Predicting user behavior over social networks

P. Anusha#1, B. Gnanakoushik #2, D. Sai Kumar #3, K. Sravan Kumar #4,

A. Venkataramana #5 #1Asst. Professor, Dept of CSE, Qis College of Engineering and Technology, Ongole.

#2, #3, #4, #5 B.Tech., Scholars, Dept of CSE,Qis College of Engineering and Technology, Ongole,

ABSTRACT:

In recent years, online social networks such as Facebook, Twitter, LinkedIn are most popular

visited sites over the internet. Presently, there is a great interest in understanding and studying the

relationships among the users in social networks. Existing link prediction methods predicts the

links based on the topological structure features and the node attribute features but overlook the

benefit other features such as clicks, shares, likes, forwards and comments could bring to any

social network. To address this gap, we propose a link prediction method based on user actions

with the post which includes clicks, shares, likes, forwards and comments. In this paper, we

propose link prediction model combining user action metrics and topological structure metrics.

The proposed metric can bridge the gap between the existing methods and propose a new metric

for defining link prediction. This is a work in progress paper, further as future direction,

implementation of the proposed metrics on standard datasets by suitably training the classifiers is

the topic ofinvestigation.

Introduction:

Online social networks such as Google plus,

LinkedIn, Facebook and Twitter have huge

number of users and the number of users

isgrowing every year [1]. Such a

potentialand diverse network of users is

attracting researchers from both academia

and industry to study and understand the

utilization of these networks for economic

and social benefits [2]. Link prediction aims

at prediction of future links between theusers

[3]. Examples of link recommendation

includes “people you may know” on

Facebook, LinkedIn etc. Members of these

social networks site use these social network

platforms to communicate with others by

posting, commenting, liking, forwarding etc.

and in turn the owners of these social

networks generate huge revenue by

displaying advertisements on the candidate

nodes (profiles) based on their

preferenceorchoice (also termed as

recommender

systems in literature, a particular case of link

prediction problem) which drives ads to

reach more users [4]. In that way one can

think that the link recommendation or link

prediction provides double folded benefits for

the users as well as for the social networks.

Existing methods in link prediction mainly

focus on the link likelihood by considering

user attributes and topological structures, and

predicts the links with the highest linkage

likelihood [5]. But they overlook the benefit

of analyzing the other existing attribute

features these social networks provide such as

clicks, shares, likes and comments etc., to

predict links. This motivates the fact to

consider these already existing features and

predict links more aptly and accurately to

recommend or predict links and there is a

need for defining new metrics apart from the

topological features of the online social

networks. A social network generates huge

data dynamicallyinafastermannerwiththe

participation of millions of users worldwide

in various forms of social networks such as

follower network, followee network, citation

network, Wikipedia network, twitter,

recommendation social network etc. These

various types of networksrequire to

categorize their users, customers in different

groups to analyze, to find out popular users

for product selling statistics for upgrading

network performance, design and so on.

This categorization of users in various

groups based on different clustering criteria,

decision rules, hidden flow and finally it

leverages some general user activity

patterns, network access pattern. These

patterns are sometimes dependent on social

network platforms, flexibility and rich

features of the social network and finally this

shapes some general user behavior. Some of

these behaviors are directly computable from

the publicly available social network data

which are persistent behavior in our

conceptualization and some which may not

be revealed out from data directly. These are

regarded as non-persistent social network

user behavior. Nonpersistent behavior can be

indirectly computed from the hidden

contents such as web server access logs,

hidden work flowetc.

User Behavior Analysis

Investigation of user behavioranalysis isan

important research area in computational

social network for various reasons.

