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MYU Tokyo
S & M 1829

Analysis of Differences in User Behavior
Based on a Variety of Online Social Networks
Fenglian Jiang,* Wencan Tong, and Liming Huang
School of Mathematics and Information Engineering, Longyan University, Longyan City, China(Received November 23, 2018; accepted February 19, 2019)

Keywords: social network, user behavior, mass data

The development of the Internet and various communication technologies has led to novel
innovations, with a variety of social networks and smart terminals prevailing, which has createda vast amount of real data for analyzing and predicting user behaviors. Here, we will analyze
and compare multiple online social networks and related user behaviors, giving an introduction
to important research from related fields. We will create a model for the analysis of differencesin user behavior based on a comparison of multiple online social networks. Differences in user
behavior will also be shown on the basis of this model.

1.

Introduction

The term “online social networks” refers to social service networks that are based onbuilding interpersonal relationships and are propagated over the Internet. Facebook, which
went online in 2004 and offers online social services based on real interpersonal relationships,
is considered to be the first online social network. With Facebook as the representative, social
networks have rapidly developed, and an increasing number of social network platforms are
emerging. At the same time, the number of people joining social networks is growing. By
2012, Facebook had more than 1 billion registered users and Sina Weibo had more than 0.3
billion users, which means that social networks have entered people’s everyday lives.
Nowadays, the most popular social networks include Facebook, Twitter, Line, QQ, Weibo,
and Zhihu. After several years of development, these platforms all offer various functions whileessentially progressing toward comprehensive platforms. However, in reality, each platform has
its own limitations in terms of service functions, regardless of the perspectives of the users orservice providers. This means that for a typical user, it is likely that he or she will register withseveral social networks at the same time to meet different requirements in different situations.
There is much research value in the coexistence of these various platforms. For example,
recent studies on the behaviors of social network users and information propagation havefocused on Facebook and Weibo, with comparisons of social networks having different media
and vertical characteristics being neglected. Furthermore, analyzing the differences in user
behavior in various social networks such as Weibo, Tieba, Zhihu, and Wechat is expected to bea future hot topic.
*

Corresponding author: e-mail: jfl6868@126.com
https://doi.org/10.18494/SAM.2019.2222
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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In the rest of this paper, we will compare various social networks and analyze differencesin user behavior between these different social networks. Recent important research in related
fields will be introduced in Sect. 2. Various online social networks will be compared in
Sect. 3. A model for analyzing differences in user behavior in various social networks will bepresented in Sect. 4. A simulation experiment will be discussed in Sect. 5, and the paper will beconcluded in Sect. 6.

2.

Related Research

Mobile social networks have grown from nonexistent to ubiquitous in just over a decade.
With this rapid development, many academics have become interested in research on social
networks. The studies on social networks, both domestic and abroad, which have been carriedout to date, can be divided into several aspects: personalized searches, precision marketing,friend recommendations, user behavior analysis, and research on influence and social graphs.(1–4)
Precision marketing: Advertisements are the main source of revenue for Internet companies,
with a large share of Internet companies’ profits based on ads. Thus far, these ads havebeen randomly inserted into various media with very low efficiency. This has created a lotof opportunities for companies to research the targeting of ads, and this is what is meant by
precision marketing.
Friend recommendations: Friendship is a crucial part of social networks. If a site has a poor
friendship recommendation function, the user experience is likely to be unsatisfactory, whichmeans that friend recommendation is an important application for social network sites. To
date, considerable progress has been achieved on friend recommendation algorithms in social
networks, such as friend recommendations based on association rules and those based oncollaborative filtering.
User behavior analysis: According to recent research, user behaviors are not random but
based on many designated models. For example, when we browse e-commerce sites, there
must be a shopping cart provided for customers to store what they will buy, which means thatshopping cart analysis is the core data analysis business model for many e-commerce sites.
Kinds of example are persuasive if given, and user behavior analysis aims to identify certain
user habits through data mining algorithms.(3,6)
Research on influence and social graphs: Influence may have various forms of proposal or
dominance, which depends on the structural type of the social network and shows different
forms and strengths. Scholars have applied algorithms from other fields to study influence insocial networks. For example, Facebook has put resources into social graphs and social network
structures, while Sina Weibo’s main social network characteristic is hobby graphs, and studies
by Sina Weibo include the development of a social network user hobby model.
A substantial number of algorithms are based on the analysis of user behavior in social
networks, and preference predictions are based on predicting the preference relationships ofinterusers. First, they calculate preference similarity based on intersocial users, then pick usersthat most closely approximate the targeted users, called “nearest-neighbor users”. They thenuse the behavior preferences of these nearest-neighbor users as the benchmarks for targetedSensors and Materials, Vol. 31, No. 3 (2019)983

