Personality and Traits score Prediction from Social Media for Students

Abstract

Individual Personality can be predicted by using Online Social Networks. The Predicted personality finds its application in various fields. This paper proposes a system to predict the personality scores of the student without having to go through any personality analysis or taking any personality test. The results obtained clearly indicate that machine learning models can be effectively used for student's personality prediction from Big-5 Traits.

Full text

Personality and Traits score Prediction
from Social Media for Students
Prajwal S., Shahid Afridi, Patel Sana Riyaj, Srihari Hegde G.K., Aditya C.R.
Vidyavardhaka College of Engineering
Abstract: Individual Personality can be predicted by using
Online Social Networks. The Predicted personality finds its
application in various fields. This paper proposes a system to
predict the personality scores of the student without having
to go through any personality analysis or taking any
personality test. The results obtained clearly indicate that
machine learning models can be effectively used for
student’s personality prediction from Big-5 Traits.
1. INTRODUCTION
Social media has become one of the important platforms
for social interactions. Social networking sites (SNS)
make it easy to interact with people through social media.
Another boon of using social media is to create, share as
well as exchange information. There is abundant
information available as we scroll through the timeline.
Facebook, Twitter, Instagram are some of the examples of
social media sites. Facebook is to be treated as one of the
biggest used sites for human interaction, as we can build
new relationships and safeguard the existing ones.
Building new relationships is one of the
biggest challenges as one personality interact with other
new personality [3].
Personality is one of the important characteristic features.
Personality can be predicted by using Online Social
Networks (OSNs). The Predicted personality finds its
application in various fields. One such field is academics.
In this paper, we try to use student generated information
on social network (Facebook), which is easy to get and
predict student’s personality. We gather public data based
on their Facebook profiles. The personality of a person
predicts about the behaviour, weakness, activeness, the
response made in certain situation [3][1]. This
information can be used to have a better education
planning for a particular student within the institution,
which helps to improve the academic performance by
fully utilizing the talent of the student.
2. PERSONALITY MODELS
The PEN model
PEN Model [4] is based on figure investigation. The
factors for PEN model is Extraversion and Neuroticism.
These super components are composed of calculate
investigations of lower-order components. It incorporates
friendliness and positive influence (components of
Extraversion). These properties comprises of factor
analysis of lower-order behaviour such as working
together as a group in a total particular assignment.
With high score in neuroticism is constrained more
towards tension, discouragement, self-question and other
negative emotions. The Person will have an enthusiastic
reaction to the occasions that would not influence the vast
majority. Here the individual is increasingly inclined to
state of mind issue, depression, hesitance, and anxiety.
Psychoticism is described as the character type that is
slanted to put it all on the line, participate in against social
practices and tactlessness. This characteristic is generally
in close relationship with the traits of un-empathetic,
contemplative and ill will practices. [23] Previous works
on character conjecture with PEN model, using the
dataset from the site of Workshop from the
Computational Personality Recognition, has demonstrated
that male individuals inclined more towards extraversion
sentences than female and as separation, the female
respondents assessed to neuroticism sentences than that of
male individuals. Regardless, the female tends to some
degree higher to the psychoticism words than that of male
respondents [23]. Be that as it may, this methodology was
to recognize character of clients dependent on general
recognitions from Malaysians point of view.
Myers Briggs Type Indicator (MBTI)
Myers Briggs Type Indicator (MBTI) [5] is a technique
by which testing is done to indicate the personality of an
individual based on ability to make decisions. This test is
mainly used during the recruitment of people into job or
choosing career path based on one’s personality.
The BIG-FIVE Model
Big-5 model popularly known as OCEAN model [6] [2]
consists of Openness, Conscientiousness, Extroversion,
Agreeableness, and Neuroticism. Openness consists of six
concepts, or a scale, counting consistent creative ability,
tasteful affectability, thoughtfulness to inward sentiments,
inclinations for assortment, and mental interest.
Conscientiousness deduces on ache for to do a task well,
and to expect responsibilities to others genuinely.
Extroversion demonstrates how active and social a person
is. Agreeableness is personality trademark showcased in
person’s social behaviour that are seen as mindful,
pleasing, warm and valiant. People having high score in
neuroticism encounter sentiments such as uneasiness,
stress, fear, outrage, dissatisfaction, envy, blame,
discourage temperament and depression.
The Big-5 model is one of the most studied models and
many researches have proved that irrespective of any
language, test or the method of analysis the validity of the
model does not change [13] [14] [15] [16]. So Big5 is
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considered as the current definitive model of personality
[14].
Conscientious people tend to pay more attention to detail
and are very efficient and well organized and show self-
discipline, and motivate for achieving aim. The people
with more conscientiousness tend to finish assignments