Identification of criminal activities, anomaly

detection, information diffusion in short

times, sentiment study of larger social

groups, popularity estimation, product rating

etc. belongs to behavior analysis in social

networks. Here data are collected froma

social network to analyze user behavior both

quantitatively as well as qualitatively. Our

proposed behavior analysis architecture has

been shown in figure 1 below. A. Locus

Standy of the Survey Our survey differs

from the existing surveys in the following

ways. Like [35], we discuss sources, causes

and aspects of social network user behavior,

and also include a detailed discussion of

behavior analysis. In addition, we include a

large collection of up- to-date social network

user behavior analysis methods under the

categories of statistical, classification-based,

knowledge based, soft computing,

clustering- based etc, rather than restricting

ourselves to only statistical approaches. We

also include several important research

issues and open challenges. Like [7] and [5]

we attempt to provide a classification of

various social network user behavior

analysis methods, systems and software

tools introduced till date in addition to a

classification of social network platforms

and their characteristics. In addition, we

perform detailed comparisons among these

methods. Furthermore, like [6], we have

provided a list of research issues and open

challenges. Unlike [8] our survey is not

restricted to only social network activity

classification and analysis. It includes a large

number of up-to-date methods and analysis

approaches. A detailed discussion on types

of social network datasets, gathering and

preprocessing has been included in our

survey. However, unlike [19], we include

ideas for analysis of user behavior, in

addition to providing a list of practical

research issues and open challenges. Unlike

[23], our survey is focused on socialnetwork

user behaviour, their characterization,

recognition and detection approaches,

methods and systems, and comparisons

among them.

The Problem of User behavior Analysis

The behavior is a qualitative term which

itself is a complex concept to express in

terms of mathematical formulae. This in our

understanding may be defined as frequent

social network activities performed by a user.

More clearly, this is the most frequent pattern

of user activity in a social network. In the

real-world, communication interactions

among social network users include

interaction patterns leading to which patterns

are most influential in the whole network for

users. It is important to determine these

influential patterns in the network. These

patterns indicate the overall behavior of the

social network. This is essential for social

network in which, the interaction patterns of

densely connected actors are involved.

Therefore, we are interested to find the most

influential interaction structures in the social

network community. For a given graph G

with vertices V and edges E. We need to

identify the most frequent social network

user interaction patterns P which are

embedded in G. Each unique pattern in the

social network is considered one behavior of

users in the social network. After finding the

frequent interaction patterns of the users in

the network, we need to correlate these

patterns of users to the social network

platform to qualitatively analyze the user

behaviour in that particular social network

platform.

Our Contributions

This paper provides a structured and broad

overview of the extensive research on social

network user behavior analysis. The

majorcontributions of this survey are listed

below:

(a) User behavior in online social networks

has been well studied, but the correlation of

this behavior across these networks has not

yet been addressed clearly. We tried to

correlate our behavior analysis of social

network users in a simple manner with

examples. (b) We have categorized user

behavior analysis in two broad perspectives:

persistent & non-persistent. This is a new

way of looking at social network user

behavior analysis research. (c) Most

existing surveys do not cover all the

approaches till date, for social network user

behavior analysis, but we have covered a

wide range of approaches available in

literature up to 2016. (d) Most existing

surveys avoid characterization and

representation of social network user

behavior, which are crucial in the

behavior analysis task. We present several

techniques to characterize the behavior from

available datasets and comparethem.

(e) In addition to discussing behavioranalysis

methods, we present several wayshow to

characterize online social network user

behavior, and also present a comparison

among the various analysis. (f) We

summarize tools used in various stepsfor

social network user behavioranalysis.

(g) We also provide a description of the

datasets used for analysis. (h) Finally, we

highlight several important research issues

and challenges from both theoretical and

practical.

Behavior Characterization

Social network user behaviour

characterization is to define behaviour

mathematically in an appropriate manner.

This mathematical behaviour representation

will help in finding out the appropriate

behaviour pattern from huge social network

data sets. This is a very complex

representation as human behaviour greatly

differs from social network user behaviour

which again varies from one social network

platform to another. Same user in different

social network platforms exhibit various

activity patterns which again varies fromuser

to user that shapes the general user

behaviour. Moreover, characterization of

behaviour in a computable form to model

the general behaviour poses difficulty in

terms of processing complexity, exact data

representation for the model input and so on.

