Popularity Metrics’ Normalization for Social Media Entities

Hiba Sebei

1,2

, Mohamed Ali Hadj Taieb

3,2

and Mohamed Ben Aouicha

3,2

Computer Science Department, Faculty of Economics and Mangement, Sfax, Tunisia

Multimedia, InfoRmation Systems and Advanced Computing Laboratory, Sfax, Tunisia

Computer Science Department, Faculty of Sciences, Sfax, Tunisa

Keywords: Popularity, Social Networks, Social Entity, Social Features, SPI.

Abstract: With the spread of online social media websites, a huge amount of online content is continuously provided.

However, some contents gain an important attention from users while other contents are completely

ignored. This highlights the analysis of popularity relative to different social content. The popularity is

expressed through measures and features that act as factors expressing and influencing the popularity. Those

features vary from an online social media website to another as it depends on the type of social entity. This

paper tries to create a normalized view of the popularity metrics independent of the online social media and

in relation with specific social entities that are user and media content (i.e. text, image, and video). We

propose a Service Provider Interface (SPI) as a contract between users. The SPI offers a variety of interfaces

for implementing services related to the quantification of social entities popularity independently of the

online social media they belong to.

1 INTRODUCTION

Over the last decade, online social media websites

have seen an exponential growth of the number of

active users as 313 million monthly active users on

Twitter

, over 1 billion users on YouTube

and about

1.94 billion monthly active users on Facebook

. This

supports the explosion of the amount of user-

generated data on those websites. In fact, the

statistics reveal that about 52 million photos shared

every day on Instagram

, 300 hours of videos

uploaded in every minute on YouTube

and about

58 million tweets every day on Twitter

This flood of data did not get the same attention

from users, as mentioned by (Lerman and Hogg,

2010) among 1600 new stories submitted on Digg

only a handful of them gather thousands of votes

while others are completely ignored by users. This

https://about.twitter.com/company

https://www.youtube.com/yt/press/en-GB/statistics.html

https://newsroom.fb.com/company-info/

http://www.statisticbrain.com/instagram-company-

statistics/

http://www.statisticbrain.com/youtube-statistics/

http://www.statisticbrain.com/twitter-statistics

http://digg.com

encourages the emergence of the notion of

popularity related to each social media content entity

as video, photo, and text. Where, the popularity

represents the corresponding amount of attention

from users to the content (Quan et al. 2012; Jiang et

al. 2014).

Studying the popularity of social entities is a

beneficial task for both social media data consumers

and producers. Most efforts made on the popularity

of social entities focus on the analysis of popularity

evolution and the prediction of popularity that help

to avoid the information overload by introducing for

users the most popular content as well as giving the

opportunity for companies to boost their business

strategy.

Through the study of social entities’ popularity

researchers try to find responses to some questions

as how we can boost social items popularity? Will

the studied item be popular or not? If, yes how

much the item will be popular in near or long time

future? Can the popularity of an item be quantified

before its creation?

To study the popularity of a social entity,

researchers have shared three requirements:

popularity measures, popularity features and

methods. However, for a specific type of social

entity the popularity metrics vary from an online

social media websites to another as it corresponds to

Sebei, H., Hadj Taieb, M. and Ben Aouicha, M.

Popularity Metrics’ Normalization for Social Media Entities.

DOI: 10.5220/0006693505250535

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 525-535

ISBN: 978-989-758-298-1

525

likes on Facebook, diggs on Digg, views on

YouTube and even in the same website popularity

can be measured in different manners as it can

correspond to the number of views or can combine

both the number of views and the number of

comments.

So, we aim through this paper to normalize the

features expressing the popularity of social media

entities independently of the social media instance to

which it belongs. In addition, we propose through

this paper a service provided interface offering the

services that can be implemented via the APIs of

social networks for gathering the existing features to

quantify the popularity degree independently of the

social media websites. Figure 1 illustrates the

problem and the aim of this study.

The rest of this paper is structured as follows:

Section 2 introduces the notion of popularity in

relation to different social entities (text, video, photo

and user) as well as the related terminology. Section

3 categorizes the studies established according to the

social entities and presents the metrics used to define

popularity. In addition, it highlights the variety of

metrics used through different studied social media

websites (Facebook, Twitter, Flickr, YouTube and

Digg), therefore our proposal to normalize these

metrics. The normalization is treated in section 4

based on the analysis of social entities’ popularity

metrics introduced by the already established

researches. Moreover, this normalization is

presented in a hierarchical way to show the different

categories of popularity metrics. Section 4

introduces, also, the materialization of the proposed

normalization under an implemented Service

Provider Interface (SPI). The final section is devoted

to presenting our conclusions and recommendations

for future research.