predictors of user behavior, with different approximation strengths weighed as the behavior
preference trends of the targeted user. Thus far, there have been many computations on
similarity, including Pearson’s correlation coefficient and the mean squared error.(1–4) Social
networks can be substantially described as graphs, so prediction algorithms based on a graphmodel are presented here.(5–8)

3.

Comparison of Online Social Networks

Online social network platforms have matured after being developed for over a decade,with each platform becoming more professional and comprehensive. Each popular social
network platform has its own irreplaceable business model allowing platforms to coexist witheach other, although they may have services overlapping with other platforms. We need to list
the characteristics of each online social network platform to make a comparison, with an eye
toward the differences between these characteristics.
3.1

Characteristics of online social networks

Here, we introduce various functions and meanings of social network platforms.
Social: Social refers to interpersonal services, and the fundamental role of each social
network is to build human relationships. Although every social network provides basic humanrelationship creation services, each platform has its own focus.
For online social platforms such as Facebook and Renren, social services are the mostimportant. These platforms are motivated to encourage real-name social activities, with manysocial functions such as adding common friends and those with a common focus.
However, information-publishing platforms such as Weibo and Twitter pay more attention
to media functions. Although every social network has improved its social services over time,
when users want to enjoy intimate social activities with their friends, they have their own
personal preferences.(9,10)
Medium: Medium refers to the characteristics that social networks have to make and publish
valuable content. The creation of social networks has given many opportunities to network
providers, such as media for publishing platforms and new media technology. Social networks
serve as important avenues for individuals and organizations to publish information, share theiropinions, and carry out marketing, with their medium characteristics increasingly being more
focused on.
Platforms such as Sina Weibo and Twitter are social networks with strong medium
characteristics. Compared with communications between private individuals, opinion leaders
in social networks have a widespread influence on information propagation among netizens,and may even accidentally use their influence in reality. The salt snapping event in 2011 is one
such negative result of social networks with medium characteristics.
Instancy: With online social networks serving as platforms for deepening humanrelationships, their basic services allow individual communication. In the case of instant apps
such as QQ, Line, and Wechat, user communication is the foundation of their models.

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Instancy refers to the ability of platforms to provide real-time communications. Although
every social network provides some kind of real-time communication service, QQ, Wechat,and Line are the mainstream players in the market owing to their high quality and massive userbases.
Community: A community provides multi-to-multi services, where users having common
interests can form groups to communicate about certain topics, which have become importantparts of social networks. This is in complete contrast to individual-to-individual communication
services or blog services.
Community characteristics refer to the multi-to-multi communication services that social
networks can provide. Forums and groups are representatives of community services. They
have the characteristics of user stickiness while improving group communications.
Multimedia character: Videos and images have long been inseparable parts of social
networks, and the multimedia characteristic refers to the ability of social networks to provide
multimedia services.
Although each social platform tries to enrich its products via multimedia services, social
networks aimed at single types of media have also become popular, such as the image social
networks Instagram and Flickr, the music social network Last.fm, and the video social network
YouTube, which all have more than 0.1 billion users.
Verticality: Verticality refers to the characteristics of profession and convenience, which are
aimed at satisfying the particular requirement of minorities. Social networks with verticality
characteristics can provide professional and personal services, in which they try to do businessin segmented fields.
Some well-known social networks with verticality characteristics include Tiexue, which
offers military information, MOMO, which aims to provide social service between strangers,
and social networks focused on knowledge such as Quora, Zhihu, and Douban. They allachieve designated crowd support for their professional and personal services.
3.2

Well-known social networks used for comparison

After listing the six characteristics of representative online social networks, we canobserve the differences between these social networks through comparisons based on thesesix dimensions. In Table 1, we will attempt to compare these social networks on the basis ofBusiness Insider’s global 10 social network rankings.
We can easily find differences between social networks through comparisons of these six
dimensions. Next, we will discuss the relationship between social network and user behavior
differences.

