Mapping Personality Traits through Keystroke Analysis

Felipe Valad

ao Goulart

and Daniel Oliveira Dantas

Departamento de Computac¸

ao, Universidade Federal de Sergipe, Brazil

Keywords:

Personality Traits Extraction, Big Five, Neural Networks.

Abstract:

Personality can be deﬁned as a set of psychological features that may determine how to think, act, and feel,

as well as may directly inﬂuence an individual’s interests. The Big Five model is widely used to describe the

main traits of the personality of an individual. This study aims to develop an approach to identify personality

traits from keystroke dynamics data using neural networks. We developed a non-intrusive approach to collect

keystroke dynamics data from the users and used a self-assessment questionnaire of personality to identify

Big Five personality traits. Experiments showed no evidence that the exclusive use of keystroke dynamics

characteristics can provide enough information to identify an individual’s personality traits.

1 INTRODUCTION

Affective computing is a branch of intelligent com-

puting that deals with the properties of users’ per-

sonality and emotion in computational systems. Its

goal is to identify, model, and implement human emo-

tion in a computational format, giving the system the

ability to react based on the user’s personality. The

personality can be deﬁned as a set of psychologi-

cal properties capable of determining the individual-

ity of someone based on the way he thinks, acts, and

feels (Pervin et al., 2004), inﬂuencing the behavior

(Carver, 2000), being public knowledge, although no-

body knows how to describe in a precise way (All-

port, 1961). The personality is known as a relatively

stable characteristic in an individual that can be mod-

iﬁed, but is relatively stable for about 45 years, start-

ing on the adult phase (Nunes et al., 2010) and is a

determining factor on the human behavior. The per-

sonality is capable of directly inﬂuencing the interests

of an individual, making the computational identiﬁca-

tion and modeling of personality a prerequisite for the

creation of new applications, system models, and cus-

tomizable virtual environments (Stathopoulou et al.,

2010).

Computational systems are developed to perform

a uniform behavior, independently of the user inter-

acting with it, using just the the input data. We may

enable computer systems to analyze the user’s input

https://orcid.org/0000-0001-6030-7651

https://orcid.org/0000-0002-0142-891X

data not just as a source of raw information but also

as a source of metadata capable of identifying individ-

uals and a group of users with similar behaviors and

interests. Doing so opens the doors to the construction

of a new class of computer systems, aimed at person-

alizing the user experience, adapting itself according

to each individual’s particular characteristics.

Keystroke dynamics is the process of extracting an

individual’s biometric pattern using the manner and

rhythm at which he types characters on a keyboard

(Shepherd, 1995). The data extracted from keystroke

dynamics can be used for authentication, identiﬁca-

tion, and analysis of the user’s particular character-

istics. This biometric pattern is available from any

conventional computer keyboard and can be easily ex-

tracted when looking for the data from a key holding

time and up time. Hold time, which is often found

in the literature as dwell time or down time, repre-

sents the time interval between pressing and releasing

the same key on a keyboard. Up time, also described

as ﬂight time or up-down time, represents the time

elapsed between releasing the current key and press-

ing the next one. From a dataset containing these two

characteristics, it is possible to determine the pattern

of typing of an individual, and from this pattern seek

the correlation with the characteristics of his person-

ality.

The demand for giving the computer the ability to

identify, interpret, and respond appropriately to a user

depending on his characteristics is an important step

in the evolution of human-computer interaction. Pre-

vious studies (Khan et al., 2008; Nahin et al., 2014;

474

Goulart, F. and Dantas, D.

Mapping Personality Traits through Keystroke Analysis.

DOI: 10.5220/0010456304740482

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 474-482

ISBN: 978-989-758-509-8

Solanki and Shukla, 2014) state that emotions have a

signiﬁcant role in this process.

Khan (Khan et al., 2008) developed a tool to ex-

tract individuals’ personality traits through the typing

rhythm and mouse usage, basing their experiments on

studies that identiﬁed that extroverts interact with the

interface of computer systems more quickly than an

introvert. As a result of his studies, Khan (Khan et al.,

2008) concluded that it is possible to measure a user’s

personality through his way of using the keyboard and

mouse. In this study, Khan (Khan et al., 2008) used

a reduced version of the NEO-IPIP (Neuroticism-

Extraversion-Openness International Inventory Item

Pool) as a form of self-assessment by the participants.