2 RELATED WORKS: SOCIAL

MEDIA POPULARITY

In this section, we introduce the notion of popularity

and its terminology related to social entities that

structure the content generated in different online

social media websites.

2.1 Popularity Notion

Several efforts focus on studying popularity related

to social entities. Some of them as (Figueiredo 2013;

Li et al. 2016; Hong et al. 2011) are motivated by

the information overload coming from online social

media data, so they try to predict the popularity of

social entities in order to help users receive the

important events and digital content. While others as

(Khosla et al. 2014; Jiang et al. 2014; Chatzopoulou

et al. 2010) are motivated by the act that being

popular on social media becomes essential for

companies and even for people, so they try to

understand and figure out the properties of social

items that make an item popular than other in order

to help people boosting the popularity of their

content. Also, we find studies as (Ma et al. 2013)

focus on the improvement of marketing strategies

Figure 1: Illustration of the problematic corresponding to the variety of social entities’ popularity metrics across different

online social media websites and the necessity of popularity metrics normalization.

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

526

and the development of diffusion strategies by

predicting real-world outcomes as the case of movie

revenue estimation (Jiang et al. 2014), predicting

popular items is also useful for websites owners as

mentioned by (Quan et al. 2012) in order to provide

the accurate resources as popular content leads to the

traffic increase that should be handled before getting

a technical problem.

There are a variety of studies that focus on the

popularity of social entities. Li et al. (2016) divided

these studies into two main categories: the first

includes those focusing on the popularity prediction

in microblogs as Twitter and the second one is

devoted to the prediction of popularity in media

sharing-websites as YouTube. For the first category

popularity is related to textual entities as a tweet on

Twitter and for the second category popularity is

relative to the media content as videos and photos.

The state of the art shows that the researches

works related to the popularity social media content

cover several social entities such as the textual

content as (Hong et al. 2011; Ma et al. 2013; Gao et

al. 2014; Lakkaraju and Ajmera 2011), videos as

(Jiang et al. 2014; Chatzopoulou et al. 2010), photos

as the case of (Khosla et al. 2014; McParlane et al.

2014) and finally there are some few works that

discuss the popularity of users in their social media

websites as (Jiang et al. 2014; Couronné et al. 2010).

As previously mentioned popularity identifies

the amount of attention from user to the content. So,

for analysing and predicting the popularity of each

social media entity among the already mentioned

ones (i.e. text, video, photo, and user) it is required

to identify the metrics of popularity, the features and

establish a link between those metrics using some

algorithms and methods for providing a

quantification.

2.2 Terminology

Popularity measures: are metrics to define the

popularity and varied from a study to another

(Khosla et al. 2014).

Popularity features: they present different

factors related to the target social entity and that can

affect its popularity (Khosla et al. 2014; Hong et al.

2011) as measuring social entities popularity is a

difficult task due the existence of variety of factors

that influence the quantification of popularity

(Cappallo et al. 2015).

Methods: they are the process used to figure out

the correlation between popularity measures and

features as factors that influence social media

entities popularity. Li et al. (2016) focus on

popularity prediction task and categorized the

approaches into three main groups: regression-based

approach, classification-based approach and model-

based approach.

2.3 Social Entities

During, the first age of social media, the content

generated by users focus on the text (blogs) then by

the integration of the web 2.0 technology as a

platform for building social media websites the user-

generated content takes additional forms such as

video, photo and audio. Each one of these types is

considered as a social entity. Also, we consider the

user presented by profile as a social entity. As,

several researchers focus on studying the popularity

relative to each type of user-generated data, in the

next section, we classify the related works according

to the studied social entities (i.e. text, video, photo

and user).

3 SOCIAL ENTITIES:

POPULARITY

QUANTIIFICATION

The state of the art shows that the identification of

popularity measures and features for a specific social

entity varies in the same online social media

websites and across different websites. These points

are presented and discussed in following sections

according to each social entity.

3.1 Text

Among the studies focusing on popularity analysis

of Twitter messages as textual entities, Hong et al.

