Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework

TELKOMNIKA, Vol.16, No.3, June 2018, pp. 1416~1425
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v16i3.4624  1416
Received August 20, 2017; Revised April 22, 2018; Accepted May 9, 2018
Selecting User Influence on Twitter Data Using Skyline
Query under MapReduce Framework
Ahmad Luky Ramdani*
1
, Taufik Djatna
2
, Heru Sukoco
3
1
Department of Science, Informatic Engineering, Institut Teknologi Sumatera, Indonesia
2
Department of Agro-industrial Technology, Bogor Agricultural University, Indonesia
3
Department of Computer Science, Bogor Agricultural University, Indonesia
*Corresponding author, e-mail: taufikdjatna@ipb.ac.id
2
, ahmadluky@if.itera.ac.id
1
Abstract
The aim of this research was to select and identify user influence on Twitter data. In identification
stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and
characterization of the opinion leader. The importan characteristics included external communication,
accessibility, and innovation. Based on these characteristics and information from Twitter data through
matrix Twitter and sentimentanalysis, a algorithm ofskyline query was constructed for the selection stage.
Algorithm of skyline query selected user influence by comparing with other users according to values of
each characteristic. Thus, user influence was indicated as user that was not influenced by other users in
any combination ofskyline objects.The use of MapReduce framework model in identification and selection
stage, support whole operation where Twitter had big size data and rapid changes. The results in
identification and selection of user influence exhibited thatMapReduce framework minimized the execution
time, whereas in parallel skyline query could reveal user influence on the data.
Keywords:opinion leader,user influence,skyline query,mapreduce framework
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Twitter has been a popular media in Indonesia that is widely used for sharing opinion
and propaganda. Based on motivation “what’s happening?”, The users deliver their opinion and
propaganda with less than 140 characters as called by tweet. The propaganda-related tweet is
commonly found in voters of a public election [1] and a product marketing [2]. In the public
election, propaganda is addressed to national figures or prospective leaders, while, in
marketing, it is related to product branding. The use of social media Twitter to blow up
propaganda is called by viral marketing [3].
One of the concepts used in the viral marketing is user influence [4]. It is a user in
Twitter that is capable to influence other users in making decision. Matrix Twitter (MT) is one of
the methods used for impact measurement. MT is a mathematic symbol that provides
information related to Twitter user in numerical values such as number of follower, following,
reply, and tweet [5]. These values are combined in formula or algorithm like in the use of
algorithm PageRank in selecting user influence [6]
The use of MT in identifying user influence was conducted by [7] based on
characteristics of opinion leader and machine learning algorithm. However the study had a
weakness; analysis of user influence was only based on MT. MT only measures users based on
their information and relation to other users. Septiandri and Purwarianti (2013) stated that the
influence level of users may result from their tweet. The reply sentiment in a tweet demonstrated
the influence of the user [8].
Furthermore, machine learning algorithm highly depends on data used in the training
and measurement, thus model consistency is always required in different various data. Different
data possibly cause inaccuracy in identifying the user influence. [9] used skyline query to
identify and select users in Facebook. Skyline query is an algorithm to obtain an object that is
not dominated by other objects [10]. The object is called as skyline. The user influence is
considered when the user is not dominated by other users. These users indicate better
particular characteristics. Hence this research used MT feature and tweet content analysis to
select user influence using skyline query based on characteristics of opinion leader (OL).

