A Methodology for Identifying Inﬂuencers and their

Products Perception on Twitter

Ermelinda Oro, Clara Pizzuti and Massimo Ruffolo

National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR),

Via Pietro Bucci, 4-11C, 87036 Rende (CS), Italy

Keywords:

Social Network, Social Media, Twitter, Inﬂuential User, Twitter Inﬂuencer, Product Perception, Social

Network Analysis, Multilayer Networks, Multilinear Algebra, Tensor Decomposition.

Abstract:

The massive amount of information posted by twitterers is attracting growing interest because of the several

applications ﬁelds it can be utilized, such as, for instance, e-commerce. In fact, tweets enable users to express

opinions about products and to inﬂuence other users. Thus, the identiﬁcation of social network key inﬂuencers

with their products perception and preferences is crucial to enable marketers to apply effective techniques of

viral marketing and recommendation. In this paper, we propose a methodology, based on multilinear algebra,

that combines topological and contextual information to identify the most inﬂuential twitterers of speciﬁc

topics or products along with their perceptions and opinions about them. Experiments on a real use case

regarding smartphones show the ability of the proposed methodology to ﬁnd users that are authoritative in the

social network in expressing their views about products and to identify the most relevant products for these

users, along with the opinions they express.

1 INTRODUCTION

Twitter is a microblogging platform that is gaining

impressive popularity because tweets enable users to

express opinions, sentiments, and preferences about

different topics and products in a very concise form.

The availability of the enormous amount of informa-

tion posted by twitterers is attracting growing inte-

rest of both research community and business com-

panies. In fact, the possibility to timely understand

motivations of the popularity of topics or products, al-

ong with opinions expressed by people on them, can

be a valuable help to design more effective promo-

tion campaigns. E-commerce websites apply social

marketing techniques to recommend their products to

customers. In this context, marketers need to better

identify users having the capability of inﬂuencing ot-

her users’choices in order to empower product recom-

mendation techniques. So, a crucial research activity

is the identiﬁcation of social network key inﬂuencers

with their products perception and preferences.

Many scholars (Probst et al., 2013; Pawar et al.,

2015) have studied the usage of Twitter contents to

analyze social behavior to create smarter and more

effective real-world applications in the area of viral

marketing and recommendation systems. Existing ap-

proaches aiming at ﬁnding inﬂuential users mainly

rely on measures based on centrality indexes, com-

puted on the network representing people relations-

hips (Riquelme and Cantergiani, 2016). Several met-

hods are based on concepts of authority and hub sco-

res (Kleinberg, 1999). But, they consider neither if a

user is active on a speciﬁc topic or product of interest,

nor her opinion.

In this paper, we propose a methodology capable

to identity inﬂuential twitterers along with their per-

ceptions about speciﬁc products. The methodology

consists of four main steps: (i) downloading tweets

related to a speciﬁc set of products and extracting re-

levant data, (ii) building a multilayer network and ten-

sor model by using extracted data, (iii) identifying in-

ﬂuencers by using the SocialAU algorithm (Oro et al.,

2017), plus (iv) identifying dominant products and re-

lated perceptions. The methodology ﬁnds inﬂuential

users whose opinion on items of interest can be ex-

ploited by business companies for promoting or mo-

difying their marketing and sales campaigns. Expe-

riments on tweets regarding the smartphone domain

show the ability of the proposed methodology to ﬁnd

users that are both authoritative in the user network

built from such tweets, and active in expressing their

viewpoint about such products.

Oro, E., Pizzuti, C. and Ruffolo, M.

A Methodology for Identifying Inﬂuencers and their Products Perception on Twitter.

DOI: 10.5220/0006675405770584

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 577-584

ISBN: 978-989-758-298-1

577

The paper is organized as follows. Next section

reports the related work. Section 3 presents the pro-

posed methodology capable to ﬁnd inﬂuential Twitter

users plus their opinions. In Section 4 experiments on

a real-world use case are presented. Finally, Section

5 concludes the paper.

2 RELATED WORK

The interest in methods capable to ﬁnd inﬂuential

users on Twitter is growing exponentially. The know-

ledge of the most relevant actors, in fact, is useful for

many applications, such as viral marketing, informa-

tion propagation, and recommendation systems (Ado-

mavicius and Tuzhilin, 2005). A detailed survey on

the measures proposed in the literature to detect in-

ﬂuencers can be found in (Riquelme and Cantergiani,

2016). In this section, the most recent methods aimed

to identify inﬂuential users is reviewed.

