7/26/2019 Novel Applications of Social Media Analytics

1/3

Guest Editorial

Novel

applications

of

social

media

analytics

Social media is continuously growing in an astonishing speed

[4]. Online social media platforms, e.g., Facebook, Twitter, Youtube,

Weibo,

many

discussion

forums,

have

developed

into

a

virtual

world

where

users

and

firms

share

contents

related

to

their

real

lives, works, and various facets of the real world. Not only the end

users are embracing the new technology, but also the firms,

corporations,

state

and

government

agencies

are

adopting

this

at

avery fast pace [8].

The explosion of social media usage have resulted in massive

user generated contents (UGCs), including media contents, user

information

and

their

interactions

and

social

networks,

geo-

locations, and many other metadata values [5,6]. These UGCs have

created numerous new research opportunities and challenges.

Social media analytics (SMA) is concerned with developing and

evaluating informatics tools and frameworks to collect, monitor,

analyze, summarize, and visualize social media data to facilitate

conversations and interactions to extract useful patterns and

intelligence [7]. Social media analytics generally involve three

stage processes: capture, understand, and present [4]. Recently,

more and more research efforts have been dedicated to key issues

therein,

such

as

analytics

and

learning

techniques

towardunderstanding social media, social media analytics tools and

systems, knowledge mining from social media, as well as social

network modeling, etc.

This special issue seeks contributions reporting novel solutions,

models, theories, or systems regarding social media analytics.

Topics of interest include but not limited to:

Understanding social content and dynamics.

Understanding firm usage of social media.

Efficient

learning

algorithms

for

scalable

social

media

analytics.

Social network modeling using social media data.

Machine learning and data mining for social media.

User interests and behavior modeling in social media.

Tagging,

semantic

annotation,

object

and

event

recognition

on

large-scale social media collections.

Novel data processing to remove noise and extract useful signals.

Effective search mechanism in large-scale social media collec-

tions.

Novel business applications and value discovery using social

media analytics.

After

the

call

for

papers

was

issued,

we

received

many

quality

submissions. After many rounds of review, the following 11 papers

stand out and get accepted into this special issue. They cover a

wide

range

of

topics

and

use

a

variety

of

social

media

data

sets.

Understanding News 2.0: a Framework for Explaining the

Number of Comments from Readers on Online News, by Qian Liu,

Mi

Zhou,

Xin

Zhao,

try

to

explain

the

user

comment

popularity

using

SMA

in

the

News

2.0

arena.

They

believe

that

the

number

of

comments can indicate the influence of online news, which brings

potential social value and economic benefits. They propose a

framework

that

involves

integrating

the

features

of

newsstructure, news content, and reader usage (social media recom-

mendation) to explain the number of comments. The results of

logistic regression suggest that the proposed framework is a

powerful

tool

for

explaining

the

number

of

comments

(R2

=

47.1%).

The relative and mediating role of recommendation in social media

from readers is also explored.

Emotion Recognition and Affective Computing on Vocal Social

Media, by Weihui Dai, Dongmei Han, Yonghui Dai, Dongrong Xu,

proposes a computational method for emotion recognition and

affective computing on vocal social media to estimate the complex

emotion as well as its dynamic changes in a three dimensional PAD

(Position-Arousal-Dominance) space. They claim that vocal media

is conveying semantic information, vocal message, as well as

abundant

emotional

information

at

the

same

time.

Vocal

mediahas become a popular way of communication in todays social

networks. They further analyze the propagation characteristics of

emotions on the vocal social media using a Wechat vocal dataset.

Personalized Recommendation Based on Time-Weighted

Overlapping Community Detection, by Haoyuan Feng, Jin Tian,

Harry Jiannan Wang, Minqiang Li, try to understand users interests

using SMA for personalized recommendation. They claim that

users in social media sites often belong to multiple interest

communities and their interests are constantly changing over time.

Therefore, modeling and predicting dynamic user interests poses

great challenges to personalized recommendation in social media

analytics research. They propose a novel solution to this research

problem by developing a temporal overlapping community

detection method based on time-weighted association rule

mining. They conducted experiments using MovieLens and Netflix

datasets, and their experimental results show that their proposed

approach outperforms several existing methods in recommenda-

tion performance.