(2011) define the popularity measure as the number

of re-tweets related to the textual entity and they

take into consideration the message content,

temporal information features, metadata of messages

and users, as well as structural properties of the

users’ social graph as features that influence the

popularity of messages on Twitter. Some other

studies focus on specific textual entities as a hashtag

on twitter messages, Ma et al. (2013) predict the

popularity of a hashtag by presenting the number of

users who post at least one tweet containing the

hashtag within the given time period as the

popularity measure. Then they specify two main

categories of popularity features: content features

and contextual features. Where the content features

refer lexical data derived from the hashtag and from

Popularity Metrics’ Normalization for Social Media Entities

527

the tweet containing the hashtag (e.g. number of

segment words from a hashtag). For the contextual

features, they are related to data derived from the

social graphs formed by Twitter users (e.g. the

number of tweets containing the hashtag). Lerman

and Hogg (2010) worked on the news as a textual

entity they try to predict the popularity of news and

through the Digg website. They express the news

popularity as the number of votes a story

accumulates on Digg. While Wu and Shen (2015)

use the number of re-tweets that the news tweet

gathers from users on Twitter.

3.2 Image

McParlane et al. (2014) studied the popularity of

image in Flickr. They define several popularity

measures as the number of views related to the

image and he considers three main features image’s

context (e.g. time, day, size, flash, orientation),

visual appearance related to information extracted

from the image’s pixel (e.g. color, faces, etc.) and

user context (e.g. gender, account, contacts, etc.).

While Khosla et al. (2014) studies popularity of

photos on Flickr by considering the number of views

as a measure for popularity and combines both

image content features (e.g. color, objects in the

image, vision, etc.) and social context features (e.g.

user‘s contact, users’ groups, mean view, title,

description, etc.) as features for studying image

popularity. Gelli et al. (2015) studied the popularity

prediction of images based on Flickr photos by

considering the number of views on Flickr as a

popularity metric including three main features:

user features (i.e. metadata related to the author of

the image), visual features (e.g. color) , and context

features (i.e. tags and description related to the

image).

3.3 Video

Several related studies focus on studying popularity

of YouTube videos as (Chatzopoulou et al. 2010)

that defines the popularity based on the number of

views and considers the number of comments,

ratings and favorites as features to understand the

evolution of YouTube video popularity. Figueiredo

(2013) considers the number of views as a

popularity measure and classifies the features in

three main classes features the first class is related to

video content (e.g. video category, upload date, etc.),

the second class refers to link features as (e.g.

referrer first date and referrer number of views) and

the third class refers to popularity features that are

measured during a defined period of time (e.g.

number of views, number of comments, number of

favorites, etc.). Jiang et al. (2014) also exploit the

number of views as a measure of popularity but they

define different popularity features to study viral

YouTube videos as the video metadata (e.g. id, title,

text description, category, number of raters, number

of likes, number of dislikes), the user metadata who

uploaded the video (e.g. user ID, name, profile view

count, etc.), the historic of view (e.g. comments,

likes and dislikes), the number of inlinks in other

social media, and the comments related to the video.

Trzcinski and Rokita (2017) focused their research

on popularity prediction of videos. They exploit two

datasets: one is from YouTube and the second is

from Facebook. For YouTube video, popularity

metrics are expressed via the number of views,

comments, favorites and ratings while for Facebook

video, popularity metrics correspond to the number

of shares, likes and comments.

3.4 User

Couronné et al. (2010) studied the popularity

evolution of online social media user in MySpace

which is considered as an online social media. Two

popularity measures are identified: the audience of

the contents and the user’s authority. The first one

identifies the figurenumber of visits to the artist’s

page while the second one defines the number of

people recommending the artist by linking to him.

The author takes into account two features: music

features (e.g. the number the visits of the profile, the

number of comments visitors have left on the

profile, etc.) and the search variables that define the

number of Twitter post containing the artist name in

the last month, the number of results of the Yahoo!

search engine when searching the artist's name.

Zafarani and Liu (2016) discuss the variation of user

popularity across sites as individual join multiple

sites and quantifies user’s popularity based on his

number of friends.

Table 1 categorizes the popularity related works

for each type of social entity. In addition, it

summarizes the features and metrics used in each

study.

3.5 Discussion

This study leads to two main results: firstly, then

lack of specific metrics to express popularity and

secondly, the popularity metrics are expressed

differently from a social media to another.

