH MAXIMUM OCR
Brands/Company
Name
No of
tweets CP ACR AFR OCR
Geonews_urdu 6891 0.117 1.939 3.785 0.858
CNN 2762 0.198 2.234 6.1 2.698
QZ 2524 0.025 13.76 5 1.721
WSJ 2427 0.107 9.103 8.965 8.732
ARYnewsofficial 1989 0.733 3.05 6.627 14.815
PTVnewsofficial 1445 0.364 0.294 2.395 0.256
iPhone_news 1437 0.09 0.85 6.5 0.497
Eexpressnewsofficial 1129 0.131 1.817 6.487 13.330
Hootsuite 979 0.454 1.12 17.8 9.050
BBCurdu 668 0.055 0.434 15.21 20.169
Discovery 502 0.65 1.2 1.65 1.287
NASA 456 0.059 0.38 1.285 0.028
Mercedes-Benz 415 0.624 7.545 12.25 150.1
LinuxDotCom 361 0.371 2.97 3.828 4.217
ARYdigitalasia 320 0.637 0.915 6.15 3.584
TABLE 3
TOP 10 BRANDS/COMPANIES IN PAKISTAN WITH MAXIMUM OCR
Company Name OCR
HSYCOUTUREKING 3.416
MotifzClothing 1.184
BBCURDU 20.169
KFC 2.014
ARYnewsofficial 14.815
Yayvo_TCS 1.129
EXPRESSnewsofficial 13.330
Windowsdev 3.328
UcBrowser 0.756
ARYdigitalasia 3.584
We also investigated the response time from company or
their followers to a user tweet. As most of the followers
replies were for a user question, so we also consider the
followers replies in investigating the response of the
company. This is achieved by exploiting Tweet timestamp.
Tweet timestamp is a string that is attached with each tweets
and it shows the generation time of the tweet (such as Dec
24 07:34:23 +0000 2017). Where ‘+0000’ shows that the
time is in GMT (Greenwich Mean Time). Average time
taken by a company or its follower to reply to a user tweets
is listed in Table 4.
TABLE 4
RESPONSE OF COMPANY OR THEIR FOLLOWERS TO USER’S POST
Company Name that
replied to a user post
Response Time
(in Minutes)
Geonews_urdu 15.5
CNN 226
QZ 22.2
WSJ 264.5
ARYnewsofficial 92.3
PTVnewsofficial 165.3
iPhone_news 66
EXPRESSnewsofficial 24
Hootsuite 394
BBCurdu 365
Discovery 23.3
NASA 60
Mercedes-Benz 112.5
LinuxDotCom 61.6
ARYdigitalasia 181. 3
It is noted that almost 2/3 replies were from followers of
a company. Low response time for companies like Hootsuite
and BBCURDU is due to the fact that they have less number
of followers. Almost all the replies were inside 24 hours. It
shows that companies and their followers’ uses Twitter
frequently and respond to questions mostly within few hours.
From followers’ replies, we noticed that some influential
(ones that replied frequently) followers’ hugely affect the
engagement of a brand post. We can identify such influential
followers by counting their replies. After identification, such
followers’ can be used and increased by brands for
increasing the brand recommendations and awareness.
Furthermore, we observed that company and followers
posts, replies also contain links to useful information for
users. We stored company and followers’ tweets and replies
in a separate document for further investigation. We
developed a regular expression for the extraction of such
information from tweets and tweets with maximum Retweet
count are given importance. Some tweets from where the
links were extracted which are liked by majority of users are
listed in Table 5. The reason to select links in a tweet with
maximum Retweet count is that if a user finds a post
interesting than most probably he or she will like the post and
retweet it which also shows that the links present in a tweet
with maximum retweet count is authentic. Some of extracted
links and tweets liked by majority of users are listed in Table
5.
Average number of tweets posted by followers and
company each day is calculated to investigate the posting
pattern of a company and their followers. We have collected
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data for 14 weeks. In total tweets, almost 40% tweets were
from either follower or company. On average, we collected
1.14K tweets approximately each week and 163 tweets each
day. Average tweets generated each day is shown in Figure
2. It can be observed that least number of tweets were posted
by a brand/company and its followers on Wednesday.
Similarly on Monday and Tuesday, tweets posting
percentage is low. The possible reason for low posting
behavior is that on weekdays, most of the people are
generally busy in their work and occasionally use social
media. On the other hand, high tweet posting frequency is
observed on weekends. From this, it can be concluded that
on Saturday and Sunday, the individual’s spends more time
on social media. Such information can be used to compute
how many days between two posts by a brand effectively
increases popularity of brand posts.
TABLE 5
INFORMATIVE LINKS FOR USER THAT A COMPANY OR FOLLOWER POSTED IN
REPLY TO A USER TWEET
Company
Name Tweet Link
ARYNEWSOFFICIAL Watch #NEWS@6 hosted
by #ZaraAnsari https://t.co/i11T1A6BsH
https://t.co/pT9eStS6Ni
CNN Starbucks pledges to hire
10,000 refugees https://t.co/lWMhuc7Ijq
https://t.co/FouBFUcTVa
CNN
Official: Recent intel found
that an al Qaeda affiliate
was perfecting techniques
for hiding explosives in
batteries
https://t.co/jsgrLpyv7z
Reebok
Let your strength shine
through each and every day.
#BeMoreHuman
https://t.co/EqcP23ozwc
PizzaHut Our top picks are toppings
heavy. https://t.co/FpKyqYrZkW
Nestle
Water is a precious, shared
resource. This Zero Water
factory doesn’t take a drop
from the ground.