People so far have tried to define the

composition of user behaviour in terms of

set of activities, patterns, way of

participation in the social network etc.

Various methods were employed in

characterization of user behaviour in various

social network platforms. Maria Kihl et. al.

2010, in their article, “Traffic analysis and

characterization of Internet user Behaviour”,

[21] analyzed traffic measurements from a

Swedish municipal broadband access

network and derived corresponding user

behaviour models. The paper focuses on

Internet usage in terms of traffic

patterns,volumes and applications. Also,

user activity characteristics, as session

lengths and traffic rate distributions, are

analyzed and modelled. Bruno Goncalves et

al. proposed a simple model for user

behaviour that includes finite priority

queuingandtimeresourcesthatreproduces

the observed social behaviour in their

research paper “Modeling Users Activity on

Twitter Networks: Validation of Dunbars

Number”[10] in 2011. They analyzed a

dataset of Twitter conversations collected

across six months involving 1.7million

individuals and test the theoretical cognitive

limit on the number of stable social

relationships. Radoslaw Brendel and Henryk

Krawczyk in 2011, proposed a behavioural

model based on user roles in social network

in their paper, “Primary role identification in

dynamic social networks”[4]. Social

network often forms very complex

structures that additionally change over time.

Description of actor roles in such structures

requires to take into account this dynamics

reflecting behavioural characteristics of the

actors. A role can be defined as a sequence

of different types of activities. Various types

of activities are modeled by pattern sub-

graphs, whereas sequences of these activities

are modelled by sequence diagrams. For

such defined roles, a role identification

procedure is proposed to assign each actor

primary role played in a dynamic social

network. Erheng Zhong et. al. in 2012 has

investigated user behaviour in composite

manners thereby meaning behaviour of one

user in various social network platforms.

Many previous works modelled user

behaviour from only one historical user log

[4]. They observed that many people are

members of several social networks at the

same time, such as Facebook, Twitter etc.

Importantly, their behaviour and interests in

different networks influence one another.

This produces an opportunity to leverage the

knowledge of user behaviours in different

networks, in order to alleviate thedata

sparsity problem, and enhance the predictive

performance of user modeling. They

formulated the problem to model multiple

networks as composite network knowledge

transfer. They first selected the most suitable

networks inside a composite social network

via a hierarchical Bayesian model,

parameterized for individual users. Then

they build topic models for user behaviour

prediction using both the relationships in the

selected networks and related behaviour data.

Francis T. Odonovan et. al. in 2013, in their

research work, “Characterizing User

Behaviour and Information Propagation on a

Social Multimedia Network”[9] studied

users’ online activities. One such activity is

the sharing of multimedia, the popularity of

which can vary dramatically. They discussed

initial analysis of anonymized, scraped data

from consenting facebook users, together

with associated demographic and

psychological profiles. They presented five

clusters of users with common observed

online behaviours, where these users also

show correlated profile characteristics.Ryan

A. Rossi studied dynamic behaviour in

social network graphs in their work in 2013

and published a research paper “Modeling

Dynamic Behaviour in Large Evolving

Graphs”[17]. They conducted Studies to

model and characterize the temporal

behaviours of individual nodes from a given

large time-evolving graph. They have also

investigated how to model the behavioural

transition patterns of nodes. They proposed

a temporal behaviour model that captures

the roles of nodes in the graph and how they

evolve over time. According to the model,

interpretable behavioural roles are

generalizable and computationallyefficient.