Since social networks are represented as graphs,

where nodes represent the users and edges their in-

terpersonal connections, a high number of methods

rely on the centrality measures to detect the most im-

port users. Many methods are based on PageRank

(Brin and Page, 1998) and HIT S (Kleinberg, 1999),

originally introduced to rank web pages. Weng et

al. (Weng et al., 2010) proposed a method, named

TwitterRank, that improves the PageRank algorithm

(Brin and Page, 1998) by automatically identifying

topics that twitterers are interested in. The method

measures twitterer inﬂuence by taking into account

the link structure of followers/following of individual

users and the topical similarity between these users.

Cha et al. (Cha et al., 2010) deﬁned three indices:

in-degree, mentions and retweets. The in-degree me-

asure corresponds to the popularity of a user, men-

tions represent the capability of a user to attract ot-

her users in a topic discussion, while retweets express

the importance of the user’s tweet content and mea-

sure the ability of that user to diffuse interesting argu-

ments. An analysis on Twitter users highlighted that

in-degree alone does not generate inﬂuence, while the

most mentioned users are celebrities and the most ret-

weeted users are news sites and businessmen. Analo-

gously to Cha et al. (Cha et al., 2010), Anger and Kittl

(Anger and Kittl, 2011) proposed three indices to me-

asure the inﬂuence based on the number of retweets

and mentions relative to a user. Thus they deﬁne the

concepts of Retweet and Mention Ratio, Interaction

Ratio, and Social Networking Potential. Almgren and

Lee (Almgren and Lee, 2015) proposed a content-

based inﬂuence measure, named CIM, that takes into

account the social interactions of users (such as ret-

weets on Twitter or ”like” on Facebook). The authors

build a weighted directed graph, where the nodes are

the users, and the weights represent the number of so-

cial interactions that a node performed on the posts of

another node. The inﬂuence is computed considering

the concept of node centrality.

All these approaches rely on graph theory where

connections express interrelationships such as follo-

wer/following or retweet/mention. The methodology

we propose builds the relationships by extracting the

information from the tweet content, thus by analyzing

not only network topology, but also opinions expres-

sed on topics of interest.

3 METHODOLOGY

In this section we present a methodology for detecting

Twitter inﬂuencers and dominant products of a dom-

ain along with their perception. It is composed by

four main steps, shown in Figure 1: (i) collection of

tweets related to products of interest and extraction of

relevant data; (ii) building of a model based on mul-

tilayer heterogeneous networks; (iii) identiﬁcation of

inﬂuencers; (iv) identiﬁcation of dominant products

and related perceptions. The methodology is general

and can be applied to different product types. In the

following we describe the four steps by considering a

use case of tweets talking about mobile phones.

3.1 Tweets Collection and Data

Extraction

As shown in Figure 1, the ﬁrst step (represented by

a dashed rectangle) is composed by: (i) the down-

load of tweets related to products to be analyzed (e.g.,

smarphones), then (ii) the extraction of interesting in-

formation (such as, users, products, and keywords)

from collected tweets. To download tweets of interest

we use the Twitter API

searching for target products

names and hashtags. We download only tweets dea-

ling with the target products, and each tweet is tagged

by the mentioned products. From each tweet, we get

the user name of the author. In order to capture inte-

ractions between other users we extract from the text

re-tweets and mentions. We considered as keywords

expressing opinions both lemmatized form of adjecti-

ves and hashtags that describe products contained in

tweets.

Twitter Rest API: https://dev.twitter.com.

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

578

Figure 1: Methodology illustration.

3.2 Multilayer Network Building

After the information from tweets has been extrac-

ted, we build a three-layer network, as shown in Fi-

gure 1 and introduced in (Oro et al., 2017). The

three layers represent users, products, and keywords.