and projects in advance, enjoy setting plans and be
attentive and more specific. The people with less
conscientiousness tend to be less organized, unlike to
schedule plan. Agreeableness is considered to be
subordinate trait that combines group of personality that
cluster together statistically. This trait shows itself in
individual behaviour, for example, helpful, warm and
social congruity. More agreeableness people tend to be
more naturally altruistic, have more concern for their
community, and make their comfort easily. They are more
likely to be patient with others.
3. BACKGROUND AND RELATED WORK
Despite using traditional method of questionnaire to find
the psychometrics and personality trait values, semantic
and textual data of the user on social media has been
proven to be reliable. It is more advanced and also
effective and efficient in terms of the dataset. With the
evolution of social media in recent times, the strong bond
of writing styles and personalities acts as a revealing
factor of characteristics of the user.
Oberlander and Nowson [21] has done research to
differentiate the personality of weblog authors using text,
by considering the data of report from the volunteers.
They studied the machine learning arrangements on Big 5
attributes and said that few models work superior to the
gauge. Since the work of Argamon et al., includes the
study of personality of individuals from different
viewpoints of the linguistics features [22,7],
differentiating based on structure [8] and based on
different machine learning algorithms [8,9]. There have
been several studies based on different social media
platforms.
Chirs Summer et al. [10] concentrate on Twitter clients
predominantly centred around Dark Trait, for example,
narcissism, Machiavellianism and psychopathy and
furthermore the connection with Twitter action, Dark
Trait and Big 5 Personality attributes. This examination
has demonstrated that the publicly supported calculations
were very flawed in foreseeing a person's Dark Trait from
Twitter movement yet the model was effective when
applied to huge gathering of individuals. This study
helped to see whether the Dark Traits are increasing or
decreasing over a population.
Sorayahakimi et al. [20] considered the associations
between character characteristics and understudy's
scholarly accomplishment and it was discovered that
these qualities were firmly identified with scholastic
accomplishment. The academic behaviour corresponding
to the individual trait was studied. Regression analysis
showed that personality traits were about in 48 percent of
variance in academic achievement. Also, it showed
academic achievement doesn’t come into picture in case
of gender. Finally, the conclusion that conscientiousness
was an important aspect of academic achievement was
drawn.
The main focus here is the Facebook dataset and
particularly the Facebook statuses of the students. In most
of the research studies, dataset is built using forms
collected by the users on filling the surveys offline.
Lampe et al. [11], Nosko et al. and De Brabander and
Boone [12] work showed us that, while college students
react most noteworthy in the account things (59%), an
example including college and non-college clients just
complete 25% of the data required in the profile. Lampe
et al. [11] proposed a model dependent on the quantity of
gatherings and the absolute number of client's profile.
This connection is greater with reference information than
others subtleties of low significance, at that point comes
contact and finally ideal information interests and side
interests. Lo Coco et al. [13] have introduced a
homogeneous order for character attributes of a client's
Facebook profile. This grouping assesses analysed
standards dependent on Facebook utilization, social and
character qualities of social associations.
4. METHODOLOGY
The reason for this research is to create a method to
predict the trait scores of the students using their
Facebook statuses. For training the models for personality
prediction, we used Random Forest algorithm. Since there
are five traits, totally 5 models were trained. To train the
models, vectorization for each of the statuses across the
features was done. The Random Forest algorithm has
been considered as the most precise prediction method for
classification and regression [17]. Also, the Random
Forest Algorithm can handle large databases efficiently
and it is non-sensitive towards noise and overfitting [18].
For testing the accuracy of trained Random Forest
models, the textual statuses from the students’ Facebook
accounts of the selected students was used. Also, students
were also made to fill a personality questionnaire and the
actual values of those students’ personality information
were collected using IPIP 50-item Big Five factor makers.
This was proposed by Gold Berg [19]. The inventory
contains 50 question and the answer of each question can
be Strongly Disagree-1, Disagree-2, Neither Agree nor
Disagree-3, Agree-4, Strongly Agree-5. The number
indicates score of each study shown that the Goldberg’s
IPIP 50-factor Big Five factor makers is fairly accurate
with only minor deviations [19]. In order to have a better
accuracy, more than 10 statuses of each student were
scraped. These statuses were then stored in a database.
Now by using the trained models, personality prediction
of each status is done and later the predictions across all
the statuses are averaged to get personality prediction of
each student. Now the student is allowed take the
personality test which consists of a questionnaire based on
Goldberg’s model. The corresponding score is stored in
the database.
After finding the scores for both the data (Facebook
statuses and from questionnaire), a compare function is
used to compare them and see how accurate the predicted
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181http://www.ijert.org
IJERTV9IS070601 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
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Vol. 9 Issue 07, July-2020
1345