Their model is for (a) identifying patterns

and trends of nodes and network states

based on the temporal behaviour, (b)

predicting future structural changes, and (c)

detecting unusual temporal behaviour

transitions. Vasanthan Raghavan et. al

developed probabilistic models for user

activity in social networks by incorporating

the social network influence as perceived by

the user and published “Modeling Temporal

Activity Patterns in Dynamic Social

Networks”[24] research paper in 2013. Qiang

Yan et. al. published “Social network based

microblog user behaviour analysis”[23] in

2013. The influence of microblog on

information transmission is becoming more

and more obvious. By characterizing the

behaviour of following and being followed

as out-degree and in-degree respectively, a

microblog social network was built in this

paper. It was found to have short diameter of

connected graphs, short average path length

and high average clustering coefficient. The

distributions of out-degree, in-degree and

total number of microblogs posted present

power-law characters. The exponent of total

number distribution of microblogs is

negatively correlated with the degree of each

user. With the increase of degree, the

exponent decreases much slower. Based on

empirical analysis, they proposed a social

network based human dynamics model in

this paper, and pointed out that inducing

drive and spontaneous drive lead to the

behaviour of posting microblogs. Zhenhua

Wang et. al. in 2014 in their research paper

“Analysis of user behaviours by mining

large network data sets”[7] studied user

behaviour model. Understanding the

intelligence of human behaviours bymining

petabytes of network data, represents the

tendency in social behaviours research and

shows great significance on Internet

application designing and service expansion.

Meanwhile, the running mobile networks

that generate huge data can be the best social

sensor for these studies. This paper

investigates a practical case of mobile

network aided social sensing which

uncovers some features of users behaviours

in mobile networks by intelligently

processing the big data. The paper studies

the users behaviours with regard to

communication, movement, and

consumption based on large user data sets.

Reza Motamedi et. al. published

“Characterizing Group- Level User

Behaviour in Major Online Social

Networks”[15] in 2014. They conducted a

detailed measurement study to characterize

and compare the group level behaviour of

users in Facebook, Twitter and Google+.

They focused to capture user behaviour with

the following metrics: user connectivity,

user activity and user reactions. Furthermore,

they conducted temporal analysis on

different aspects of user behaviour for all

groups over a two-year period. This analysis

leads to a set of useful insights including: (i)

Facebook and Google+ users express their

opinion quickly whereas Twitter users tend

to relay a received post to other users and

thus facilitate its propagation. Despite the

culture of re-share among Twitter users, a

post by a Popular Facebook user receives

more re-shares than a post by a Popular

Twitter user. (ii) Added features in an

Online Social Network (OSN) can

signicantly boost the rate of action and

reaction among its users.Vasanthan

Raghavan et. al studied temporal user

behaviour in 2014 and published “Modeling

Temporal Activity Patterns in Dynamic

Social Networks”[14]. The focus of this

work is on developing probabilistic models

for temporal activity of users in

socialnetworks (e.g., posting and tweeting)

by incorporating the social network

influence as perceived by the user. Although

prior work in this area has developed

sophisticated models for user activity, these

models either ignore social network

influence completely or incorporate it in an

implicit manner. They overcome the non-

transparency of the network in the model at

the individual scale by proposing a coupled

hidden Markov model (HMM), where each

user activity evolves according to a Markov

chain with a hidden state that is influenced

by the collective activity of the friends of the

user.

Behaviour Recognition

From various observation of social network

user data, activities, web usage content etc a

perceived underlying user behaviour can be

identified. Many people tried to reveal user

behaviour in different social network

platforms. Marcelo Maia et. al in

theirresearch paper “Identifying User

Behaviour in Online Social Networks”[20] in

2009 has described about human behaviour.

They explained how to characterize the

different classes of user behaviour.

Traditionally, user behaviour

characterization methods, based on user

individual features, are not appropriate for

online networking sites. In these

environments, users interact with the

siteandwithotherusersthroughaseriesof

multiple interfaces that let them to upload

and view content, choose friends, rank

favourite content, subscribe to users and do

many other interactions. Different

interaction patterns can be observed for

different groups of users. In this paper, they

proposed a methodology for characterizing

and identifying user behaviours in online

social networks. They used a clustering

algorithm to group users that share similar

behavioural pattern. Next, they have shown

that attributes that stem from the user social

interaction, are good discriminators and

allow the identification of relevant user

behaviours. K. R. Suneetha R.