Intra-layer interactions model the types of connecti-

ons among the actors of the same layer, while inter-

layer interactions give the information that a user u

expresses an opinion on a product p by using a ke-

yword k. Formally, the three-layer network is a pair

M = (G, T ), where G = {G

, G

} is a set of

graphs, and where: • G

= (X

, E

) is a directed

weighted network representing the n users that are

active on the domain and their connections. Thus

= {u

, . . . , u

}, and E

= {(u

, u

) | user u

men-

tions user u

or retweets u

’s posts}. • G

, E

) is the product network representing the set of

m products X

= {p

, . . . , p

} and their similarity re-

lationship E

= {(p

, p

) | sim(p

, p

)}, i.e. two pro-

ducts are connected if they satisfy a similarity crite-

rion. • G

= (X

, E

) is the keywords network repre-

senting adjectives and hashtags that describe products

= {k

, . . . , k

} and their ties. E

= {(k

, k

) | ∃ a

post where k

and k

co-occur}, i.e. two keywords

are connected if they both appear in the same tweet.

• T is the 3rd-order tensor representing the inter-

layer connections among all the three layers. The cor-

responding n × m × r adjacency tensor X represents

that a user u ∈ G

expresses z times an opinion on a

product p ∈ G

by using a keyword k ∈ G

Therefore, we can represent tweets as triples ca-

pable to capture the opinion expressed by users about

selected products. Figure 2 shows an example of

tweet and the information extracted from it: a con-

nection from Ivan to twandroid in the USER network

because of the retweet, the triple (Ivan, Galaxy S5,

pas s

ecuris

e) in the three-layer network because Ivan

says that the ﬁngerprint sensor of the Galaxy S5 is

vulnerable, i.e. it no safe (pas s

ecuris

e).

Figure 2: Example of tweet where users, keywords, pro-

ducts, intra- and inter-layers connections are identiﬁed. Ex-

tracted information are denoted in red font, dashed arrows

represent extracted intra (e.g., retweets) and inter-layer arcs

(e.g., the triple (Ivan, Galaxy S5, pas s

ecuris

e).

Figure 3 shows an example of three-layer network

built from the posts published by 10 twitterers re-

garding 4 smartphones by using 8 keywords. The

Figure 3: Example of three-layer network with 10

users, 4 products, and 8 keywords, with triples

, G3, thin)=2, (u

, Z3, good)=3, (u

, S5, good)=1,

, Z3, good)=2, (u

, Z3, beauti f ul)=1, (u

, P7, thin)=1,

, G3, beauti f ul)=1, (u

, S5, beauti f ul)=2.

thickness of arcs is proportional to the number of con-

nections between the two nodes. For instance, the

tie between user u

and user u

in the USERS layer

means that u

mentioned u

or retweeted her posts

many times, while the tie (good, beauti f ul) means

that the two words appear in the same tweet. The

triple (u

, Z3, good) means that user u

sent several

tweets (note the thickness of the arcs) containing the

keyword good on the product Z3.

A Methodology for Identifying Inﬂuencers and their Products Perception on Twitter

579

3.3 Inﬂuencers Identiﬁcation

The third step of the methodology is the identiﬁcation

of inﬂuential users by analyzing the multilayer model

(see Figure 1). To ﬁnd the most authoritative users

sending tweets regarding particular products, we ex-

ploit the algorithm SocialAU presented in (Oro et al.,

2017). It extends the TOPHITS method, proposed by

Kolda et al. (Kolda et al., 2005), based on multilayer

networks to identify topics and the associated autho-

ritative web pages, by including the scores compu-

ted on each layer by using the HITS (Hypertext Indu-

ced Topic Selection) algorithm proposed by Kleinberg

(Kleinberg, 1999).

The deﬁnitions of authority and hub introduced in

(Kleinberg, 1999) can be adapted to users by substitu-

ting the concept of web page with that of user. Thus,

if a user u

links to a user u

she has conferred autho-

rity on u

. In fact, if a user u

mentions another user

or retweets u

’s tweets, she deems interesting the

contents issued by u

, thus she has conferred authority

on u

. If a user u

links to many authoritative users,

she is said a hub. A good hub is a user that points to

many good authorities; a good authority is a user that

is pointed by many good hubs. The same notions can

be applied also to the products and keywords layers.