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Selecting User Influence on Twitter Data Using Skyline Query... (Ahmad Luky Ramdani)
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OL represents the individuals that are able to influence others in decision making of a
community [11]. User influence in twitter has similarity to OL [12]. [13] stated that OL could be
seen from characteristics including external communication, innovation, accessibility, and
social-economy status.
Different from common skyline query, selection of user influence using OL
characteristics is not simple. The characteristics are not directly from Twitter data. Increasing
Twitter data, data processing is unable to be processed using simple approach in a computer.
MapReduce in a group of computer is a solution for determination of user influence. It is a
programming model and software that relates to big size data [14]. MapReduce framework can
process distributed data in the group of computer. Based on the description, the study aimed to
(1) identify OL based user influence according to MT and tweet content analysis in MapReduce
framework, and (2) apply algorithm of skyline query in MapReduce framework to select user
influence.
2. Research Method
The method consists of 3 stages, 1) pre-processing 2) identification of user influence,
and 3) determination of user influence. Data collection based on topics and sentiment analysis
are conducted in pre-processing, while selection of user influence includes skyline process and
top-k query. Identification and selection of user influence are processed in MapReduce
framework. The research flowchart is presented in Figure 1.
Preprocessing
MapReduce framework
Selecting User Influence
Data Collection Based on TopicsData Collection Based on Topics Sentiment AnalysisSentiment Analysis
Identification of User InfluenceIdentification of User Influence
Skyline querySkyline query Top-k queryTop-k query
Figure 1. Research stages
2.1. Pre-processing
2.1.1. Data collection based on topics
This step identifies a character to obtain national figure for topic and keyword in data
collection. This uses contextual and morphology on data article from online news media and
channels of the political party such as detik.com. Article data was obtained by using Really
Simple Syndication (RSS). Contextual and morphology rule are chosen since they have better
accuracy compared to association mining rule in identifying the characters [15]. The characters
obtained are then used to determine keywords. The data was collected using library Twitter4j. In
this study, only opinion tweet is selected for next stage. Therefore, normalization and selection
of opinion are performed. Part of speech tagging, Hidden Markov Model algorithm (HMM) and
opinion sentence rule are used to filter the opinion [16]. This step results in Twitter containing
opinion that comment national figures.
2.1.2. Sentiment analysis
The step aims to obtain model of sentiment classification. The model is used to
determine the level of reply sentiment in user influence. The step employs hybrid, a combination
of machine learning algorithm and sentiment dictionary (lexicons) to gain classification
model [17].

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The hybrid method consists of several process including corpus formation,
normalization, feature reduction, standardization, mark extraction, and classification. Formation
of corpus is based on [18-19], which uses emoticon characters. Normalization is to improve
invalid data in Twitter [20]. Feature reduction aims to reduce unused word/characters such as
punctuation and stop words. Standardization of sentiment dictionary consists of translation,
validation, and standardization. Translation is used by means of library TextBlob in Bing
Liu [21], SentiStrength Emoticons [22], SentiWords [23], and MPQA Subjectivity (MPQA-S) [24].
Validation employs Kamus Besar Bahasa Indonesia (KBBI) to make sure that translated output
has meaning in Indonesia language. Furthermore, each dictionary has different sensitivity
range. Therefore, standardization is applied in value 0 and 1 [25].
Standardized dictionary is used in mark extraction. This step is to determine vector of
mark sentiment used in the sentiment classification. The marks used are from [26] with
modification, namely, word number of positive, negative, and neutral sentiments, word number
of sentiments, word value of sentiment, maximum value of positive and negative sentiments,
and number of negation word in the tweet. The vector and machine learning algorithm, Naive
Bayes Classification (NBC), are used in the classification to achieve classification model. The
algorithm NBC provides high accuracy in the classification of text document [27]
2.2. Identification of user influence
Identification of user influence is based on specification and characteristics of OL, MT,
and classification model of sentiment. Therefore, data attribute, MT calculation, and sentiment
weight of user are analyzed. The proses uses BPMN workflow for analyzing and modelling a
process. Programming language java and Hadoop are used for implementation. BPMN used is
Sybase Power Designer 16.0. BPMN is understandable notation for analyzing, modelling, and
representing process stream. The OL characteristics are external communication, accessibility,
and innovation adaptation. Additionally, social-economy status is discarded since Twitter users
have good social-economy status [7].
a. Analysis of data attribute
Attribute analysis aims to determine data attribute for calculation of MT value, which is
conducted using data observation.
b. Calculation of MT value
Calculation of MT value is based on MT [5]. This step is needed since some values of
MT are not directly obtained from Twitter data. The output of this process is Twitter user
and MT value.
c. Calculation of sentiment weight
Calculation of sentiment weight uses model from sentiment analysis. The use of
sentiment model only focuses on sentiment value of tweet, which is a reply. This is because
value of reply sentiment on tweet shows user’ss influence [8]
2.3. Selecting user influence
2.3.1 Skyline query
Skyline query is a method to select objects that are not dominated by other objects in
data. These objects are skyline. Skyline algorithm includes Block Nested Loops (BNL) [28], Sort
Filter Skyline (SFS) [29], Nearest Neighbor Skyline (NN) [30], Bitmap, Index and Branch-and-
Bound Skyline [31]. Sort Filter Skyline (SFS) algorithm is development of Block Nested Loops
(BNL) algorithm.
Based on the value from identification, a user is skyline if and only if this user is not
dominated by other users. User i is not dominated by other users when value of user I in each
OL characteristics is better than other users. Therefore, a user influence is not dominated by
other users based on OL characteristics. Skyline query in the current study uses algorithm Sort
Filter Skyline (SFS) in MapReduce framework [32].
SFS in MapReduce framework consists of tow steps, in which determination of local
and global skyline. Determination of local skyline is initially conducted by splitting data. Each
part is counted to attain skyline based on SFS algorithm. Skyline obtained from each part is
merged and filtered to gain global skyline. Partition process employs technique used in Nearest
Neighbor and Divide & Conqure. Data is divided by 2
d
parts based on median value from each
data attribute, which d is number of data attribute. Each part is defined as d-bit, which is used in
key-value of function map and reduce. The result of local skyline determination is user/skyline

