See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/330234859

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

CITATIONS

2READS

199

7 authors, including:

Some of the authors of this publication are also working on these related projects:

SOFTWARE DEFINED NETWORKING View project

Data Mining View project

Basit Shahzad

King Saud University

78 PUBLICATIONS   439 CITATIONS   

SEE PROFILE

Kinza MEHR Awan

2 PUBLICATIONS   10 CITATIONS   

SEE PROFILE

Muhammad Ikram Lali

University of Gujrat

57 PUBLICATIONS   173 CITATIONS   

SEE PROFILE

M. Saqib Nawaz

Peking University

21 PUBLICATIONS   67 CITATIONS   

SEE PROFILE

All content following this page was uploaded by Muhammad Ikram Lali on 09 January 2019.

The user has requested enhancement of the downloaded file.

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

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Digital Object Identifier 10.1109/ACCESS.2019.Doi Number

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: msaqibnawaz@pku.edu.cn).

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].

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

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

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

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

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

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

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

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

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

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

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

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.

REFERENCES [1] E. Alwagait, B. Shahzad and S. Alim. Impact of social media

on students’ academic performance in Saudi

Arabia. Computers in Human Behavior, vol. 51, pp. 1092-

1097, 2015.

[2] T. Kaya and H. Bicen. The effects of social media on students’

behaviors; Facebook as a case study. Computers in Human

Behavior, vol. 59, pp. 374-379, 2016.

[3] M. S. Nawaz, R. U. Mustafa and M. I. Lali. Role of online data

from search engine and social media in healthcare

informatics. Applying Big Data Analytics in Bioinformatics

and Medicine, IGI Global, Chapter 12, pp. 272-293, 2018.

[4] Social Media Statistics. Available at:

https://www.brandwatch.com/blog/amazing-social-media-

statistics-and-facts. Accessed on June 18, 2018.

[5] B. Shahzad, M. I. Lali, M. S. Nawaz, W. Aslam, R. U. Mustafa

and A. Mashkoor. Discovery and classification of user

interests on social media. Information Discovery and Delivery,

vol. 45, pp. 130-138, 2017.

[6] A. Patterson. Social-networkers of the world, unite and take

over: A meta-introspective perspective on the Facebook

brand. Journal of Business Research, vol. 65, pp. 527-534,

2012.

[7] R. Hanna, A. Rohm and V. L. Crittenden. We’re all connected:

The power of the social media ecosystem. Business Horizons,

vol. 54, pp. 265-273, 2011.

[8] D. Ganley and C. Lampe. The ties that bind: Social network

principles in online communities. Decision Support Systems,

vol. 47, pp. 266-274, 2009.

[9] A. M. Muniz and T. C. O'guinn. Brand community. Journal of

Consumers Research, vol. 27, pp. 412-432, 2001.

[10] M. S. Nawaz, M. Bilal, M. I. Lali, R. U. Mustafa, W. Aslam

and S. Jajja. Effectiveness of social media data in healthcare

communication. Journal of Medical Imaging and Health

Informatics, vol. 7, pp. 1365-1371, 2017.

[11] Twitter Search API. Retrieved from website:

https://dev.Twitter.com/rest/public/search.

[12] A. M. Kaplan and M. Haenlein. Users of the world, unite! The

challenges and opportunities of Social Media. Business

Horizons, vol. 53, pp. 59-68, 2010.

[13] D. Heaps. Twitter: Analysis of corporate reporting using social

media. Corporate. Governance Advisor, 17, pp. 18-22, 2009.

[14] Z. Zhou, Q. Zhang, C. Su and N. Zhou. How do brand

communities generate brand relationships? Intermediate

mechanisms. Journal of Business Research, vol. 65, pp. 890-

895, 2012.

[15] K. Z. Zhang, M. Benyoucef and S. J. Zhao. Building brand

loyalty in social commerce: The case of brand

microblogs. Electronic Commerce Research and Applications,

vol. 15, pp. 14-25, 2016.

[16] R. U. Mustafa, M. S. Nawaz, M. I. Lali, T. Zia and W.

Mehmood. Predicting the cricket match outcome using crowd

opinions on social networks: A comparative study of machine

learning methods. Malaysian Journal of Computer Science,

vol. 30, pp. 63-76, 2017.