Krishnamoorthi, in 2009 in their research

paper “Identifying User Behaviour by

Analyzing Web Server Access Log

File”[1]applied data mining techniques on

web server log data to find out web access

pattern behaviour. They explained that

Personalization for a user can be achieved

by keeping track of previously accessed

pages. These pages can be used to identify

the typical browsing behaviour of a user and

subsequently to predict desired pages. By

determining access behaviour of users,

important links can be identified to improve

the overall performance of future accesses.

The information gathered through Web

mining is evaluated by using traditional data

mining parameters such as clustering and

classification, association, and examination

to find out sequential user activity pattern.

Sofia Angeletou, Matthew Rowe Harith

Alani studied online user behaviour in

online communities in 2012 published the

article “Modelling and Analysis of User

Behaviour in Online Communities”[23].

They used statistical analysis,combined

with a semantic model and rules for

representing and computing behaviour in

online communities. They applied this

model on a number of forum communities

from Boards.ie to categorise behaviour of

community members over time, and report

on how different behaviour compositions

correlate with positive and negative

community growth in these forums. Aoying

Zhou, Weinign Qian Haixin Ma in 2012

studied collective behaviour in social

networks and published “Social Media Data

Analysis for Revealing Collective

Behaviours”[3]. They established that given

sufficient social media data, users collective

behaviours could be sensed, studied, and

even predicted in a certain circumstance.

They conducted experiments on data from

two social network platforms, i.e. Twitter,

and Sina Weibo. Collective behaviours are

actions of a large amount of various people,

which are neither conforming nor deviant.

Various collective behaviours are studied in

the context of social media. Their studies

show that there are various information flow

patterns in social media, some of which are

similar to traditional media such as

newspapers, while others are embedded

deep in the social network structure. The

evolution of hotspots is highly affected by

external stimulation, the social network

structure, and individual users activities.

Furthermore, social media tends to be

immune to some repeated similar external

stimulations.

Behaviour Prediction

So far we have discussed social network

user behaviour characterization &behaviour

recognition in various platforms.Various

methods & algorithms used in above

mentioned tasks have been reported. Next

we are to report relevant research works in

the line of behaviour prediction. By

analyzing various social network data sets

and by observing previous activities, future

behaviour can be predicted as mentioned

below. M. Vasudevan and M. Tamilarasi

conducted a survey on users collective

behaviour in social network in their research

paper “Collective behaviour prediction in

social media: A survey”[19] in 2012.

Collective behaviour refers to how

individuals behave when they are exposed in

to a social network environment. This

collective behaviour gives the opportunity to

predict online behaviours of users provided

behaviour information of some actors in the

network are available. This work studies

how networks in social media can help

predict some sorts of human behaviour and

individual preference. This can help

understand the behaviour patterns presented

in social media, as well as other tasks

likesocial networking advertising and

recommendation. Adam Sadilek in

hisdoctoral thesis [1], studied “Human

behaviour at large scale in various social

networks”. The underlying theme of this

thesis is the unification and data mining of

diverse, noisy, and incomplete sensory data

over large numbers of individuals. They also

found that raw sensory data linked with the

content of users online communication,

theexplicit as well as the implicit online

social interactions, and interpersonal

relationships are rich information sources

upon which strong machine learning models

can be built. Examples where such models

apply include understanding human

activities,predicting

peoples location and social ties from their

online behaviour, and predicting the

emergence of global epidemics from day-to-

day interpersonal interactions. Jihang Ye et.