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influence from each part. The data were used in the determination of global skyline to obtain
user influence from whole data.
2.4.2 Top-k query
Top-k query is ranking method for user influence, which k is a positive integer. The
value defines expected number of the most influential users. Based on user influence obtained
from skyline query, the users are ranked to find the most influential user. The process is
conducted using top-k query according to [33]. This is because the algorithm does not depend
on value function of data and is effective in determining the best object. Additionally, the
algorithm can be processed in MapReduce framework. The following points are steps for top-k
algorithm in MapReduce framework.
a. Data form skyline query was split according to data attribute (m) { , , }. Value m
in this study was 3.
b. Data m+1 { m } is made, which the value is addition of three previous attributes.
c. In map process, data is ranked in each part according to ascending function and then
process the ranking.
d. Map process output is a pair of user and ranking value (r) in each data that represents
temporary key-value.
e. Combiner step is processed for the user pair and r form map process, which groups
user-based-data.
f. The output from previous step is a pair of user and r with similar user. This output is
used in reduce step.
g. In reduce step, sum-r is processed in each user group.
h. The output of reduce step is pair of user and sum-r.
i. The ranking is done according to ascending function and sum-r value. User with low
sum-r is user influence on data.
j. Pair of user and sum-r is saved in accordance with value k.
3. Results and Analysis
3.1. Data
In this work, a focus on Indonesian national figures were taken pleace. They are
selected for the topic in data collection are Joko Widodo (jokowi), Jusuf Kalla, Ridwan Kamil
(ridwan kamil) and Basuki Tjahaja Purnama (Ahok). These names are gained from identification
process in January 2016. Based on these names, this study creates two groups of data
containing opinion for the topic, corpus data and election.
First, corpus is data used as corpus to create model of sentiment classification. Data
contain tweet collected in April 2016. The filtration is 1281 tweet consisting of 891 positive
tweet, 289 negative tweet, and 101 neutral tweet. Second, selection data is used for
identification and determination of user influence. The data are collected in April 2016.
Identification of spam or bot is processed in data using Twitter follower-following ratio (TFF),
which is useful to discard spam or bot users. TFF result is exhibited in Figure 2. Users identified
as spam are users with TFF value approximately 0 or under black line. This stage results in
65.702 users with 551.513 tweet.
Figure 2. TFF ratio