The NEO-IPIP questionnaire is composed of 120

questions and requires between 15 and 25 minutes to

be answered. The results obtained by Khan (Khan

et al., 2008) for the ﬁve personality traits mapped var-

ied between -0.4 and -0.56, which according to the

Pearson scale represent ”weak” and ”moderate” cor-

relations, respectively.

Khanna and Sasikumar (Khanna and Sasikumar,

2010) proposed an approach to detect the emotions

of computer users from data obtained from the use

of a conventional keyboard. The approach is based

on the study of more speciﬁc characteristics, such

as typing speed, mode, standard deviation, amount

of backspaces, and others, obtained from the analy-

sis of the hold time and up time of each individual.

This study included 300 participants (45% women

and 55% men), aged between 21 and 41 years, and

obtained up to 88.88% and 89.02% success rates in

the identiﬁcation of positive and negative emotions,

respectively.

The use of keystroke dynamics data as a source

of information for detecting emotional state was also

the subject of a study carried out by Epp, Lippold and

Mandryk (Epp et al., 2011), which, with only 12 par-

ticipants (10 men and 2 women), mapped 15 distinct

emotional states with success rates ranging between

77.40% and 87.80%. Zimmermann (Zimmermann

et al., 2003) present an approach similar to Khan

(Khan et al., 2008) when simultaneously using data

extracted from the keyboard and mouse, although the

studies of Zimmermann (Zimmermann et al., 2003)

do not focus directly on personality extraction but

on the measurement of mood. His aim is to im-

prove human-computer interaction without worrying

if the current mood comes from permanent personal-

ity characteristics or something temporary related to

the individual’s emotional state.

Supporting the usage of keystroke dynamics as a

method of extracting user data, Nahin (Nahin et al.,

2014) justify that the computer keyboard, even though

it is a cheap equipment, still allows communication

between humans and computers. In their work, seven

classes of predeﬁned emotions were used (joy, fear,

anger, sadness, guilt, shame, and disgust) to detect

the variation in the behavior of computer users dur-

ing the process of transition from one emotional state

to another. The work developed by them was done

so that no additional hardware other than a conven-

tional computer keyboard was needed. To carry out

their study, Nahin (Nahin et al., 2014) deﬁned two

different approaches: use of predeﬁned text, and; use

of free text. Not only was the user’s typing rate an-

alyzed, but also an analysis of the text he produced.

The data acquisition phase was carried out with only

25 volunteers, whose ages varied between 15 and 40

years, with approximately 45% of the participants be-

ing women and 55% men. As a result of their work,

Nahin (Nahin et al., 2014) achieved between 60% and

87% accuracy in identifying those emotions.

Solanki and Shukla (Solanki and Shukla, 2014)

also developed an approach to extract the emotional

state of individuals using data from the typing rhythm

and its correlation with self-assessment question-

naires. In their work, Solanki and Shukla (Solanki

and Shukla, 2014) aimed to identify the emotional

state based on the use of a conventional computer key-

board, focusing on identifying emotions: conﬁdence,

sadness, happiness, tiredness, nervousness, and anger.

As well as done by Nahin (Nahin et al., 2014), their

work involves two approaches: use of predeﬁned text,

and; use of free text. Both experiments obtained good

results in identifying the selected emotion classes, and

the use of predeﬁned ﬁxed text was more accurate in

identifying most emotions. In another of his studies,

Khan (Khan et al., 2015) analyzed 47 individuals to

identify programmers’ personality through the inter-

action with keyboard and mouse and the application

of self-assessment questionnaires. At the end of his

study, Khan (Khan et al., 2015) stated that it is pos-

sible to differentiate good programmers from not so

good programmers in an objective way by correlating

the data produced by them.

To identify characteristics such as gender and

age of individuals through the typing rhythm, Plank

(Plank, 2018) presented in her work evidence about

the strong relationship between the individual’s iden-

tity and the way he types. Buker (Buker et al., 2019)

also shows a similar approach to identify the typist

gender through keystroke dynamics, with accuracy

higher than 95%. Although these studies can iden-

tify characteristics such as age and gender with a high

accuracy, no details about the possibility of extract-

ing other characteristics, such as personality traits,

through the typing rhythm are presented.