4.

Model Used for Analysis of Differences in User Behaviors in Online Social
Networks

The infrastructure of social network deployment is based on relationships among users, andthe active users who lead interactive behaviors in social networks are the core part of each social

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Table 1
Popular social networks for comparisons.
Site
SocialMediumFacebook
StrongStrongYouTubeWeak
StrongQQ ZoneStrongMiddleSina Weibo
MiddleStrongWhatsApp
Weak
Weak
Google+MiddleMiddleTumblrMiddleMiddleLine
MiddleWeak
Twitter
MiddleStrongWechatStrongMiddle985

Instancy
MediumWeak
StrongMiddleStrongMiddleMiddleStrongMiddleStrongCommunity
StrongWeak
MiddleWeak
Weak
StrongWeak
MiddleWeak
MiddleMultimedia
MiddleStrongMiddleMiddleWeak
MiddleMiddleWeak
MiddleMiddleVerticality
Weak
Strong
Weak
Weak
Weak
MiddleMiddle
Weak
Weak
Weak

network. Analyzing user behaviors will lead to a better understanding of social networks, help
improve social network platform interface designs, and aid research on the mutual activities ofsocieties.
To study user behaviors, we need to focus on users in a variety of social network platforms.
As every platform provides different services, we need a united standard of user behavior todesign a benchmark based on the basic interactive behaviors of users. Thus, we need to discuss
what behaviors users exhibit before creating the model.
4.1

Common user behaviors

Online social network user behaviors can be divided into two categories, explicit and implicit
behaviors, and the differences between them should be analyzed if explicit interactive data
exist. Explicit behaviors include messages and comments that can be seen by visitors, while
implicit behaviors do not leave any explicit messages, with visiting homepages being a typicalexample. Both can be sensed through website mouse click stream data. Thus, here, we defineuser behaviors as at least one HTTP request activity in mouse click stream data, with Facebook
as an example, and the 40 common online social network user behaviors listed in Table 2.(11)User manipulations on online social network platforms through mouse clicks can besummarized into the 40 behaviors in Table 2. Although the exact names or concepts for user
behaviors may differ across the various platforms, all the behaviors can be found in Table 2.
To reduce the variation so as to facilitate model creation while setting a standard for various
platforms, these 40 items will be integrated.
4.2

Integration of user behaviors  

We divide user behaviors into nine categories based on object manipulation: Search, Post,
Private Message, Message, Video, Image, Management, Community, and Configuration. Eachcan be defined as follows:986

Table 2
List of user behaviors.
User behavior
General search
Visit a post
Edit a post
Browse a private
message
Edit a private
message
Browsecomments

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Description
Information search by
keywords
Browse own orfriend’s post
Edit a new post
Browse messages
received

User behavior
Browse a friend
updateBrowse community
members
Edit a moment
Browse a user list

Description
Browse friend’s
update moments
Browse members offriend’s community
Edit own moment
Browse a list of
strangers
Management of users
except friends

Edit private messages

User management

Browse comments
left by friends
Edit a message
for friends
Browse messages
read
Browse arecommended video list

Browse acommunity
Browse community
comments
Join/leave acommunity
Browse community
members

Browse a video

Browse a video of interest

Browse a topic list

Browse a photo
album list
Browse a photo
album
Browse animage
Browse tagged
imagesBrowse image
comments

Browse an available
photo album list

Join in a topic discussion

Browse a photo album

Community management

Browse an image

Vote

Vote for a community

Browse tagged images
annotated by self or friends

Browse a community
list
Community management
invitation

Browse communities
joinedApply for or accept a
community invitation
Join in a community
activity
Visit an application
the platform offers

Edit a message
Browse a pastmessage
Browse video list

Edit a photo
Browse states
Browse ahomepage
Browse friend
listManage friend
invitation