However, in such a case the corresponding networks

are undirected, thus the concepts of authority and hub

coincide. Analogously to TOPHITS (Kolda et al.,

2005), SocialAU computes triplets (h, a, w) from the

3-mode tensor T , where h contains the hub scores of

users, a the score of items, and w contains the sco-

res of the keywords. However, differently from TOP-

HITS, the computation takes into account the role of

objects in their own layer. Thus, while computing the

hub and authority scores of a user in the 3rd-way ten-

sor, it considers if the user is also a dominant user

in the proper monolayer network. In such a way the

opinions the user expresses in her tweets are more in-

ﬂuential if she is an authoritative users that also sends

many tweets, i.e. she is a good hub.

Considering the example of three-layer network

shown in the Figure 3, we can see that user u

has

many incoming edges in the USERS network, while

has only outgoing edges, thus though both u

and

expresses several opinions on different items, u

considered by SocialAU more inﬂuential than u

be-

cause of the many mentions or retweets received.

3.4 Products Perception Identiﬁcation

The last step shown in Figure 1 has the objective to

identify the most relevant products for the inﬂuential

users, along with the opinion they express. By analy-

zing the sentiment keywords extracted from the tweet

texts, it is possible to elicit the inﬂuencer’s opinion on

a product. Therefore, while the user’s score gives the

information if that user is authoritative to diffuse in-

formation, the keywords which represent her opinion,

allow to understand the kind of inﬂuence this user can

generate. Positive opinions of inﬂuential users can be

exploited by E-commerce websites to improve their

marketing techniques for recommending their pro-

ducts to customers. Our approach can be a valid sup-

port to address key marketing questions like: (a) Who

is talking about our products? (b) Who are the top in-

ﬂuencers and what characterize them? (c) Which pro-

ducts are generating most interest? (d) Which aspects

or opinion are people associating with every product?

(e) Which is the sentiment and overall trend towards

a product?

4 USE CASE

Driven by the needs of a mobile company, which was

trying to expand in France in 2015, we conducted a

study on the main smartphone brands in the French

market by aggregating discussions and social media

information from Twitter. With this real use case,

in this section, we demonstrate the effectiveness of

the presented methodology, based on SocialAU (Oro

et al., 2017), in detecting Twitter inﬂuencers and do-

minant products along with their perception.

4.1 Dataset

From May 7th to July 27th 2015, we downloaded

24843 French tweets dealing with 51 smartphones

manufactures and models reported in Table 1. Rows

show the main brands manufacturers and columns

group similar smartphones. Smartphones belong to a

given category on the base of their features like: dis-

play size, average price and photo camera Mpxl. Ca-

tegory Cat. 1 represents top level smartphones having

highest features.

From the downloaded tweets relative to 51

smartphones, we extracted 2706 keywords, which in-

clude adjectives or hashtags. We recognized and ex-

tracted 9783 users that dealt with the smartphone

products (i.e. including authors of tweets, mentio-

ned users, retweeted users). However, many of these

users, even if they posted tweets related to smartp-

hones, did not express any opinion (i.e. their tweets

did not contain adjectives or hashtags). Hence, they

have been eliminated because spawning no useful in-

formation for our goal of analysis. In addition, We

removed users that talk about smarthphones but wit-

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

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Table 1: Smartphone brands and competitive products.

BRAND CAT. 1 CAT. 2 CAT. 3 CAT. 4 CAT. 5

Alcatel Hero Idol 2s Idol S, Idol 2, Mini S,

Pop S7 Pop S3

HTC One Max One M8 One Mini2

Huawei Ascend Ascend G7 Ascend P7, Ascend Ascend

Mate 7 P8 G620s Y550

LG Flex G3S G3 L80, L90, L Bello F70, L60

Nokia 1320, 1520 830, 930 535, 735 635

Samsung Galaxy Galaxy S5 Galaxy Galaxy Galaxy Ace 4,

Note 4 Alpha, A8 Grand Core 4G

Sony T2 T3 Z3, M2, E3

Z3 compact M2 Aqua

Wiko Gateway, Rainbow 4G, Birdy,

Highway 4G Wax Kite

hout expressing opinions, and 112 users having no

followers or followees because they represent spam-

mers generating the same tweets. Therefore, we kept

4953 interesting users. The user network we gene-

rated contains 9 028 nodes with 9191 arcs. The key-

word network is composed of 2706 nodes and 29 554

arcs. The number of triples of the 3rd-order tensor

9028×51×2 706 are 26673. Statistics of the Smartp-

hone dataset are listed in Table 2. The dataset is pu-

blished for further analysis and comparisons

Table 2: Statistics of the Smartphone dataset.