A Novel Social Media Competitive Analytics Framework with

Sentiment Benchmarks, by Wu He, Harris Wu, Gongjun Yan,

Vasudeva Akula, Jiancheng Shen, present a social media competi-

tive

analytics

framework

with

sentiment

benchmarks

that

can

be

used

to

glean

industry-specific

marketing

intelligence.

Based

on

the idea of the proposed framework, new social media competitive

analytics with sentiment benchmarks can be developed to enhance

Information & Management 52 (2015) 761763

Contents

lists

available

at

ScienceDirect

Information & Management

journal homepage: www.elsevier .co m/loc ate / im

http://dx.doi.org/10.1016/j.im.2015.07.007

0378-7206/ 2015 Published by Elsevier B.V.

http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://www.sciencedirect.com/science/journal/03787206http://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://www.elsevier.com/locate/imhttp://dx.doi.org/10.1016/j.im.2015.07.007http://dx.doi.org/10.1016/j.im.2015.07.007http://www.elsevier.com/locate/imhttp://www.sciencedirect.com/science/journal/03787206http://dx.doi.org/10.1016/j.im.2015.07.007http://crossmark.crossref.org/dialog/?doi=10.1016/j.im.2015.07.007&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.im.2015.07.007&domain=pdf

7/26/2019 Novel Applications of Social Media Analytics

2/3

marketing intelligence and to identify specific actionable areas in

which

businesses

are

leading

and

lagging

to

further

improve

their

customers experience using customer opinions gleaned from

social media. An innovative business-driven social media compet-

itive analytics tool named VOZIQ is developed based on their

proposed

framework.

They

demonstrate

the

usefulness

of

the

VOZIQ

tool

by

analyzing

tweets

associated

with

five

large

retail

sector companies.

The Deeper, the Better? Effect of Online Brand Community

Activity

on

Customer

Purchase

Frequency,

by

Ji

Wu,

Liqiang

Huang,

Jianliang

Leon

Zhao,

Zhongsheng

Hua,

study

a

problem

on

how to determine customer purchase frequency based on the level

of activity in an online brand community. They believe that there is

a

great

need

to

reconcile

the

mixed

findings

obtained

so

far

in

this

area.

Drawing

on

regulatory

focus

theory,

they

hypothesize

that

the effect of community participation may be contingent on

participators goal-pursuit focus (prevention or promotion). Their

further

analysis

of

customer

blog

data

in

a

company-sponsored

community

together

with

transactional

data

of

the

same

company

demonstrates that deep community participation among promo-

tion-focused customers significantly increases purchase frequen-

cy;

however,

deep

participation

has

a

very

different

effect

among

prevention-focused

customers.

Effectiveness of Corporate Social Media Activities to IncreaseRelational Outcomes, by Marten Risius, Roman Beck, applies

social

media

analytics

to

investigate

the

impact

of

different

corporate

social

media

activities

on

users

word

of

mouth

and

attitudinal loyalty. They conduct a multilevel analysis of approxi-

mately 5 million tweets regarding the main Twitter accounts of

28

large

global

companies.

They

empirically

identify

different

social

media

activities

in

terms

of

social

media

management

strategies (using social media management tools or the web-

frontend client), account types (broadcasting or receiving infor-

mation),

and

communicative

approaches

(conversational

or

disseminative). They also find positive effects of social media

management tools, broadcasting accounts, and conversational

communication

on

public

perception.

Why Users Contribute Knowledge to Online Communities: AnEmpirical Study of an Online Social Q&A Community, byJiahuaJin,

Yijun Li, Xiaojia Zhong, Li Zhai, studies why users continuously

contribute

knowledge

to

online

social

Q&A

communities

based

on

social

capital

theory,

social

exchange

theory,

and

social

cognitive

theory. Empirical panel count data was collected from a popular

Chinese online social Q&A community. The results from a negative

binomial

regression

model

with

user

fixed

effects

indicate

that

a

users

self-presentation,

peer

recognition,

and

social

learning

have

positive impact on his/her knowledge contribution behaviors.