Mapping Personality Traits through Keystroke Analysis

475

In this work, we aim to classify individual’s per-

sonality traits based on his keystroke dynamics. A

dataset was acquired containing subjects’ data from

a self-assessment personality questionnaire and their

keystroke dynamics. To do that, we used convolu-

tional neural networks with the keystroke dynamics

data as input and as output the level of each personal-

ity trait of the Big Five model.

2 METHODOLOGY

This study is focused on the analysis of the keystroke

dynamics of computer users to identify personality

traits. Keystroke dynamics is an automatic, non-

intrusive approach with a reduced cost of applica-

tion. A self-assessment questionnaire, based on the

Big Five model for describing personality traits, was

used. The objective of this study is to answer two

questions:

• Q1: Is it possible to measure how much a certain

personality trait is present in the personality of an

individual through the typing rhythm?

• Q2: Is it possible to determine which personal-

ity traits stand out in the individual’s personality

through the typing rhythm?

A dataset was acquired, pre-processed, and used

to train different neural networks to reach the objec-

tives of identifying an individual’s personality traits

from keystroke analysis. This section describes the

methodology used, divided in three steps:

1. Data acquisition

2. Pre-processing

3. Analysis

The data acquisition step was responsible for ac-

quiring the basic information for the accomplish-

ment of this study. It was done by capturing the

subjects’ keystroke data and the application of a

self-assessment personality questionnaire. In pre-

processing step, equalizations and conversions of the

obtained data were done to adapt them to each experi-

ment. In the analysis step, we trained different neural

networks to classify the individual’s keystroke data.

2.1 Data Acquisition

The data acquisition process was completely online,

aiming to be accessed by the largest possible number

of participants. It was implemented as a web page

built to extract the raw data of the participants’ typing

rhythm from a conventional computer keyboard and

then apply the Ten Item Personality Inventory (TIPI)

self-assessment questionnaire.

The web page was available for collecting data for

56 days. Of the 177 participants, 56 were female,

and 121 were male. Ages ranged between 12 and 46

years, with 24.83 years as the average age of the par-

ticipants.

The data acquisition process was divided into

three steps: contextualization; extraction of the

keystroke dynamics, and; extraction of personality

traits. Each stage is described in the sections below.

The average session length of a participant on

the data acquisition page, including contextualization,

was 11.27 minutes. Extraction of the keystroke dy-

namics and extraction of personality traits steps had

an average duration of 1.97 minutes and 1.61 min-

utes, respectively, which gives us an average duration

of 3.58 minutes to perform the two main steps of the

data acquisition process.

2.1.1 Contextualization

The ﬁrst step of the data acquisition process started

with presenting the data acquisition objective, a brief

contextualization about what the next steps would be

and what should be done by the participant in the next

steps. A free consent form was presented to guarantee

that the participant understood the details about the

conﬁdentiality of his information and that he agrees

with the purposes for which the data provided by him

in the next steps would be used. The text makes it

clear the possibility to give up at any time during the

data acquisition process.

2.1.2 Keystroke Data Extraction

After the contextualization step, the data acquisition

step effectively starts with presenting the predeﬁned

text. Participants are instructed to type the text exactly

as shown, in a text ﬁeld, twice in a row.

The text selected for data acquisition was designed

in the native language of the participants, so it was

not necessary to capitalize letters or add accents. The

text contains only a single punctuation symbol, with

a simple vocabulary composed of words common in

casual speech, with 194 characters.

All care in the text elaboration came from the

concern to reduce the discrepancies between different

users, which can be caused by the use of uncommon

words or a difﬁcult text.

From this process, it is possible to identify a typ-

ing rhythm for each participant, composed of a set of

characteristics extracted from the keypresses during

the data acquisition process. Such characteristics are

known as hold time (H), up time (U) and down-down

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

476

time (DD). The hold time represents the duration of a

keypress; the up time represents the time elapsed be-

tween releasing the current key and pressing the next

one, and; the down-down time represents the inter-

val between pressing the current key and pressing the

next one.