#WorldWaterDay…
https://t.co/BGoAbgkiyt
UCBrowser Can you get your head
around this one? #UCQuiz https://t.co/8lLorqZEaU
IBM
If you missed CEO Ginni
Rometty’s keynote address
from #ibminterconnect,
watch the replay here:
https://t.co/sAM5BHE6BJ
IBM
Learn about Olli - the 3D-
printed, #cognitive-enabled,
self-driving minibus ????
turning heads at
#ibminterconnect:…
https://t.co/4LoqDKxMZ0
Dominos Eeny, meeny,
miny...BOTH. ???? https://t.co/lODSBBXo51
Discovery
You've better have plenty
of energy to catch up to
these spunky dogs! via
@AnimalPlanet
https://t.co/KJAQ674gP0
Obtained results suggest that brands / companies now
frequently use social media as a platform for advertisement
where they provide the information about products,
promotions and for interaction with consumers. The
approach proposed in this article allows the characterization
and quantification of brands popularity on the basis of online
content created by brands and their followers. The results
obtained are consistent with hypothesis mentioned in [36]
that brands posts and followers’ replies reflect brand post
popularity.
FIGURE 2. Company and their followers’ average daily posting pattern
Some important limitations of the work done in this
article deserve mention. We have chosen popular brands in
Pakistan only. The amount of data that we collected and
analyzed is sufficient to investigate the factors that drive the
popularity of brands on social media. However, majority of
the posts from brands were related to their products, thus we
exclude two explanatory variables (quiz post form or event
post from a brand/company [34]) from the analysis. Future
studies may require to use a more comprehensive dataset.
Since we have given importance to the response times for
followers’ replies, we can also trace brands most engaging
minutes, hours and days in the week to find real effective
time windows that should be taken into consideration for
better computations of the popularity for brands/companies.
V. CONCLUSIONS
Twitter data are now considered an important online source
for trend-setting, future prediction, information filtering,
recommendation systems and marketing. In this article,
Twitter data from popular brands are analyzed to investigate
the responsiveness of company and their followers towards
their consumers. Moreover, tweets are analyzed to extract
useful links for consumers and posting patterns of companies
and their followers on weekly basis. Twitter network features
is also exploited in the modeling process as textual
information can often be noisy and coarse. We believe that
this methodology can also be used to examine
responsiveness of companies on other sites for social
networking such as Facebook.
40
50
60
70
80
90
100
110
120
No
of
Twee
ts
Avarage replies/day
Followers Company
2169-3536 (c) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/ACCESS.2019.2891081, IEEE Access
Specifically, investigating social networking sites for
brands that are popular in other countries is an important and
valuable research area that will enable us to find more
important generalized features that play a role in determining
the popularity of brands. We are also interested to investigate
how the popularity of brands and their posts is affected by
social contagion parameters (such as brand fans influencing
each other). Moreover, prior information about how long it
takes before a certain number of people re-tweet or comment
on a brand post can be used to model the adoption curve of
re-tweets and comments. Such results will enable brands to
manage their online relationship with consumers and
marketing communication. Last but not the least, we plan to
investigate the effectiveness of our approach with classifiers
such as Support Vector Machine (SVM), Logistic
Regression (LR), Naïve Bayes (NB), K-Nearest Neighbor
(kNN) and to explore which classifier is statistically better
than other.
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/ACCESS.2019.2891081, IEEE Access
APPENDIX A
No Name of Brand No of tweets No Name of Brand No of tweets
1 geonews_urdu 6891 42 Mcdonaldspk 90
2 CNN 2762 43 Kodak 64
3 QZ 2524 44 Pizzahut 63
4 WSJ 2427 45 Motifzclothing 60
5 ARYNEWSOFFICIAL 1989 46 Acer 59
6 PTVNewsOfficial 1445 47 HSYCOUTUREKING 59
7 iPhone_News 1437 48 Intel 58
8 EXPRESSNewsPK 1129 49 Bonanzamarket 58
9 Hootsuite 979 50 YamahaMotoGP 51
10 BBCURDU 668 51 ProcterGamble 44
11 Discovery 502 52 Cadbury_SA 42
12 NASA 456 53 Walls 61
13 Mercedes-Benz 415 54 Zongers 90
14 LinuxDotCom 361 55 HaierPakistan 86
15 Arydigitalasia 320 56 Ultabeauty 85
16 UCBrowser 250 57 ChenOneOfficial 93
17 Loreal 261 58 Samsung 112
18 Sony 577 59 Firefox 60
19 Lawn_Collection 223 60 Sprite 80
20 Nissan 271 61 Twitter 77
21 IBM 270 62 TelenorPakistan 107
22 Yayvo_TCS 362 63 MountainDew 86
23 dolcegabbana 143 64 Dominos 95
24 Reebok 185 65 HP 167
25 Nestle 132 66 KITKAT 122
26 Windowsdev 128 67 Hardees 61
27 Nokia 124 68 LAYS 71
28 Toyota 424 69 HascolPetroleum 120
29 KFC 316 70 Jazzpk 176
30 Official_PIA 113 71 DunkinDonuts 53
31 Huawei 109 72 OlpersMilk 91
32 Alkaramstudio 106 73 UrbanOutfitters 79
33 Honda 104 74 NatGeo 89
34 Pepsi 96 75 Oreo 69
35 Dell 88 76 CocaCola 218
36 Shell 88 77 Tcs_couriers 137
37 Lenovo 83 78 Batashoes 63
38 Pantene 80 79 Googlechrome 75
39 LGUS 77 80 SunsilkPakistan 98
40 SonyElectronics 276 81 Surfexcelpk 65
41 Microsoft 170
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