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3.2. Sentiment analysis model
Sentiment analysis with a setup of hybrid approach on corpus data shows accuracy
75.57% by applying a 10-fold cross validation. Thus, the model was applied for classification of
selection data to determine sentiment weight of the user. Table 1 illustrated number of tweet
and user in selection data with sentiment groups. In selection data, 11.326 tweet are tweet
reply.
Table 1. Tweet and user in each sentiment group
Positive Neutral Negative Total
Tw eet 182.932 184.289 184.302 551.523
Number of tw eet user 35419 35629 35.752 65.702
Tw eet reply 12.213 12.375 12.289 36.877
Number of tw eet reply user 5.350 5.420 5.301 11.326
3.3. Characteristics value of user influence
BPMN in identifying user influence is presented in Figure 3. Initial identification is by
analyzing data attribute of Twitter, calculating MT value, and determining sentiment weight of
user. Based on data observation, Twitter data consists of 3 groups of attribute, attribute
connecting with user activity, user profile, tweet content.
Figure 3. BPMN for identification of user influence
User activity is an attribute that connects with the user activity on Twitter. User profile is data
attribute that contains user information related to user and its relation to other users including
name, number of follower, following, etc. Tweet content describes information in tweet, such as
user_mention, hastag, and symbols.
Table 2 is results of data attribute analysis that is usable for calculating MT value. The
calculation is based on each user in selection data and attribute in Table 2. User that has ID
RP3 is more than zero (RP3>0) is counted its sentiment weight using Equation 1. Data with MT
value and sentiment weight are used to select value from each characteristic of user influence.
u
∑ u-r (1)
BSu is sentiment weight u which is an value of reply sentiment ( u-r) on tweet of user u in data.
Sentiment 2 for positive sentiment reply, -2 for negative sentiment reply, and 0 for positive
sentiment reply.
Weight Sentiment
Data
Twitetr Data
Matrik Twitter Data
RP3>0
X
X _
X
Calculation of
Matrix Twitter
Value
Calculation of
Sentimen Weight
[No]
End
Start
End
[Yes]
Analysis of characteristics
user influence
Analysis of Data
Attribute

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Tabel 2. ID matrik Twitter dan atribut data Twitter
ID Atribut Data
F1 User:Follow ers_Count
F2 User:Follow ers_Count
F3 User: Friends_Count
F4 User: Friends_Count
F5 User:Screen_Name, Text
F6 User:Screen_Name, Text
M1 User:Screen_Name, Entities: User_Mentions
OT1 Text
OT2 Url
OT3 Hashtag
RP1 User:Screen_Name, In_Reply_To_Screen_Name
RT1 Retw eeted_Status, User: Screen_Name
RT2 Retw eeted_Status, Retw eeted_Status:User:Screen_Name
RT3 Retw eeted_Status, Retw eeted_Status:User:Screen_Name
FT1 User:Screen_Name, User:Favorites_Count
FT2 User:Screen_Name, Favorited(True)
FT3 User:Screen_Name, User:Favorites_Count
3.3.1. External Communication
External communication indicates popularity. In Twitter, the popularity can be observed
from F1 and F3 of MT, number of follower and following. User with the highest follower maybe
not because of its tweet content quality, but it is socially active. Consequently, it has many
friends and followers. Moreover, it may possible for user with the highest follower since it shares
useful information, which is subsequently followed by other users. Therefore, value of external
communication is calculated using equation 2 [34]. The user with external communication value
approximately 1 is the best user based on this characteristic.
ollower ank (2)
3.3.2. Accessibility
Accessibility in Twitter based on MT as shown in Table 2 as seen from ID value F3,
RP1, RT1, FT1, M2, appointed to following, reply, retweet, favorite/like, and mention of a user.
User with high following activity shows preference for discussion with other users. A user is a
following of other users, thus the user can reply, retweet, like and mention. Hence accessibility
is gained from addition of each MT that represents user accessibility, calculated using
formulation in equation 3.
a essi ility (3)
3.3.3. Innovation
Innovation value in Twitter based on MT can be observed form OT1 and RT2, namely
tweet and tweet that is re-tweet by other users in other users. In addition, innovation is
observable from tweet sentiment given through reply from other users. Sentiment is represented
by sentimen weight. Thus innovation value is obtained by adding value from each MT that
represents innovation characteristic with sentiment weight. Innovation value is calculated using
formulation in Equation 4.
innovation u (4)
3.3.4. Implementation
Implementation compares between conventional approach and MapReduce.
Conventional model is used to investigate efficiency and speedup MapReduce model.
Conventional model is used in a single computer using programming model object oriented with
aid of library Google guava. MapReduce uses library Hadoop in 3 computers (node). Table 3