2.1.3 Personality Traits Extraction

The Big Five model is a model designed to represent

an individual’s personality, having been created in

psychology, and is currently an accepted and widely

studied model. This personality representation model

classiﬁes the personality as composed of ﬁve major

traits that determine each of the characteristics of be-

ing, thinking, and acting of an individual.

The Big Five traits are openness, agreeable-

ness, extraversion, conscientiousness, and neuroti-

cism. These ﬁve traits can be divided into six subsets

of characteristics, known as facets, to allow a more

precise and detailed personality analysis.

A standard way of extracting an individual’s per-

sonality traits is by applying a questionnaire con-

taining a series of questions to map each personal-

ity’s speciﬁc characteristics. There are different ques-

tionnaires based on The Big Five model. One of

them is the Neo-International Personality Item Pool

(NEO-IPIP), consisting of 300 questions distributed

equally among the big ﬁve traits. The participant

has to choose the answer that best suits him, among

the ﬁve available alternatives, which vary according

to the Likert scale (Likert, 1932), ranging from ”to-

tally agree” to ”strongly disagree”. Due to the num-

ber of questions that compose it, the application of

NEO-IPIP requires a large amount of time, resulting

in inaccurate answers due to the participant’s tired-

ness or even incomplete questionnaires due to partic-

ipants giving up during the process.

To reduce the amount of time required to apply

a self-assessment questionnaire, Gosling, Rentfrow,

and Sawnn (Gosling et al., 2003) developed the TIPI,

a self-assessment questionnaire derived from NEO-

IPIP and composed of only ten questions. Due to

its small size, TIPI can not extract Big Five char-

acteristics as accurately as NEO-IPIP does. How-

ever, all Big Five traits are mapped. Its application

is carried out similarly to NEO-IPIP, where the par-

ticipant must choose the answer that best suits him

from the seven available alternatives, following the 7-

point Likert scale, which vary from ”totally agree” to

”totally disagree”.

In this study, the TIPI questionnaire was used as

an approach to extract the participants’ personalities.

It was adapted to be used in the data acquisition web

page. The ten questions from the original question-

naire were presented sequentially, with their respec-

tive alternatives. The interface has just a ”next” button

to take the participant to the next question without al-

lowing him to change the previous question’s answer.

2.2 Pre-processing

The pre-processing step consists of removing irrele-

vant data, such as repeated or non-representative in-

formation, and performing transformations in the data

so that the resulting dataset is better suited for neural

network input than the original. We used the pre-

processing approach described in (Montalv

ao Filho

and Freire, 2006) to equalize the keystroke data. This

approach proved to be advantageous when applied to

keystroke data for biometric identiﬁcation. The equal-

ization function can be described as follows:

g(x) =

1 + exp(−

K(log

(x)−µ

)

where K is a constant with value 1.7 and x is a time in-

terval. The normalization function maps the values of

x to a normal distribution with mean µ

and standard

deviation σ

, assuming that x follows approximately

a distribution log-normal.

2.3 Analysis

With the data obtained in the data acquisition pro-

cess, artiﬁcial neural networks were trained using the

keystroke data as input and with information about

the personality traits as output. The neural network

architectures were composed of an input layer, a hid-

den layer, and an output layer.

To classify the keystroke data, neural networks

with two different output types were trained:

• Output of seven Likert scale values, as obtained

with the TIPI questionnaire.

• Output of two values, obtained by the binariza-

tion of the results of the TIPI questionnaire. Re-

sults with values greater than or equal to 5 were

mapped to 1, while results less than 5 were

mapped to 0.

Figure 1 represents the structure of the neural net-

work used in the Likert scale approach. Figure 2, on

the other hand, represents the structure of the neural

network used in the binarized approach.

The two output formats of the neural networks are

closely related to the questions Q1 and Q2 proposed

in the beginning of this section. The neural networks

with output in the Likert scale format aim to identify

exactly how much a speciﬁc personality trait repre-

sents the individual’s personality (Q1). It can be di-

rectly compared with the result obtained through the

Mapping Personality Traits through Keystroke Analysis

477

Figure 1: Structure of the neural network used in the Likert

scale approach.