 Lack of specific metrics to express popularity

metrics: For a specific social entity (i.e. text,

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

528

video, photo, and user) a variety of metrics are

used by the researcher to express popularity.

The variety of these metrics inside the same

social entity type is reflected in the same online

social media website. As to study popularity of

image on Flickr McParlane et al. (2014)

defines three main set of features image’s

context, visual features and user context.

While, Khosla et al. (2014) defined other sets

image content and social context features. It is

worth to mention that despite the difference in

the nomination of the sets of metrics between

the two works there is an overlap between the

sets as the user context set considered by

(McParlane et al. 2014) which holds user

metadata as well as the social context features

set considered by (Khosla et al. 2014).

Table 1: Categorization of popularity related works based on the type of social media entities.

Social

entity

Popularity measures and features

Reference Measures Features

Text

(Hong et al.

2011)

The number of re-tweets

Message content, temporal information features,

metadata of messages and users, structural properties of

the users’ social

(Ma et al.

2013)

The number of users who post

at least one tweet containing the

hashta

The number of users who post at least one tweet

containing the hashtag

(Lerman

and Hogg,

2010)

The number of votes

Story metadata, historic of votes, the list of friends of

the top-ranked users

(Wu and

Shen, 2015)

The number of re-tweets

Metrics related to the topology of the re-tweet

propagation (e.g. date of creation, number of direct

followers receiving update, number of followers

viewed the news, etc.

)

Image

(McParlane

et al. 2014)

The number of views and

number of comments

Image’s context (e.g. time, day, size, flash, orientation),

Visual appearance (e.g. color, faces, etc.) and user

context (e.g. gender, account, contacts, etc.)

(Khosla et al.

2014)

The number of views

Image content features (e.g. color, objects in the image,

vision, etc.), Social context features (e.g. user‘s contact,

users’ groups, mean view, title, description, etc.)

(Gelli et al.

2015)

The number of views

User features (i.e. metadata related to the author of the

image) and visual features and Context features (i.e. tags

and descri

tion related to the ima

)

Video

(Chatzopoulo

u et al. 2010)

The number of views The number of comments , ratings and favorites

(Figueiredo

2013)

The number of views

Video content (e.g. video category, upload date, etc.),

Link features (e.g. referrer first date and referrer number

of views) and Popularity features (e.g. number of views,

number of comments, number of favorites, etc.)

(Jiang et al.

2014)

The number of views

Video metadata (e.g. id, title, text description, category,

number of raters, etc.), user metadata (e.g. user ID,

name, profile views count, etc.), historic of view (e.g.

Comments, likes and dislikes), the number of in-links

and the video comments

(Trzcinski

and Rokita,

2017)

YouTube: the number of views,

comments, favorites and ratings

Facebook : the number of shares,

likes and comments

Visual features (e.g. Video characteristics, color, etc.),

Temporal features: refer to the number of views and

number of social interactions (e.g. number of shares,

likes and comments)

User

(Couronné et

al. 2010)

The audience of the contents:

number of visits of the artist’s

page

User’s Authority: number of

people recommending the artist

y linking to him.

Music variables (e.g. the number the visits of the profile,

the number of comments visitors have left on the

profile)

Search variables the number of the Twitter post

containing the artist name in the last month, the number

of results of the Yahoo! search engine when searching

the artist's name

Popularity Metrics’ Normalization for Social Media Entities

529

 A Variety of popularity metrics across different

online social media websites: Hong et al.

(2011) considered a textual entity use case. In

fact, the popularity of message on Twitter as a

textual entity is expressed based on the number

of re-tweets while authors in (Lakkaraju and

Ajmera, 2011) measure the popularity relative

to a post made by a brand page based on the

number of comments gathered by the target

post. Also, Trzcinski and Rokita (2017)

measured the popularity of video in YouTube

by considering the number of views,

comments, favorites and ratings. For Facebook,

they count the number of shares, likes and

comments as popularity measures. Khosla et al.

(2014) highlight the variety of popularity

metrics. Indeed, they cite that for an image the

popularity can correspond to “the number of

likes on Facebook, the number of pins on

or the number of diggs on Digg“.

Two main questions arise in relation to the

variety of parameters used to analyze the relative

popularity of a particular social entity: The first,

question that arises is how to evaluate the subjective

parameters that express the popularity as mentioned

by (Cappallo et al. 2015)? The second question is

how to break away from the specific parameters of

each social media website in order to express the

popularity?