al. in 2012 in research work “Whats Your

Next Move: User Activity Prediction in

Location-based Social Networks”[12]

exploited the check-in category information

to model the underlying user movement

pattern. They proposed a framework which

uses a mixed hidden Markov model to

predict the category of user activity at the

next step and then predict the most likely

location given the estimated category

distribution. The advantages of modelling

the category level include a significantly

reduced prediction space and a precise

expression of the semantic meaning of user

activities. Michal Kosinski et. al in their

study “Manifestations of user personality in

website choice and behaviour on online

social networks”investigated the user

behaviour in social networks. This work,

based on a sample of million users,

examines how users behaviour in the online

environment, captured by their website

choices and Facebook profile features,

relates to their personality [24]. Results

show that there are psychologically

meaningful links between users personalities,

their website preferences and Facebook

profile features. They summarized that

predicting a users personality profile can be

applied to personalize content, optimize

search results, and improve online

advertising. This concept can be applied to

predict user?s behaviour in social network.

Sharad Goel and Daniel G. Goldstein

studied prediction of individual behaviour [4]

in 2013. They employed acommunications

network of over 100 million people to

forecast highly diverse behaviours from

patronizing an online department store. They

found that social network data are strongly

informative in identifying individuals who

are most likely to undertake actions, and that

in identifying such individuals, social data

generally improve the predictive accuracy of

base-line models. They published the

research article “Predicting Individual

Behaviour with Social Networks”. Tad

Hogg et. al. published “Stochastic Models

Predict User Behaviour in Social Media”in

2013. User response contributes content in

online social media depends on many factors

[2]. These include how the site lays out new

content, how frequently the user visits the

site, how many friends the user follows, how

active these friends are, as well as how

interesting or useful the content is to the user.

They presented a stochastic modeling

framework that relates a user behaviour to

details of the sites user interface and user

activity. They also described a procedure for

estimating model parameters from available

data. They applied the model to study

discussions of controversial topics on

Twitter, specifically, to predict how

followers of an advocate for a topic

responded to the advocates’posts.

Methods & Approaches For Behaviour

Analysis

Social network user behaviour analysis has

been discussed in this survey in three

subcategories such as behaviour

characterization, behaviour recognition and

behaviour prediction. They found that,

despite the relative randomness and lesser

commitment of structural relationships in

online forums, users community joining

behaviours display some strong regularity.

So here they have described that behaviour

in SN user are forum or platform dependent.

There is no such common characterization

of user behaviour for all kinds of social

network forums. Ahn Dae-Yong & Randal

Watson in 2010 in their research paper, “A

Dynamic Model of User Behaviour in a

Social Network Site”[2], estimated a

dynamic model of user behaviour in a social

network site using unique data on the daily

login activity of MySpace.com members.

Using a social network site, it may have two

effects (interactions, flow of utilities over

time) of: (1)maintaining the quality of

interactions with an existing group of friends,

and (2) expanding the quantityof interactions

with the addition of new friends to the

network. Since both of these effects will

yield a higher flow of utilities in future, it is

appropriate to analyze usage of a social

network site with a model that takes into

account consumers forward-looking

behaviour. David John Robinson in his

doctoral thesis in 2010, “Cyber-Based

Behavioural Modeling”[5] proposes a noble

approach to the modelling and analysis of

behaviours based on a users’ cyber activities

(communication, workshop, play etc). They

presented a methodology to identify, extract,

and analyze cyber behaviours providing the

foundation for cyber-based behavioral

modeling. Methods are implemented to

characterize, predict, and detect change in

individual and group behaviours. Kartik

Bommepally et. al. in their research paper

“Internet Activity Analysis Through Proxy

Log”[15] in 2010 have analyzed the

behaviourofInternetusersbehindaproxy.

They also analyzed users Internet usage

pattern. User usage behaviour can be

analyzed using server log data. Laszlo

Gyarmati and Tuan Anh Trinh, in their

research paper “Measuring User Behaviour

in Online Social Networks”in 2010,

presented a large scale measurement

analysis of user behaviour in some popular

OSNs [17]. A measurement framework has

been created in order to observe user activity.