Figure 2: Structure of the neural network used in the bina-

rized approach.

application of the TIPI questionnaire. The neural net-

works with binary outputs aim to identify whether a

personality trait stands out in an individual’s personal-

ity (Q2). The training and evaluation of this approach

were done with the binarized TIPI questionnaire. In

both approaches, the neural network output is consid-

ered correct when its value is equal to the value of the

TIPI questionnaire.

Raw keystroke dynamics data were used as input

to the neural networks due to a lack of a formal deﬁ-

nition in the literature regarding the relationship of a

speciﬁc characteristic of the keystroke dynamic, such

as the typing speed, with a speciﬁc personality trait.

Raw data were used as input in order to the neural net-

work deﬁne by itself which characteristics are most

relevant for each personality trait. As an alternative

to the raw keystroke data, the equalized input data,

pre-processed as described in Subsection 2.2 was also

used.

The acquired keystroke data was used in three dif-

ferent ways as the input of the network: only the hold

time data; only the down-down time, and; the com-

bination of hold time and down-down time data si-

multaneously. During the data acquisition process,

376 time intervals were extracted from the keyboard

events, 188 of them referring to hold time and 188

referring to up time. The down-down time intervals

were calculated from these two vectors, also com-

posed of 188 time intervals.

Twelve different experiments were done. The ex-

periments are deﬁned by all combinations of three in-

put types, two output types, and the use or not of the

equalization method. In each experiment, ﬁve neural

networks were trained, one for each personality trait,

i.e. each trait was analyzed individually by its respec-

tive neural network.

The experiments were performed by applying the

data to multilayer neural networks with similar ar-

chitecture but with the number of inputs and outputs

varying according to the data being used. The number

of neurons in the middle layer is the same as the num-

ber of inputs. From the 177 participants in the data

acquisition step, 85 were used in the neural network

training process, 46 were used for the validation step,

and 46 for the testing step.

3 RESULTS

The experiments presented in this work were carried

out using the input data selected in the neural network,

and comparing them with the expected output for each

of the inputs.

3.1 Likert Scale Experiments

The Likert scale (Likert, 1932) is a type of scale

where the interviewees must specify their level of

agreement with a statement, having been developed

speciﬁcally for psychometric questionnaires. It can

be presented in the format of three, ﬁve, or seven

points. In this study, seven points were used, that is,

each question has seven answer options, as this is the

standard adopted in the development of the TIPI ques-

tionnaire.

Data equalization was performed following the

method described by (Montalv

ao Filho and Freire,

2006), a method used in biometric analyses aimed

at authentication purposes. As it has shown good re-

sults, it was decided to use the same approach in order

to compare the results obtained by the experiments

with and without the application of the equalization

method. However, as stated by (Montalv

ao Filho and

Freire, 2006) in their study, equalization approaches

in conjunction with neural networks can be consid-

ered redundant given that, due to its learning process,

the neural networks equalize the input data. The ap-

plication of the equalization method was carried out

using mean (µ) 128.4094 and standard deviation (σ)

842.9373.

Figure 3 shows a comparison of the results ob-

tained in all the experiments carried out following the

approach with the Likert scale.

In the literature, the down-down time is consid-

ered the characteristic of the typing rhythm that car-

ries the greatest amount of information about the in-

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

478

Figure 3: Accuracy (%) of Likert scale experiments.

dividual. For this reason, the down-down time was

extracted together with the other characteristics of the

typing rhythm (hold time and up time), in order to

provide the neural network with as much information

as possible with the minimum necessary characteris-

tics. Figure 3 shows a comparison between the equal-

ized approach and the non-equalized approach, using

data from the down-down time, where the results ob-

tained varied between 8.70% and 50%.

From the analysis of all the results obtained by the

neural network approaches with output on the Likert

scale, presented in the Figure 3, it was observed that

the success rates varied between 6.52% and 50%, with

an average of 24.60% of correct answers, which is un-

satisfactory for the prediction of how much a person-

ality trait is present in an individual’s personality.

3.2 Binary Experiments

Neural networks with binary outputs were developed

to identify which personality traits stand out in an in-

dividual’s personality. The binary approach allows a

clearer identiﬁcation of which personality traits stand

out in a speciﬁc individual’s personality, thus allow-

ing the network to predict which personality traits

are in evidence in a given personality. In this stydy,

traits classiﬁed with a value greater than or equal to

5, according to the 7-point Likert scale, are consid-

ered present (1), and traits with values less than 5 are

considered absent (0).