In this paper, we are interested in answering the

second question by proposing a factorization of the

different popularity metrics relative to each type of

online social entity independently of the social

media website source. This factorization will be

expressed via a Service Provider Interface (SPI)

offered the concluded services from the study

according to the already discussed social entities:

text, video, image and user.

4 NORMALIZED POPULARITY

METRICS AND THE

PROPOSED SERVICE

PROVIDER INTERFACE

In this section we propose a normalization of

different social entities’ popularity metrics based on

the state of the art presented in the previous section

as well as the different popularity metrics extracted

from a number of online social media websites (e.g.

https://fr.pinterest.com/pinterestfr/

Twitter, Facebook, YouTube, Google+ and Flickr)

relative to each social entity.

4.1 Normalization of Popularity

Metrics across Online Social Media

Websites

Based on the study made in the previous section, we

consider the popularity related to the social entities:

text, video, photo and user. So, we distinguish

between two main categories user entity and media

entity.

User: The variety of purposes behind using social

network websites reveals a variety of self-

presentation on those websites. Social networks are

used by simple individuals to establish social or

business relationships, by organizations and

companies to promote a marketing purpose, by a

community of individuals to group people with

common social or professional interest or by non-

physical individuals such as the presentation of an

event or a channel. So, different entities exist to

identify the user across the network such as profiles,

groups, pages and events. It is worth to distinguish

between popular user and influence user. A popular

user does not imply that he is influential. The

difference between popularity and influence is

discussed in (Kwak et al. 2010) where authors adapt

the number of followers related to a Twitter user as a

popularity measure but they prove its inefficiency

regarding the quantification of the user’s influence.

Describing the user popularity, is treated through

three main categories of metrics: user profile

metadata that refer to metadata created during the

creation of the profile, (e.g. name, gender, age,

member duration, etc.), user activities metadata

reflect how much the user is active in his network

(e.g. number of posts, number of posted media) and

profile’s connectivity metadata reflects user’s

relationships in the network (e.g. number of

contacts, number of friends, number of followers,

etc.).

Media: refers to the different type of online social

media user-generated content: text, video and photo.

Presentation: refers to image, video and textual

entities. It is worth to mention that the textual entity

can refer to a tweet on Twitter, a Facebook post or

an activity posted on Google plus. The textual entity

can embed media entities.

Normalization: based on the related works, it is clear

that the metrics correspond to the popularity of each

social entity define two main categories of metrics:

metrics related to the content of the target entity and

metrics related to the context of the target entity.

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

530

 The content metrics: correspond to

parameters extracted from the content of the

target entity (i.e. video, image, text). These

metrics are obtained based on advanced

techniques as sentiment analysis, clustering and

natural language processing (Khosla et al.

2014) applied to textual entities as made by

(Ma et al. 2013) who derives lexical parameters

from hashtag as content features. The task

becomes harder when the content feature is

derived from media objects (Khosla et al. 2014;

Cappallo et al. 2015), advanced techniques as

computer vision and machine learning are used

by (Khosla et al. 2014) in order to extract

content features from an image. The content

features of media items (i.e. image and video)

correspond to visual features as colors, objects

in images (e.g. people faces) (Khosla et al.

2014; McParlane et al. 2014), also visual

sentiment features as mentioned by (Gelli et al.

2015).

 The contextual metrics: they refer to

parameters relative to the target social entity;

they do not require the use of complicated

algorithms and techniques to get them.

Actually, these metrics are directly extracted

from online social networks as a category of

social media (e.g. Facebook, Twitter, Google+,

etc.) using the application provided interface

(API) offered by those websites. These metrics

vary from a social network to another. The

parameters, associated with popularity metrics

and related to different social entities (e.g.

textual entity from Twitter and Google plus,

using the Twitter Search API and Google plus

REST API respectively, etc.), are extracted.

The results are summarized in Table 2 that presents

some instance of social entities from different social

Table 2: Social entities instance and its related popularity metrics across different social media websites.

Social

entit

Social entity

instance

Social

media

Extracted metrics API

Text

Tweet Twitter

FavoriteCount, HashtagEntities, id, retweetCount, text, user,

CreatedAt, etc.

Twitter

Search API

Activity

Google

Plus

Id , Activity author, Activity publishedAT, Activity Title,

Activity URL, Activity content, Activity replies, etc.