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shows comparison of execution time between conventional model and MapReduce in 3 nodes.
The use of MapReduce enhances execution time (speed up) in counting characteristic
value of user.
Tabel 3. Speed up for identification of user influence
Conventional MapReduce Speed up
External communication 598,318 second 16,974 second 35,249
Aaccessibility 3.105,096 second 28,566 second 108,610
Innovation adaptation 468,139 second 22,973 second 20,378
Figure 4 shows graphic of time execution in MapReduce model framework with different
nodes and data. Addition of node in MapReduce model shows shorter execution time and
stagnant tendency in each variation of data number when data is more than 500.000 tweet.
Data variations used are 100.000, 200.000, 300.000, 400.000, and more than 500.000.
Figure 4. Comparison of execution time in different node number and data in each characteristic
of user influence
Therefore, additional node in data results reflected consistency of time execution, which
also suggested that MapReduce model enables to distribute time complexity to each involved
node. Output of this stage is user data with value from each characteristic of user influence.
3.4. User influence
Data obtained from previous step consist of 3 attributes that represent characteristic
number of user influence. Based on data, calculation is needed using algorithm SFS. Local
skyline obtained are 186 users that are user influence in each part and 16 users for global
skyline. Calculation of top-k query is required to determine the most influential user.
Figure 5 illustrates that bigger red dots or dots on the area are user influence in data.
Figure 6 shows comparison execution time of conventional model and MapReduce in selection
of user influence in skyline and top-k query. Data used are 100.000, 200.000, 300.000, 400.000,
and more than 500.000 users. The k value used is 2, 4, 6, 8, and 10. Processing with skyline
query shows shorter time execution and constant using MapReduce in each various node and
data. However conventional model demonstrates exponential time execution in each
different data.
In algorithm top-k with various k and node, execution time is not significantly different
and constant either conventional model or MapReduce due to small k value. Nevertheless, the
use of MapReduce model always has shorter execution time in various data. Table 4 is an
example of user influence with value k=5. User influen e y name “andreas_o_joeda” sele ted.

1423
Figure 5. Illustration of user influence distribution and result of skyline query
Figure 6. Comparison of execution time of conventional model and MapReduce on Skyline and
top-k query
Application of skyline and top-k query in MapReduce framework in selecting user
influence points out user that is able to influence other users in shorter time. However selected
users do not discuss national figures-related topics corresponding to the issues in online media
portals.
Table 4. Result of user influence determination with k=5
User External Communication Accessibility Adaptation Innovation
andreas_o_joeda 8.293,21 941 349
ridw ankamil 4.028,25 233 245
beasisw aindo 344,18 176.842 56
kapanlagicom 400,99 3.097 789
metro_tv 256,032 2 43
4. Conclusion
Identification process of user influence showed that OL characteristics-external
communication, accessibility, and innovation adaptation-are applicable for finding user influence
in Twitter data. Based on these characteristics, the determination of user influence using by MT
values and tweet content analysis achieved 16 user influences out of 65.702 users. In addition,
identification process and user selection in MapReduce model demonstrates faster execution
time in comparison with conventional model.
In case of future trand and investigation, the determination of user influence should be
conducted in dynamic data style. Therefore selection process potentially address to current and
real time position of user influence according to current issue of national figures.

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