Even with accuracies reaching 95.65%, it cannot

be said with certainty that the hold time input with

binary scale output approach, shown in Figure 4, is

capable of indicating whether the personality traits

studied are likely to be mapped through the binary

approach, that is, to determine whether or not a per-

sonality trait is highlighted in an individual’s person-

ality, without ﬁrst validating the results obtained in

conjunction with the analysis of the probability distri-

Figure 4: Accuracy (%) of binary experiments.

bution between the classes used in this approach.

Figure 4 shows the accuracies for each of the ex-

periments carried out through the application of the

binary approach, with results between 30.43% and

95.65%. With accuracies above 95%, as shown by

the personality trait agreeableness in the Figures 4,

the binary approaches in general presented accuracies

considered high in a preliminary analysis.

However, when compared with the probability

distribution of the analyzed classes, presented in Sec-

tion 3.3, we can conclude that the results obtained

are unsatisfactory as it is evident that the same result

could be achieved by a classiﬁer guessing the most

frequent class. Thus, to verify how signiﬁcant the

results obtained are, it was decided to perform addi-

tional tests, presented in Section 3.3.

3.3 Prior Knowledge Analysis

To validate the neural networks’ results, we carried

out an additional experiment to compare them with an

approach using prior knowledge. In the prior knowl-

edge approach, the probability distribution of the an-

alyzed classes is known, as shown in Figure 5 and

Figure 6.

This analysis was performed with each of the out-

put types produced by neural networks (Likert scale

and binary), in order to identify which of the possi-

ble outputs is the most repeated (mode) in the train-

ing set of each approach, and then use that answer as

the only answer to predict the test set data. Thus, the

aim is to prove that the experiments developed using

neural networks do not perform better than a purely

statistical approach, and these results are not relevant

enough to predict an individual’s personality traits.

Figure 7 and Figure 8 illustrate the results ob-

tained when comparing these experiments. Figure 7

and Figure 8 show that, although some of the results

obtained by neural networks are superior to those of

Mapping Personality Traits through Keystroke Analysis

479

Figure 5: Probability distribution over the Likert scale.

Figure 6: Probability distribution over the binary scale.

analysis with prior knowledge, it is not possible to

safely say that neural networks are capable of infer-

ring the personality of an individual.

3.4 Hypothesis Testing

A series of hypothesis tests were done to verify the

reliability of the results obtained. One test for each

personality trait was done to conﬁrm that the results

are unsatisfactory to predict an individual’s personal-

ity traits through keystroke dynamics characteristics.

Hypothesis testing is a statistical method for an-

alyzing samples through the theory of probabilities.

In order to perform a hypothesis test it is necessary

to have two hypotheses, known as (i) null hypoth-

esis (H

); and (ii) alternative hypothesis (H

). The

null hypothesis is the hypothesis that we assume to be

true, while the alternative hypothesis is the hypothe-

sis that will be considered true if the null hypothesis

is rejected.

In a hypothesis test, two types of errors can oc-

cur. Type I error is the rejection of the null hypothesis

) when it is actually true. On the other hand, Type

II error is the failure to reject a false null hypothe-

Figure 7: Neural network vs. prior knowledge using Likert

scale.

Figure 8: Neural network vs. prior knowledge using binary

approach.

sis (H

). Our null hypothesis (H

) states that an ap-

proach based on neural networks is as or less effective

than choosing the most likely result, while our alter-

native hypothesis (H

) states that an approach based

on neural networks is more effective than choosing

the most likely outcome. The following formula was

used to perform the hypothesis tests:

z =

a −b

√

(1)

where a represents correct classiﬁcations by the neu-

ral network, b represents the correct classiﬁcations by

the approach with prior knowledge, both expressed in

the number of people, n represents the size of the test

population, and σ represents the population standard

deviation.

The hypothesis test was built with a 5% signiﬁ-

cance level, using the Equation 1, which is a unilat-

eral hypothesis test on the right, in order to prove that

the alternative hypothesis (H

) is true. For a hypoth-

esis test with these characteristics, the critical region

regarding the level of signiﬁcance is represented by

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

480

Table 1: hypothesis testing of Likert scale experiments.