#user #smartphone #keywords #tensor

nodes arcs nodes arcs nodes arcs triple arcs

9028 9191 51 268 2706 29 554 26 673

4.2 Inﬂuencers

In this section we focus on the authoritative users re-

turned by our algorithm SocialAU and compare them

with those obtained by TOPHITS with respect to well-

know inﬂuence measures (Oro et al., 2017), as sum-

marized in the Table 3. For each user, we computed

the value of each inﬂuence measure and we used the

relative order of users’ ranks as a measure of diffe-

rence. Users have been sorted by descending order

value of each measure, so that the rank 1 indicates the

most inﬂuential user, and increasing rank denotes less

inﬂuential users.

To measure the strength of the association bet-

ween two rank sets, we computed the Spearman’s

ρ (Pirie, 1988) and the Kendall’s Tau τ (Conover,

1980) correlation coefﬁcients for both approaches So-

cialAU and TOPHITS with respect to all the deﬁned

evaluation measures. As pointed out by Cha et al.

(Cha et al., 2010), positive correlation values between

two measures mean that users receive similar scores.

Thus, a user considered inﬂuential by one measure

because having a high score, is considered inﬂuential

http://www.lindaoro.com/datasets/twitter.html

Table 3: Meaning of the inﬂuence measures used to evaluate

the results.

Notation Meaning

Followers/Following Ratio compares the amount of users who

have subscribed to the updates of a user u with the number of users

that u is following.

Retweet inﬂuence Ratio measures the fraction of retweets relative

to a user.

Mention inﬂuence Ratio measures the fraction of mentions contai-

ning user’s name.

Retweet and Mention Ratio enables to detect how many out of the

total tweets of a user u imply a reaction from other users.

nRT

Normalized Retweet and Mention Ratio measures r

normalized

with respect to the fraction of tweets posted by the user.

Interaction Ratio measures how many different individual users

interact with a user.

Normalized Interaction Ratio measures r

normalized with respect

to the number of followers.

SNP Social Networking Potential represents the potential of interactions

within the network of followers on Twitter.

nRMU

User Normalized Retweet and Mention Ratio measures the re-

action of a user u to the tweets of other users. The user’s activity

is normalized with respect to the fraction of tweets posted by u.

nIU

User Normalized Interaction Ratio weighs the number of retweets

and mentions posted by a user u with respect to the followees nor-

malized by the maximum number of followees.

UA User Activity measures the percentage of comments a user u sends

on the target products out of the total number of posts.

also by the other measure, i.e. the two inﬂuence mea-

sures agree on the role of inﬂuencers played by users.

The closer the value to +1 or −1, the stronger the

positive or negative correlations between two measu-

res respectively. We observed that both SocialAU and

TOPHITS have a negative correlation with the Follo-

wers/Followings ratio r

. Such a negative correlation

is expected since both approaches do not use the in-

formation on followers/followings to determine inﬂu-

ential users. The normalized interaction ratio has po-

sitive coefﬁcients, higher for SocialAU than for TOP-

HITS. All the other correlation values, except for UA,

are higher for SocialAU than for TOPHITS. In fact,

TOPHITS is based on UA, while SocialAU, besides

UA, exploits also the networks of each layer. Thus,

SocialAU indirectly exploits the number of retweets

and mentions obtained by a user, as well the number

of users that did them. This result points out that So-

cialAU effectively takes advantage of the information

coming from the User network because it gives a hig-

her rank to users that are mentioned more times than

other users, and whose tweets raise interest.

This result is conﬁrmed by the ranking of Twitter

users. The ﬁrst 50 dominant users sending tweets on

these smartphones obtained by SocialAU and TOP-

HITS are shown in Table 4 and Table 5, respectively.

Other than the relative position assigned by the com-

pared approaches, for each user u indicated by its

A Methodology for Identifying Inﬂuencers and their Products Perception on Twitter

581

name we report: Fw, the number of followers of user

u; Fg, the number of users that u follows; NRMU,

the sum of the number of retweets posted by u and of

mentions towards other users; NRM, the sum of num-

ber of retweets and mentions obtained by u; URM, the

sum of the number of users that retweeted or mentio-

ned u; T

, the number of three-layer connections the

user u participates.