Browse image comments
Edit a published photo
Browse own orfriend’s states
Browse own orfriend’s homepage

Community activities
Visit applications

Browse a community
Browse community
comments
Join or leave acommunity
Browse a community
members
Browse community
topic list
Post in a community
topic
Community
management

User configuration

User configuration

Browse own friend list

Spam/verification code

Browse spam

Accept a friend request

User log in or log out

User log in or log out

Search: Search refers to a general search, for providing a post, moment search, community,
topic search. Users can find what they need by searching, for example, targeted users,
communities, posts, images, and videos.
Post: A post has a different name on each social platform. It is called a post in Facebook, a
Weibo in Sina Weibo, a Tweet in Twitter, and a Shuoshuo in QQ. In general, a post is publicly
published by users and can be seen by anybody (who has reading authorization), in contrast
to a private message or email. Posting is the basic way for users to show who they are and to
express themselves; of course, it has the most important position on social platforms.

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Private Message: Private messaging refers to the exchange of private information between
individuals. Private messages dominate instant message social networks, such as QQ, Wechat,and Line, and have auxiliary functions in forums, which are focused on communities.
Message: Messaging includes leaving a message for friends and responding to friends’comments, and it is a major component of public behavior. Messages are created by users
and sent to each other, but are open to others, although the owners can also set viewing
authorizations.
Video and Image: Videos and images include all multimedia contents. Similarly to text,
multimedia are open to everyone, can be tagged by others, and authorizations can be set.
Management: Management includes user management, friend management, and friend
visit management. Unlike posts, private messages, and messages, which all have their own
publishing objects, moments have no publishing objects, but provide other user information,such as user homepages, signatures, profiles, visit logs, and geographical positions. Moments
can be public to others on the basis of authorizations. User management includes friend
applications, requests, and deletions while editing and visiting.
Community: Communities play an important role in social networks. Registered membersof a community can set a topic, exchange information with each other, set a vote or question,or establish games within communities. Common communities include forums and QQ/Line/
Wechat groups.
Configuration: User configuration refers to all behaviors related to the configuration byusers on social networks, including the use of applications, spam shielding and deletion, andlogging in/out
We can create a model to analyze the differences between user behaviors on multiple social
network platforms on the basis of the nine characteristics mentioned above, with user behaviors
compared across the different social network platforms.
4.3

Model used to analyze differences in user behavior

We can create a model to analyze differences in user behavior through the multivariate
analysis of variance (MANOVA) using the categories listed above.
Imagine we have mouse click stream data sets from social networks A, B, C, and D, andrandomly pick 200 users from each set. The user behavior parameters are shown in Table 3.
One month parameter data are shown in Table 4. Different times parameter coefficients areshown in Table 5. Then, we calculate the cumulative counts of eachbehavior across one week.
First, we calculate the total variance as follows before creating a model of difference analysis
by MANOVA:∑∑ (Yij − GM ) = n∑ (Y j − GM ) + ∑∑ (Yij − | Y j ) .
2

2

i

j

j

2

i

j

(1)

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Table 3User behavior parameters.
SearchText
Message
a
A1
A2
A3
b
B1
B2
B3
c
C1
C2
C3
d
D1
D2
D3

Testimonial
A4
B4
C4
D4

VideosA5
B5
C5
D5

Table 4User behavior parameter data (one month).
Search Text Message Testimonial Videos
a
59
38
89
40
55
b
88
45
90
60
45
c
109
56
130
78
67
d
78
28
38
45
38

PhotosA6
B6
C6
D6Manage Communities
A7
A8
B7
B8
C7
C8
D7
D8

Setting
A9
B9
C9
D9

Photos Manage Communities Setting Average Total variance
67
43
98
34
58.11
21.54
78
65
76
25
63.56
20.68
98
76
98
14
80.67
31.81
65
23
34
24
41.44
17.66

Table 5User behavior parameter coefficients (different times).
Average Average Average Average Total variance Total variance Total variance Total variance(30 d)(60 d)(90 d)(120 d)(30 d)(60 d)(90 d)(120 d)
a
58.11
60.12
62.51
60.52
21.54
22.82
22.02
22.85
b
63.56
70.35
72.51
75.12
20.68
23.41
22.64
23.01
c
80.67
75.62
86.71
85.12
31.81
30.21
30.55
30.12
d
41.44
35.42
38.66
40.12
17.66
18.12
17.52
18.52