Their findings can help guide the development and operation of

online

social

Q&A

communities.

EXPRS:

An

Extended

Pagerank

Method

for

Product

Feature

Extraction from Online Consumer Reviews, by Zhijun Yan,

Meiming

Xing,

Dongsong

Zhang,

Baizhang

Ma,

studies

methodsto

extract

useful

features

from

online

reviews.

They

believe

that

online

consumer

product

reviews

are

a

main

source

for

consumers

to obtain product information and reduce product uncertainty

before making a purchase decision. However, the great volume of

product

reviews

makes

it

tedious

and

ineffective

for

consumers

to

peruse

individual

reviews

one

by

one

and

search

for

comments

on

specific product features of their interest. Their study proposes a

novel method called EXPRS that integrates an extended PageRank

algorithm,

synonym

expansion,

and

implicit

feature

inference

to

extract

product

features

automatically.

The

empirical

evaluation

using consumer reviews on three different products shows that

EXPRS is more effective than two baseline methods.

Subjective

Well-being

Measurement

based

on

Chinese

Grass-

roots

Blog

Text

Sentiment

Analysis,

by

Jiayin

Qi,

Xiangling

Fu,

Ge

Zhu, proposes a new method to measure the subjective well-being

(SWB)

of

Chinese

people.

Based

upon

the

classic

framework

in

psychology, their model constructs a system of multiple weighted

emotions in positive and negative affect by applying text

sentiment analysis. They study SWB in the Chinese context. They

establish

and

supplement

their

model

with

a

new

lexicon,

Ren-

CECps-SWB

2.0.

Their

tests

on

a

blog

data

set

from

Sina.com

demonstrate the validity of their model. They also find some

interesting patterns of the SWB of Chinese people on a weekly and

monthly

basis.

Reading

Behavior

on

Intra-organizational

Blogging

Systems:

A

Group-level Analysis through the Lens of Social Capital Theory, by

Naichen Li, Xunhua Guo, Guoqing Chen, Nianlong Luo, aims to

explore

the

factors

that

potentially

determine

the

continued

reading

behavior

of

users

on

intra-organizational

blogging

systems. They propose a group-level model that consists of

constructs regarding structural, relational, and cognitive social

capital.

The

model

is

empirically

tested

using

system

record

data

collected

from

a

large

telecommunications

company.

Their

results

illustrate that social capital factors have significant impacts on

continued reading behavior. However, part of their influence is

subject

to

the

moderation

effects

of

workgroup

characteristics.

Their

study

and

its

findings

contribute

to

the

literature

on

intra-

organizational social networking.An Empirical Analysis of Users Privacy Disclosure Behaviors

on

Social

Network

Site,

by

Kai

Li,

Zhangxi

Lin,

Xiaowen

Wang,

examines

users

privacy

disclosure

behavior

via

SMA.

They

believe

users privacy on social network sites is one of the most important

and urgent issues in both industry and academic fields. They

investigate

the

effect

of

users

demographics,

social

network

site

experience,

personal

social

network

size,

and

blogging

productivi-

ty on privacy disclosure behaviors by analyzing the data collected

from social network sites. Their results show that males and

females

have

significantly

differentiated

privacy

disclosure

pat-

terns in the dimensions of disclosing breadth and depth. In

addition, age has negative and significant relationships with

disclosing

breadth,

disclosing

depth,

and

high

sensitive

disclosure.

We hope the collection of papers in this special issue on socialmedia analytics will spark more interests and follow-up work in

this exciting research area.

References

[1] A.S. Abrahams, J. Jiao, G.A. Wang, W. Fan, Vehicle defect discovery from socialmedia, Decis. Support Syst. 54 (1), 2012, pp. 8797.

[2] A.S. Abrahams, J. Jiao,W. Fan, G.A. Wang, Z. Zhang,What is buzzing in theblizzardof buzz: automotive component isolation in social media postings, Decis. SupportSyst. 55 (4), 2013, pp. 871882.