As can be seen from the Tables 4 and 5, SocialAU

returns as the top user twandroid while TOPHITS gi-

ves kinghousse. The result of TOPHITS is obvious

since this user generated the highest number of three-

layer connections. However kinghousse is an online

sale web site for smartphones and accessories, that

sent a lot of tweets regarding smartphone promotions

that have not been considered interesting by others.

In fact, neither this user receives mentions never it is

retweeted. twandroid, instead, is a very popular An-

droid community giving information about smartpho-

nes and delivering opinions about them. Though the

number of triples it generated in the considered period

is much lower than those of kinghousse, 78 against

870, the number of retweets and mentions, as well as

the number of users performing them, is high (517

and 149 respectively). Thus twandroid can be consi-

dered much more inﬂuential than kinghousse since

it has been judged authoritative by other users. Mo-

reover, by considering the enormous number of pe-

ople (73 800 followers) it can reach when broadcas-

ting information, its opinion could sensibly bias the

choices of many users. Looking at the Table 4, which

shows the top 50 Twitter users sorted according to So-

cialAU results, same considerations can be applied to

other users, such as phonandroid, that is ranked 110

by TOPHITS and third by SocialAU, considering the

number of its retweets and mentions, orangejeux,

ranked 2 043 by TOPHITS and 8

by SocialAU, that

sent only one tweet generating the reaction from 475

users, or mobileactus, ranked 137 by TOPHITS and

by SocialAU, a web site giving news on smartp-

hones having 52 200 followers and receiving 109 ret-

weets or mention for its 14 posts regarding smartpho-

nes. Table 5 shows the top 50 users found by TOP-

HITS and the corresponding ranking given by Socia-

lAU. It is worth pointing out that almost all the values

of NMRU, NMR, URM of these authoritative users

are null, while the ranking positions assigned by both

approaches to the same user are close only when T

is high, and thus the hub score of users in the ten-

sor dominates the hub and authority scores in the user

network G

So, the tables highlights the different behavior

of TOPHITS and SocialAU assigning rather different

rankings to those users active in their own network.

Table 4: Top 50 Twitter users according to SocialAU results

and corresponding rank position given by TOPHITS.