More than 500 PlanetLab nodes across the

globe have been used for measurement,

monitoring more than 80,000 users for six

weeks by downloading more than 100

million profile pages. Based on the

measurements, they addressed two key

issues of online social networks:

characterization of user activities and usage

patterns in the examined OSNs. Pietro

Panzarasa et al. in their research Patterns

and Dynamics of Users [2] “Behaviour and

Interaction: Network Analysis of an Online

Community”2010, draws on longitudinal

network data from an online community to

examine patterns of user behaviour and

social interaction, and infer the processes

underpinning dynamics of system use. The

online community represents a prototypical

example of a complex evolving social

network in which connections between users

are established over time by online messages.

Huiqi Zhang in his doctoral thesis, 2010,

“SOCIOSCOPE: Human Relationship and

Behaviour Analysis”[13] , proposed a model

for social network, relationship and human

behaviour analysis based on mobile phone

call detail records. Because of the diversities

and complexities of human social behaviour,

one technique cannot detect different

features of human socialbehaviours.

Therefore he used multiple probability and

statistical methods for quantifying

socialgroups, relationships and

communication patterns, for predicting

social tie strengths and for detecting human

behaviour changes. Mark Granovetter, State

University of New York at Stony Brook, in

his article, “Models of collective behaviour”,

developed a model for situations where

actors have two alternatives and the costs

and/or benefits of each depend on how many

other actors choose which alternative [22].

The key concept is that of threshold the

number or proportion of others who must

make one decision before a given actor does

so. Beginning with a frequency distribution

of thresholds, the models allow calculation

of the ultimate or equilibrium number

making each decision. The stability of

equilibrium resulting against various

possible changes in threshold distributions is

considered. Stress is placed on the

importance of exact distributions for

outcomes. Groups with similar average

preferences may generate very different

results; hence it is hazardous to infer

individual dispositions from aggregate

outcomes or to assume that behaviour was

directed by ultimately agreed-upon norms.

Suggested applications are riot behaviour,

innovation and rumour diffusion, strikes,

voting, and migration. Issues of

measurement, falsification, and verification

are discussed in thestudy.

Datasets For Behaviour Analysis

There are several datasets available for

social network user behaviour analysis

methods. The most commonly used datasets

are given below. Capturing and

preprocessing of high speed social network

traffic is essential for practical analysis of

social network user behaviour. Different

tools are used for capturing and analyzing of

social network traffic data for analysis.

Various types of data are used for behaviour

analysis. Krishna Das et. al. in their research

paper [16] used some similar data sets as

mentioned below to study the social network

users’ group dynamic in community

detection problem. Types of data sets used

in behaviour analysis are: (i) Profile Data: A

member provides to social network

platforms for registering itself. Statistics of

the surveyed paper (ii) Posted Data: The

data which is explicitly posted by the

members such as messages, comments and

blog entries.These data are highly sparse in

nature and hence processing of these type of

datasets pose tremendous challanges. But

these are highly essential in persistent

behaviour analysis such as sentiment

analysis, product rating, reputation & trust

estimation etc. (iii) Derived Data: The data

which is derived or mined by correlating

other information. Log server data, web

traces, browsing history, time stamp data etc

belong to this category and are used in non-

persistent behaviouranalysis.

Conclusion:

we studied user behaviour in OSNs from

three perspectives- behaviour

characterization, behaviour recognition

&behaviour prediction. The study considered

users? various activities in the social

network platforms, such as connections and

interactions, traffic activities, positional

arrangements of the users in the network etc.

We reviewed the existing representative

schemes and also provided potential future

directions. We also focused on twodifferent

broad research lines such as persistent and

non-persistent behaviour of social network

users. This survey will help in understanding

chronological advancement of user

behaviour analysis research as many of the

published works till 2016 have been included

in thisreport.

References

[1] Adam Sadilek 2012, Modeling human

behavior at a large scale, Doctoral

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