[17] M. I. Lali, R. U. Mustafa, K. Saleem, M. S. Nawaz, T. Zia and

B. Shahzad. Finding healthcare issues with search engine

queries and social network data. International Journal on

Semantic Web and Information Systems, vol. 13, pp. 48-62,

2017.

[18] P. Blackshaw and M. Nazzaro. Word-of-mouth in the age of

the web-fortified consumer. In Consumer-Generated Media

(GCM), 101, 2006.

[19] R. Flagler. Are You Social? Marketing your Business with

Facebook and Twitter. E-Marketing Newsletter, vol. 102, pp.

6, 2011.

[20] Y. Chen, S. Fay and Q. Wang. The role of marketing in social

media: How online consumer reviews evolve. Journal of

International Marketing, vol. 25, pp. 85-94, 2011.

[21] M. Laroche, M. R. Habibi, M. O. Richard and R.

Sankaranarayanan. The effects of social media based brand

communities on brand community markers, value creation

practices, brand trust and brand loyalty. Computers in Human

Behavior, vol. 28, pp. 1755-1767, 2011.

[22] M. Laroche M. R. Habibi and M. O. Richard. To be or not to

be in social media: How brand loyalty is affected by social

media? International Journal of Information Management,

vol. 33, pp. 76-82, 2013.

[23] J. Munnukka, H. Karjaluoto and A. Tikkanen. Are Facebook

brand community members truly loyal to the

brand? Computers in Human Behavior, vol. 51, pp. 429-439,

2015.

[24] L. Dessart C. Veloutsou and A. Morgan-Thomas. Consumer

engagement in online brand communities: A social media

perspective. Journal of Product and Brand Management, vol.

24, pp. 28-42, 2015.

[25] P. Barwise and S. Meehan. The one thing you must get right

when building a brand. Harvard Business Review, vol. 88, pp.

80-84, 2010.

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

[26] S. N. Nikolova. The effectiveness of social media in the

formation of positive brand attitude for the different users.

Master’s Thesis, University of Amstradem, 2012.

[27] E. Kim, Y. Sung and H. Kang. Brand followers’ retweeting

behavior on Twitter: How brand relationships influence brand

electronic word-of-mouth. Computers in Human Behavior,

vol. 37, pp. 18-25, 2015.

[28] B. J. Jansen, M. Zhang, K. Sobel and A. Chowdury. Twitter

power: Tweets as electronic word of mouth. Journal of

American Society for Information Science and Technology,

vol. 60, pp. 2169-2188, 2009.

[29] C. M. Chung and P. R. Darke. The consumer as advocate: Self-

relevance, culture, and word-of-mouth. Marketing Letters, vol.

17, pp. 269-279, 2006.

[30] T. A. Shimp, S. L. Wood and L. Smarandescu. Self-generated

advertisements: Testimonials and the perils of consumer

exaggeration. Journal of Advances Research, vol. 47, pp. 453-

461, 2007.

[31] E. S. Kwon, E. Kim, Y. Sung and C. Y. Yoo. Brand followers:

Consumer motivation and attitudes toward brand

communications on Twitter. International Journal of

Advertising, vol. 33, pp. 657-680, 2014.

[32] H. Edman. Twittering to the top: A content analysis of

corporate tweets to measure organization-public relationships.

PhD Dissertation, Louisiana State University, USA, 2010.

[33] R. U. Mustafa, M. S. Nawaz, J. Ferzund, M. I. Lali, B.

Shahzad, P. F. Viger. Early detection of controversial urdu

speeches from social media. Data Science and Pattern

Recognition, vol. 1, pp. 26-42, 2017.

[34] J. Gallaugher and Sam Ransbotham. Social Media and

customer dialog management at Starbucks. MIS Quarterly

Executive, vol. 9, pp. 197-212, 2010.

[35] A. H. Zadeh and R. Sharda. Modeling brand post popularity

dynamics in online social networks. Decision Support

Systems, vol. 65, pp. 59-68, 2014.

[36] L. de Vries, S. Gensler and P. S. H. Leeflang. Popularity of

posts on brand fan pages: An investigation of the Effects of

social media marketing. Journal of Interactive Marketing, vol.

26, pp. 83-91, 2012.

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

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

View publication statsView publication stats