Google+

API

Comment YouTube

AuthorChannelUrl, AuthorName, ViewerRating, LikeCount,,

Text,

ublishedAt

YouTube

API

Post Facebook

Id, shares, admin_creator, created_time, description, link,

message, place, picture, source, etc.

Facebook

Graph API

Video

Video YouTube

ChannelId, description, PublishedAt,, title, Url, ViewCount,

CommentCount, DislikeCount, FavoriteCount, etc.

YouTube API

Embedded

video

Twitter

URL, id , sizes (e .g large, medium, etc.), duration_millis,

Video formats, video aspect ratios, updated_at, title, etc.

Twitter

Search API

Video Facebook

ad_breaks, backdated_time, created_time, id, description, from,

len

th,

lace, source, title.

Facebook

Gra

h API

Photo

Flickr

Owner (id, name, etc.), title , description, number of comments,

tags, URL, number of favorites

Flickr API

Embedded

hoto

Twitter URL, id, sizes (e .g large, medium, etc.)

Twitter

Search API

Photo Facebook

Id, album, backdated_time, created_time, from, icon, height,

link, name, place, etc.

Facebook

Graph API

User

Page Facebook

About, created time, number of likes, number of fans, name,

icture, id, and cate

Facebook

Gra

h API

Profile Twitter

Id, Name, Screenname, createdAT, StatusesCount, Description

FavoritesCount FollowersCount, FriendsCount, User Tweets:

list of tweets

Twitter

Search API

https://developer.twitter.com/en/docs

https://developers.google.com/+/web/api/rest/

https://developers.google.com/youtube/v3/docs

https://developers.facebook.com/docs/graph-api

https://www.flickr.com/services/api/misc.overview.html

Popularity Metrics’ Normalization for Social Media Entities

531

media websites and presents the related popularity

metrics.

We focus on the contextual metrics in order to

normalize them independently of the online social

media websites. So, we distinguish between two

main categories of contextual metrics: media

contextual metrics and media author contextual

metrics.

Media Contextual Metrics: refers to the

metadata of the target media. It is divided between

media metadata and user feedback metadata. Where

the media metadata refer to two sets: firstly, a set

describes metadata generated by end users during

the upload of the media entity and devoted to

describe the entity (e.g. a video description, tags,

date of the upload, etc.), secondly, a set of metadata

generated after the upload of the media (e.g.

accumulated comments, related media, etc.). Then,

the user feedback metadata refer to metrics resulted

from user activities related to the media this

metadata can express either a simple feedback from

user (i.e. does not require an explicit activity from

the user) as the number of views which is counted as

soon as the user just visit the media or it can refer to

an explicit feedback accumulated after an explicit

activity from the user as sharing a media, rating a

video, like or dislike a post from the execution of

these activities a number of popularity metrics are

generated (e.g. number of likes, number of favorites,

number of ratings, etc.). The user feedback metrics

are also characterized by their dynamics as they

evaluate during the time.

Media Author Metrics: several researchers as

(Khosla et al. 2014; Quan et al. 2012; Szabo and

Huberman 2010) discuss the impact of the

connectivity of the user who uploaded the popularity

of the target entity. So, they use the metadata related

to the author of the media entity.

The author contextual metrics are those defining

the user popularity discussed in the previous

paragraph and referring to user’s profile metadata as

the gender of the user that can be extracted directly

using the social network API or based on their

names on the target social network as the case of

(McParlane et al. 2014). It includes, also, the user

activities metadata and user connectivity metadata.

Figure 2 defines the media entity popularity

metrics in a hierarchical manner in order to present

the different factorization levels. In addition, it

illustrates also the popularity of the user entity via

the media author popularity (the part framed in red).

This hierarchy is materialized by implementing

an extensible application that provides to its users a

set of unified services allowing the definition of

popularity instances related to social entities and

independently of online social media websites.

Figure 2: Hierarchical presentation of the media entity popularity metrics with common metrics across online social media

websites.

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4.2 Proposed Service Provider

Interface

Based on the study made in previous sections, we

aim to implement the proposed normalization of

popularity metrics related to each social entity

independently of online social media websites.

In this context, we propose the normalization in

the form a Service Provider Interface (SPI). The SPI

is considered as a contract between users to define in

a unified way the popularity metrics correspond to

the different social entities (i.e. text, video, photo

and user) independently of the online social media to

which they belong. In addition, this SPI allows users

to create extensible applications. Because it defines

a set of public interfaces and abstract classes that a

service defines.