[3] A.S. Abrahams, W. Fan, J. Jiao, G.A. Wang, Z. Zhang, An integrated text analyticframework for product defect discovery, Prod. Oper. Manag. 24 (6), 2015, pp.975990.

[4] W. Fan,M.D. Gordon, The power of social media analytics, Commun. ACM 57 (6),2014, pp. 7481.

[5] H. Lin, W. Fan, P. Chau, Determinants of users continuance of social networking

sites:

a self-regulation perspective, Inf. Manag. 51 (5), 2014, pp. 595603.[6] G.A. Wang, J. Jiao, A.S. Abrahams, W. Fan, Z. Zhang, ExpertRank: a topic-awareexpert finding algorithm for online knowledge communities, Decis. Support Syst.54 (3), 2013, pp. 14421451.

[7] D. Zeng, H. Chen, R. Lusch, S.-H. Li, Social media analytics and intelligence, IEEEIntell. Syst. 25 (6), 2010.

[8] Mi (Jamie) Zhou, Lijun (Gillian) Lei, Jianling Wang, Weiguo Fan, Alan G. Wang,Social Media Adoption andCorporate Disclosure, J. Inf. Syst.:Summer 2015 29 (2),2015, pp. 2350.

Weiguo Fan, L. Mahlon Harrell fellow, is a professor of accounting and information

systems, professor of computer science (courtesy) and director of the Center for

Business Intelligence and Analytics at Virginia Tech. He received his Ph.D. in business

administration from the Ross School of Business, University of Michigan, Ann Arbor, in

2002, a M.Sc in computer science from the National University of Singapore in 1997,

and a B.E. in Information and Control Engineering from the XianJiaotong University,

P.R. China, in 1995. His research interests focus on the design and development of

novel

information

technologies

Big

data,

social

media

analytics,

information

Editorial/ Information & Management 52 (2015) 761763762

http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0080http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0075http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0070http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0065http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0060http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0055http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0050http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045http://refhub.elsevier.com/S0378-7206(15)00075-0/sbref0045

7/26/2019 Novel Applications of Social Media Analytics

3/3

retrieval, data mining, text/web mining, business intelligence and analytics

techniques to support better business information management and decision

making. He has published more than 150 refereedjournal and conference papers. His

research has appeared in many elite journals such as Information Systems Research,

Journal of Management Information Systems, Production and Operations Management,

IEEE Transactions on Knowledge and Data Engineering, Information Systems, Commu-

nications of theACM, Information and Management, InternationalJournal of Production

Research, Journal of the American Society on Information Science and Technology,

Information ProcessingandManagement, Decision SupportSystems, ACMTransactions on

Internet Technology, Pattern Recognition, IEEE Intelligent Systems, Information Sciences,

Journal of Informetrics . His research on product (including vehicles, and consumer

electronics) defect discovery from social media [13] has been well cited and featured

in numerous news media, including New York Times.

Xiangbin Yan, is a professor and department head of Management Science &

Engineering in the School of Management at Harbin Institute of Technology (HIT),

P.R. China. He also serves as associate dean of the Institute of Economics,

Management, Humanities, and Social Sciences at Harbin Institute of Technology. He

received his Ph.D. in Management Science & Engineering from Harbin Institute of

Technology, and a M.Sc and B.E. in Mechanical Engineering from Harbin Institute of

Technology, in 1995. He has been a visiting research scholar in MIS Department at

the University of Arizona from 2008 to 2009, and 2014. His research interests

include electronic commerce, social media analytics, social network analysis, and

business intelligence. His research has appeared in many mainstreamjournals, such

asJournal of Informetrics, Computers in Human Behaviors, Scientometrics, Information

Systems Frontiers, Journal of Homeland Security and Emergency Management, and

PhysicaA: Statistical Mechanics and itsApplications. His research has been funded by

several large Chinese NSF grants.

Weiguo Fan

Virginia

Tech,

USA

Xiangbin Yan

Harbin Institute of Technology, PR China

Available

online

17

July

2015

Editorial/ Information & Management 52 (2015) 761763 763