SocialAU username F w Fg NRMU NRM URM T

TOPHITS

1 twandroid 73800 2444 14 517 149 78 14

2 magikstar29 77 36 231 0 0 168 10

3 phonandroid 27000 1693 3 222 103 43 110

4 kinghousse 930 682 0 0 0 870 1

5 droidtrackr fr 978 378 0 14 10 382 2

6 francois 974 748 1084 40 0 0 42 86

7 guillaumeg516 773 359 0 1 1 518 3

8 orangejeux 13 200 1052 1 479 475 1 2043

9 petitbuzz 3816 4176 23 46 3 455 4

10 frandroidforum 315 55 42 2 1 100 23

11 meilleurmobile 4 446 2 554 4 134 30 62 49

12 bonplanhightech 208 29 0 0 0 473 5

13 arkangelscrap 7371 6963 38 1 1 25 151

14 androidpit fr 2844 94 2 102 21 57 84

15 petitbuzzblog 1 583 1683 34 12 2 322 6

16 rnb 001 1337 744 43 0 0 24 67

17 mobileactus 52200 37 700 0 109 33 14 137

18 echosix8 221 201 25 0 0 20 83

19 rez0 4 185 2003 28 0 0 16 181

20 roxinofr 1751 13 7 65 34 31 81

21 prix discount 232 174 12 13 2 234 7

22 jprenaud78 160 326 27 7 3 26 87

23 wilborie80 25 76 24 0 0 22 63

24 dididogg 25 54 12 0 0 12 96

25 vlantenois 24 151 16 0 0 14 155

26 hichamb143 52 115 11 0 0 11 91

27 willou du80 68 62 18 1 1 18 226

28 creativeoctpus 966 807 0 0 0 137 9

29 prixchocamazon 151 3 0 0 0 307 8

30 mobileovernewz 5 571 4129 0 100 23 77 18

31 dojo l33t 374 563 22 0 0 17 412

32 informatiqueuh 227 29 0 0 0 211 11

33 andoriaofﬁciel 88 209 13 0 0 10 462

34 kevindecularo 231 66 13 0 0 9 120

35 cllia m 420 289 19 0 0 4 2 071

36 pangocase 248 476 12 0 0 14 270

37 lesbonsplansdun 521 71 0 2 1 165 13

38 stevelumia 31 112 15 0 0 16 150

39 17heures17 171 248 2 1 1 415 30

40 laveilletechno 7993 1716 15 0 0 9 228

41 amevorf32 47 182 13 0 0 10 308

42 ulrichrozier 6419 123 13 0 0 5 250

43 scl002 18 82 6 0 0 12 92

44 julien62162 146 63 23 0 0 32 145

45 ﬁgplans 31 32 11 0 0 17 126

46 mickael colin 449 510 8 0 0 7 637

47 trophyhunter35 304 1 701 14 0 0 8 613

48 oyonode 153 36 0 0 0 125 16

49 ericﬁtteduval 337 162 0 0 0 125 17

50 stephetheve 48 55 11 0 0 9 1 113

SocialAU takes into account, besides the inter-layer

connections, also the number of intra-layer links, that

determines the dominant users and products.

Figure 4 depicts a portion of the user network that

highlights the position of the ﬁrst ﬁfteen users obtai-

ned by SocialAU. It is important to notice the dense

connections around twandroid, magickstar,

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Table 5: Top 50 Twitter users according to TOPHITS results

and corresponding rank position determined by SocialAU.