These interfaces are implemented to allow the

creation of extensible applications based social

media entities popularity. We cite as examples the

prediction and detection of online trending topic that

aims to define the most trending topic across the

online community independently of the social

network, the detection of most popular brand sales in

online communities and the identification of the

most popular users on their networks.

All these applications require the identification

of the most popular social items across several social

media. So in order to avoid the heterogeneity of

metrics across social networks, the SPI gives the

opportunity for end-users to define the popularity

metrics of each social entity by simply implementing

the abstract provided method. The creation of the

contract of the social entities’ popularity

normalization is made through the implementation

of an SPI composed of two main interfaces: the

media popularity interface and the user popularity

interface.

 Media popularity interface: defines the SPI

specification of the media popularity service.

It includes methods that define the media

entity metadata, the media’s author metadata

and the user feedback metrics given the URL

of the social entity.

 User popularity interface: refers to the SPI of

the user popularity service. It provides

methods to define user’ metadata, activities

and connectivity that used to study popularity.

Besides, the proposed solution for the

normalization provides a set of service provider

classes that present the implementation of services

Figure 3: Excerpt from the SPI modeling in relation to Video Popularity.

Popularity Metrics’ Normalization for Social Media Entities

533

offered by the media and user popularity interfaces.

These services store the social entities URLs and

their related popularity information. It is worth also

to mention that the proposed solution implements a

service loader class introduced by the class

PopularityServiceLoader that follows the Singleton

design pattern and works as a template for the

relationships and interactions between classes and

ensuring that only a single instance of a class is ever

created.

Figure 3 presents the model of the media

popularity SPI implemented for the video popularity

provider class and it shows the interaction between

the client and the SPI using the popularity service

loader class. As they are categorized in the previous

hierarchy, the popularity metrics related to each

social entity are introduced by a set of classes. The

figure also includes two classes related to video

popularity metrics which are:

VideoPopularityMetadaMetrics and

VideoPopularityFeedBackMetrics.

Figure 4 is an excerpt from the whole

implemented model. It focuses on the case of video

entity but it is worth to mention that the definition of

other media entities popularity (i.e. text and photo)

implements a user popularity interface previously

introduced.

The SPI consumer extends the popularity

interfaces and implements its services to instantiate

his own popularity according to the application

needs and the availability of information. The

architecture of the SPI consumption is described in

Figure 4. The Client application identifies a task

related to a specific social entity (e.g. predict video

popularity).

He identifies the target social media websites

from which he defines his popularity metrics (e.g.

YouTube videos and Facebook videos).The client

implements the services relative to the target entity

popularity. So, the invocation of the specific services

(e.g. in video popularity interface) and the

instantiation of popularity is based on the metrics

extracted from the target social media. The

developed SPI is available on GitHub under the link

https://github.com/SebeiHiba/SocEntPopularitySPI.

Figure 4: integration of the proposed SPI in the applications based on the analysis of social entities popularity.

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534

The details that are not clear in Figure 4 can be

viewed in the code from the previous link.

5 CONCLUSION

In this paper, we dealt with the problem related to

the variety of metrics of the quantification of the

popularity of social entities (text, video, photo and

user) studied across several online social media

websites which are Facebook, Twitter, YouTube,

Google+ and Flickr. This variety is clear during the

investigation of the various studies established to

analyse the popularity of the social entities as well as

during the extraction of data related to social entities

using the various APIs provided by social

networking websites as Twitter search API and

Facebook Graph API. Our proposal to create a

normalized view of these metrics divides it into two

main categories: media (i.e. text, photo and video)

popularity metrics and user popularity metrics

extracted from profiles and pages that present the

user’ self-presentation. In each one of these

categories, the metrics are factorized according to

the ones adopted in the related works of popularity

analysis also according to the analysis of the

extracted data from social networking websites. In

addition, the normalized metrics are presented in a

hierarchical model to highlight the different

factorization levels. Moreover, the normalized view

is materialized via in an implemented SPI used as a

unified contract between users to express social

entities popularity independently of different online

social media. The SPI, available for researchers,

provides a set of basic services that can be extended

to define social entities popularity.

This work can be improved in future by moving

it to another level of abstraction through the

integration of Resource Description Framework

(RDF) to model the different popularity metrics.

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