Personality trait Neural network

result (a/n)

Average expected

output (b/n)

Standard devi-

ation (σ)

Hypothesis test-

ing result (z)

Extraversion 17.39% 17.39% 2.57 0.00

Agreeableness 50.00% 60.87% 3.39 -10.00

Conscientiousness 23.91% 23.91% 2.80 0.00

Neuroticism 26.09% 21.74% 2.98 4.55

Openness 34.78% 34.78% 3.12 0.00

Table 2: Hypothesis testing of binary approach experiments.

Personality trait Neural network

result (a/n)

Average expected

output (b/n)

Standard devi-

ation (σ)

Hypothesis test-

ing result (z)

Extraversion 58.70% 58.70% 3.34 0.00

Agreeableness 95.65% 82.61% 1.38 29.48

Conscientiousness 54.35% 54.35% 3.38 0.00

Neuroticism 69.57% 60.87% 3.12 8.69

Openness 69.57% 69.57% 3.12 0.00

z > 1.64. Thus, for the alternative hypothesis to be

considered true, that is, to reject the null hypothesis,

the result obtained through the hypothesis test, repre-

sented by z, must be greater than 1.64.

Table 1 shows the hypothesis tests performed with

the Likert scale approach, and Table 2 shows the hy-

pothesis tests performed with the binary approach.

The tables present the resulting z values obtained

through the hypothesis tests performed for each ap-

proach. To perform the hypothesis tests, we used the

same test set used for evaluating the neural networks,

with size n = 46. The results shown in the column

”neural network result” refer to the best results (high-

est accuracy) of the approach in detecting the person-

ality trait in question.

Analyzing the result of the hypothesis tests, repre-

sented by the column z in the tables 1 and 2, we can

see that only three of them obtained a result inside the

critical region, i.e., z > 1.64. In the other seven tests,

the z is outside the critical region, so we do not reject

the null hypothesis, which states that neural networks

are no better than the a priori approach with a conﬁ-

dence of 95%. The characteristics extracted from the

keystroke dynamics probably do not present enough

information to map an individual’s personality traits.

The similarity between the results obtained through

an approach based on the choice of the most likely

personality trait and an approach based on neural net-

works is evidenced by the number of tests with z equal

to zero.

4 CONCLUSIONS

In this study, 12 different experiments were carried

out to search for the relationship between an indi-

vidual’s typing rhythm and his personality traits. A

dataset was acquired with the keystroke dynamics

data extracted from 177 volunteers and the person-

ality traits of each one. A data acquisition tool was

developed speciﬁcally for this study and was made

available online. The experiments were designed to

answer two different questions. Q1: Is it possi-

ble to measure how much a given personality trait

is present in an individual’s personality through the

typing rhythm? And Q2: Is it possible to determine

which personality traits stand out in the individual’s

personality through the typing rhythm?

Experiments aimed at answering question Q1

measured the personality traits’ intensity in a 7-point

Likert scale for each participant. The best neural net-

work classiﬁers resulted in the following accuracies

for each personality trait: extraversion 17.39%; agree-

ableness 50.00%; conscientiousness 23.91%; neuroti-

cism 26.09%, and; openness 34.78%.

In the experiments aimed at answering question

Q2, a binary approach was used. The objective of

the experiments was to identify whether or not a per-

sonality trait is present in an individual’s personality.

So, the results in Likert scale were binarized. Values

greater or equal to 5 were mapped to 1; otherwise,

they were mapped to 0. The binary experiments re-

sulted in higher accuracies than the Likert scale exper-

iments: extraversion 60.87%; agreeableness 95.65%;

conscientiousness 54.35%; neuroticism 69.57%, and;

openness 69.57%. However, the improvement ob-

Mapping Personality Traits through Keystroke Analysis

481

served is due to the reduction in the number of classes

analyzed from seven to two, resulting in higher accu-

racies. Even so, the results obtained by this approach

are equivalent to those from the approach with prior

knowledge.

Finally, when analyzing the results obtained, we

concluded that it was not possible to identify an in-

dividual’s personality traits from the typing rhythm

using the approaches described in this work. The ex-

clusive use of keystroke dynamics characteristics may

not provide enough information to map the personal-

ity traits of an individual.