TOPHITS username Fw Fg NRMU NRM U RM T

SocialAU

1 kinghousse 930 682 0 0 0 870 4

2 droidtrackr fr 978 378 0 14 10 382 5

3 guillaumeg516 773 359 0 1 1 518 7

4 petitbuzz 3 816 4176 23 46 3 455 9

5 bonplanhightech 208 29 0 0 0 473 12

6 petitbuzzblog 1 583 1 683 34 12 2 322 15

7 prix discount 232 174 12 13 2 234 21

8 prixchocamazon 151 3 0 0 0 307 29

9 creativeoctpus 966 807 0 0 0 137 28

10 magikstar29 77 36 231 0 0 168 2

11 informatiqueuh 227 29 0 0 0 211 32

12 soldes du net 15800 3 780 0 0 0 240 69

13 lesbonsplansdun 521 71 0 2 1 165 37

14 twandroid 73800 2444 14 517 149 78 1

15 phon android 594 1 0 0 0 160 66

16 oyonode 153 36 0 0 0 125 48

17 ericﬁtteduval 337 162 0 0 0 125 49

18 mobileovernewz 5571 4129 0 100 23 77 30

19 techekt 258 31 0 2 1 154 54

20 altetiafrance 26 4 0 0 0 90 61

21 arabianpages 55 500 54100 0 2 2 45 72

22 itnewsfrance 351 1 996 0 0 0 44 73

23 frandroidforum 315 55 42 2 1 100 10

24 ululafr 156 50 0 0 0 50 70

25 appsforward 57 102 0 0 0 47 92

26 bakoblal 92 241 0 0 0 45 75

27 editeuragoogle 133 152 0 0 0 49 77

28 mobilesactu 123 27 0 0 0 143 109

29 boriswapgeek 585 661 0 4 1 46 81

30 17heures17 171 248 2 1 1 415 39

31 ovnizefeed 33 1 0 0 0 41 86

32 achatsengroupes 227 5 0 1 1 418 108

33 shenmueforsony 4011 4156 7 0 0 20 74

34 bonplanlogiciel 201 29 0 0 0 239 121

35 keepmymindfree 643 515 0 4 2 220 155

36 angelapk4 494 1 236 0 0 0 22 123

37 crisapk1 494 1 277 0 0 0 22 124

38 davidapk1 548 1309 0 0 0 22 125

39 messiapk 387 1 068 0 0 0 22 126

40 nawalapk 469 1 156 0 0 0 22 127

41 rihanaapk 560 1353 0 0 0 22 128

42 monphpnet 42 141 0 0 0 32 117

43 neodymeind 1 596 512 0 0 0 39 136

44 doudou87000 27 51 0 1 1 27 99

45 webmasterfree 883 2 001 0 0 0 40 134

46 hassounak 1 386 413 0 0 0 37 135

47 bonplanovernewz 3 028 2847 0 9 5 20 141

48 alitsp 448 2 000 0 0 0 37 138

49 meilleurmobile 4446 2554 4 134 30 62 11

50 planspromos 34 5 0 0 0 139 107

phonandroid, francois 974, orangejeux,

milliermobile, arkangelscrap, androidpit fr,

scored 1

, 2

, 3

, 6

, 8

, 11

, 13

, 14

and by

TOPHITS 14

, 10

, 110

, 86

, 2043

, 49

, 151

, respectively. Moreover, it is possible to see

that kinghousse and bonplanhightech, scored ﬁrst

and ﬁfth by TOPHITS are isolated nodes in the user

network. The Figure 4 visually conﬁrms the ability

of SocialAU in ﬁnding users well connected in their

own network sending opinions about smartphones.

Figure 4: A portion of the user network of the Smartphone

dataset showing the position of the ﬁrst ﬁfteen users obtai-

ned by SocialAU and reported in Table 4. The connections

of twandroid are colored in red.

4.3 Products Perception

While the knowledge of the user’s score gives us

the information if that user is authoritative to dif-

fuse information, the keywords used by such user,

which represent its opinion, allows us to understand

the kind of inﬂuence this user can generate. Ta-

ble 6 shows, for the ﬁrst four dominant smartpho-

nes, the ﬁrst ﬁve dominant users posting tweets on

them, along with the adjectives used. From the ta-

ble we can notice that the opinions of twandroid,

magikstar and doidtrack fr are mainly positive

for Sony Experia Z3, Samsung Galaxy S5 and LG

G3, while as regards Sony Experia Z3 Compact,

the sentiment words are less frequent. phonandroid

and kinghousse, for instance, post tweets containing

mainly hashtags (not reported in the table).

5 CONCLUSIONS

We presented a methodology able to identify the most

inﬂuential twitterers along with their perceptions and

opinions about speciﬁc products. We conducted ex-

periments on a real use case regarding smartphones

and we demonstrated the effectiveness of the propo-

sed methodology. This methodology is general and

can be applied to support the analysis and marketing

strategies related to any type of products. By exploi-

ting this methodology, marketers are able to disco-

ver social key factors hidden in Twitter, such as: who

A Methodology for Identifying Inﬂuencers and their Products Perception on Twitter

583

Table 6: Most popular smartphone models in the dataset

with dominant users and adjectives used to express a judg-

ment on each smartphone.

smartphoneuser keywords

Sony twandroid good, thin, comparative, immediate

Xperia Z3 magikstar29 beautiful, efﬁcient, comparative, new, different,

the best, compact, thin, anti-overheating, advan-

tageous, good

phonandroid disponible,internationale,excessive,new,fast

kinghousse portable

droidtrackr fr beautiful, efﬁcient, top, compact, good, top, bur-

ning, new, thin, the best, fast

Samsung twandroid good, vulnerable, attractive

Galaxy S5 magikstar29 compatible, huge, new, expensive, good, at-

tractive, superior, vulnerable

phonandroid good, superior, successful

kinghousse mini, rigid, expensive, portable, light, ﬁne

droidtrackr fr compatible,good,vulnerable,attractive,superior

Sony twandroid good, bluetooth

Xperia Z3 magikstar29 compact, big

Compact phonandroid

kinghousse

droidtrackr fr compact, good

LG G3 twandroid good, available

magikstar29 the best, new, excellent, big, different, good

phonandroid small, good

kinghousse rigid, portable

droidtrackr fr the best, good, hard, super

are the top inﬂuencers and what characterizes them,

which products are generating more interest, which

opinions are associated with each product by the aut-

horitative users etc. These factors can help in the im-

plementation of effective techniques of viral marke-

ting and recommender systems.

The algorithms applied in the various steps of the

methodology can be extended and improved. For

instance, the product perception is based on opini-

ons expressed through simple extracted adjectives and

hashtags. In the future, we intend to apply more accu-

rate techniques of sentiment analysis and opinion mi-

ning (Medhat et al., 2014). In addition, we would

recognize accurate entity-targets of the opinions, i.e.,

not just the products but also features of them. In this

case, we could extend our model as a four-layer net-

work to analyze, for instance, which are the most re-

quired features discussed from users.

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