Then, we calculate the interclass variance:22

nkm ∑∑ ( DTkm − GM )= nk ∑ ( Dk − GM ) + nm ∑ (Tm − GM )
k

m

k

2

m


2
2
2
+  nkm ∑∑ ( DTkm − GM ) − nk ∑ ( Dk − GM ) − nm ∑ (Tm − GM )  .
k m
k
m



(2)

We replace DT with function Y to calculate22∑∑∑ (Yikm − GM )= nk ∑ ( Dk − GM ) + nm ∑ (Tm − GM )
i

k

m

k

2

m


2
2
2
+  nkm ∑∑ ( DTkm − GM ) − nk ∑ ( Dk − GM ) − nm ∑ (Tm − GM ) 
k m
k
m


2

+ ∑∑∑ (Yikm − DTkm ) .
i

k

m

(3)

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989

Then, we calculate the significance coefficient lambda（∧）as∧=SerrorSeffect + Serror.

(4)

Finally, we list the significantly different coefficients of the model in Table 6. We can findthe specific difference variances of each website from these coefficients.

5.

Simulation Experiment and Results

Assume that A, B, C, and D are behavior data created on social platforms over one month.
The changes in the behavior of user A with time are shown in Fig. 1. We set the parameters asshown in Fig. 2 on the basis of fundamental data to analyze user differences.
In social networks, differences in user behavior habit also cause different behavior
characteristics. In Figs. 2 and 3, the behaviors of different users are shown to vary within acertain range.

Table 6Significantly different coefficients of the model.
Wilks’
Source of variancedf1
LambdaTreatment
0.13772
2.00
Disability
0.25526
4.00
Treatment by disability
0.90807
4.00

df 2
11.00
22.00
22.00

Multivariate
F34.43570**
5.38602*0.2717*p < .01. **p < .001.

Fig. 1. (Color online) Curve showing behavior
changes in behavior of user A.

Fig. 2. (Color online) Change in mean behavior ofdifferent users over time.

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Fig. 3.

6.

(Color online) Total variance of the behavior characteristic for different users over time.

Conclusions

By creating a model to compare user behaviors in social networks, in this paper, we revealthat there are relationships among user behavior characteristics based on social networks,
deployments are based on relationships among users, and the active users who lead interactive
behaviors in social networks are the cores of social networks. Analyzing user behaviors isan effective means of increasing knowledge on social networks, improving the user interface
designs of social networks, and assisting research on social interactivity. The results of thisstudy may be meaningful for future research on social-network-related models.

Acknowledgments
This work was supported by the Science and Technology Project of Longyan City (2017LY74),
Longyan University (LQ2016004), and the Youth Project of Fujian Province Education Hall
(JT180522).

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About the Authors
Feng-Lian Jiang received her B.S. degree in Computer Science from Fujian
Normal University in 1992 and her M.S. degree in Computer Science in2009. She teaches in the School of Mathematics and Information Engineeringof Longyan University, which is based in Longyan, Fujian, China. She
now serves as an associate professor and the head of the Computer Science
Department. Her main research interests include computer science, dataapplication, and network information security.
Wen-Can Tong received his B.S. degree in Computer Science andTechnology from Huaqiao University in 2011 and his M.S. degree in
Computer Application Technology in 2015. In Aug 2016, he served as alecturer and the deputy director of the Internet of Things Department of the
School of Mathematics and Information Engineering of Longyan University,
which is based in Longyan, Fujian, China. His main research interests include
computer science, network and information security, internet of things, and
WSN.
Li-Ming Huang received his B.S. degree in Software Engineering from
Jiangxi Normal University in 2011 and his M.S. degree in Computer
Technology from Xiamen University in 2014. He joined the School ofMathematics and Information Engineering of Longyan University, which isbased in Longyan, Fujian, China, in Aug 2015 and now serves as a lecturer.
His main research interests include software architecture, machine learning,and computer graphics.