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Hifza Afzal

Computer Science,
Balochistan University of information technology, engineering, and management
sciences, Quetta, Pakistan. Quetta, Pakistan

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Social media is increasingly broadening up its horizons by making possible
different ways of communication between individual and groups. These different
ways make us possible to understand one’s views, behavior, reactions, and
activities. Communication on Facebook is the common way to exchange the
different views. BUITEMS university students show their reactions on different
public Facebook pages through likes. The first classical signal ‘Like’ is the
common feedback expression on Facebook. The main objective of this paper is to
evaluate the reactions of different type of users on different posts. Secondly,
the impact of likes on a different type of posts. This has been done through
taking different measures on the dataset fetched from Facepager tool. The paper
findings reveal that Facebook ‘like’ reaction can be very helpful for
understanding students and their interests as well, in finding the moderate and
weak correlation and regression measures between a type of posts and likes.

Keywords— Social computing, social media, Post likes analysis, Facebook

I.     Introduction

the past years, social media has played a highly influential role in
influencing public opinion globally. This includes, for example, expressing
their satisfaction, happiness, anger, and disapproval textually or visually
1. Facebook has millions of active user groups sharing different views
globally about different topics 2. A large amount of social feedback
expressed by social signals (e.g. like +1, rating) are assigned to web
resources. ‘Like’ is the most common classical signal used to express reaction
towards posts social signal. Conducting analysis on users’ likes in order to
determine their views through understanding their reactions towards different

II.    Proposed approach

BUITEMS University,
having several pages with majority students following and participating in it.
Understanding their views regarding different issues is of core importance.
Finding meanings from their reactions on public participation will be done and
then classifying them distinctively on bases of their likes on different posts.
This will help to understand students’ different perspectives and activities by
deciphering meanings from their likes. This would be done through following

1.Capturing and storing public data from social
media (Facebook).

2.Classifying individuals based on social
media behavior.

3.Analyzing social media users’ likes, such as
their level of            reactions.

4.Predicting future activities based on social
media data.

5.Understanding public perceptions and views.

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paper has been organized in following order: Section III is based on component
details of our thesis. Section IV provides a complete methodology about the analysis
performed on Facebook likes. Section V shows the concluded results based on our
analysis. In Section VI some predictable future works are estimated. Section
VII concludes this paper based on our analysis.

III.   components

The dataset of 75 posts and its likes are obtained and is
used then in Rstudio for further analysis. Results have been obtained in the
form of charts, graphs, and tables.

A.    Dataset collection tool

Facepager is a dataset obtaining tool for social media public
pages. For analysis of likes Facepager (Version 3.7) has been used, to collect
Facebook posts and there likes.

B.    Dataset

The dataset used for analysis of Facebook post likes are
fetched from BUITEMS University public Facebook page (BUITEMS Social Corner).
The page current posts and its likes are obtained from Facepager tool through
selecting maximum pages option consisting of 75 posts. The dataset is exported in
.csv file, further used in Rstudio.

C.    Rstudio

An open-source free IDE used in this paper for statistical
computing on obtained dataset to perform several analyses resulting in several
answers obtained in the form of charts and graphs.

D.   Post types

For performing analysis, posts have been categorized into two
forms, that are video posts and without video or other posts as this will make
us easy to understand and evaluate the interests of students towards these two
different categories of posts. Through this analysis, we will find the effect
of number of likes on these two different types of posts.

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