values from trained model are to that of Goldberg’s IPIP
Big five factor makers.
5. RESULT AND DISCUSSION
This Random Forest models trained on Big-5 Traits were
tested on the textual statuses extracted from the students’
Facebook accounts of the selected students. The predicted
scores were compared with the scores generated through
IPIP 50-item Big Five factor makers from the answers to
questionnaire. Then the percentage difference was taken
between the scores generated by Facebook statuses and
that of questionnaire.
The Tables 1 and 2 show the results of personality
prediction for sample two students. The differences of
individual traits for first student are 10,17.2,17.14,8.16
and 9.09(in %). And that of the second student are
14.54,18.18,16.66,7.5 and 8.33. Similar results were
found for the remaining students selected for testing the
models.
Table 1
Table 2
Finally, the average of percentage differences among all
the 100 students was taken as shown in Table 3. The
differences are in the range of 5-20% which clearly
indicates that using machine learning models can be
effectively used for personality prediction from Big-5
Traits. Table 3
Personality Traits Average Difference among 100
students
Openness
12.27
Conscientiousness 17.7
Extraversion 16.9
Agreeableness
7.83
Neuroticism 8.71
As the number of statuses used to generate each
student’s data was increased the difference was
considerably reduced. It can also be said that if a
student is more vocal on social media it becomes
fairly simple to predict the personality trait scores
without examining the student into any sort of
personality test. The predicted results can be used by
the educational institutions to concentrate on the
performance enhancement of students and w to utilize
each student’s strength to the full extent.
6. CONCLUSIONS
Big-5 is considered as the most suitable and accurate
model of personality. The statuses of students in social
media can be scrapped to generate the Personality
Traits for training machine learning models.
Questionnaire consisting of IPIP 50-item Big Five
factor makers can be considered to validate the traits
prediction accuracy of machine learning techniques.
The results obtained clearly indicate that machine
learning models can be effectively used for student’s
personality prediction from Big-5 Traits.
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Traits Facebook
results Question
naire
results
Difference
(%)
Openness
20 18 10.00
Conscientiousness 24 29 17.2
Extraversion 29 35 17.14
Agreeableness
49 45 8.16
Neuroticism 55 50 9.09
Traits
Facebook
results Questionnaire
results Difference
(%)
Openness
58 50 14.54
Conscientiousness
18 22 18.18
Extraversion
24 20 16.66
Agreeableness
40 37 7.5
Neuroticism
60 55 8.33
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International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181http://www.ijert.org
IJERTV9IS070601 (This work is licensed under a Creative Commons Attribution 4.0 International License.)
Published by :
www.ijert.org
Vol. 9 Issue 07, July-2020
1347