Even knowing the limitations of a conventional

computer keyboard, as a source of information on

characteristics capable of differentiating individuals

from each other, the usage of the keyboard as an ap-

proach is encouraged by Solanki and Shukla (Solanki

and Shukla, 2014), Nahin (Nahin et al., 2014) and Ko-

lakowska (Kołakowska et al., 2013). They conﬁrm

the beneﬁts of using the typing rhythm from a con-

ventional computer keyboard, which is inexpensive

and already widely used in most computer systems,

in addition to being a non-intrusive approach and eas-

ily adaptable to different computer systems, including

smartphones with touchscreens.

Due to the unsatisfactory results on extracting per-

sonality traits, we believe that it is not possible to

clearly map an individual’s personality traits through

the keystroke dynamics. On the other hand, we be-

lieve in the possibility of success in the development

of studies aimed at new approaches and experiments

focused on mapping information with a greater rela-

tionship with human motor functions, such as emo-

tions and emotional state, as presented by Zimmer-

mann (Zimmermann et al., 2003). Such work can be

performed using an adaptation of the data acquisition

tool already developed, using the same data acquisi-

tion process, adapting only the self-assessment ques-

tionnaire to map emotions and emotional states.

REFERENCES

Allport, G. W. (1961). Pattern and growth in personality.

Buker, A. A., Roffo, G., and Vinciarelli, A. (2019). Type

like a man! inferring gender from keystroke dynamics

in live-chats. IEEE Intelligent Systems, 34(6):53–59.

Carver, C. S. Scheier, M. F. (2000). Personality is a dy-

namic organization, inside the person, of psychophys-

ical systems that create the person’s characteristic pat-

terns of behavior, thoughts and feelings.. (Boston).

Perspectives on personality.

Epp, C., Lippold, M., and Mandryk, R. L. (2011). Identi-

fying emotional states using keystroke dynamics. In

Proceedings of the sigchi conference on human fac-

tors in computing systems, pages 715–724.

Gosling, S. D., Rentfrow, P. J., and Swann Jr, W. B. (2003).

A very brief measure of the big-ﬁve personality do-

mains. Journal of Research in personality, 37(6):504–

528.

Khan, I. A., Brinkman, W.-P., Fine, N., and Hierons, R. M.

(2008). Measuring personality from keyboard and

mouse use. pages 1–8.

Khan, I. A., Khalid, O., Jadoon, W., Shan, R. U., and Nasir,

A. N. (2015). Predicting programmers’ personality

via interaction behaviour with keyboard and mouse.

PeerJ PrePrints, 3:e1183v1.

Khanna, P. and Sasikumar, M. (2010). Recognising emo-

tions from keyboard stroke pattern. International jour-

nal of computer applications, 11(9):1–5.

Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W.,

and Wr

obel, M. R. (2013). Emotion recognition and

its application in software engineering. pages 532–

539.

Likert, R. (1932). A technique for the measurement of atti-

tudes. Archives of psychology.

Montalv

ao Filho, J. R. and Freire, E. O. (2006). On the

equalization of keystroke timing histograms. Pattern

Recognition Letters, 27(13):1440–1446.

Nahin, A. N. H., Alam, J. M., Mahmud, H., and Hasan, K.

(2014). Identifying emotion by keystroke dynamics

and text pattern analysis. Behaviour & Information

Technology, 33(9):987–996.

Nunes, M., Bezerra, J. S., Reinert, D., Moraes, D., Silva,

E. P., and Pereira, A. J. (2010). Computac¸

ao afetiva

e sua inﬂu

encia na personalizac¸

ao de ambientes edu-

cacionais: gerando equipes compat

ıveis para uso em

avas na ead. Educac¸

ao E Ciberespac¸o: Estudos, Pro-

postas E Desaﬁos. Aracaju: Virtus Editora, 1:308–

347.

Pervin, L. A., Cervone, D., and John, O. P. (2004). Person-

ality: Theory and Research. Wiley, 9th edition.

Plank, B. (2018). Predicting authorship and author traits

from keystroke dynamics. In Proceedings of the Sec-

ond Workshop on Computational Modeling of Peo-

ple’s Opinions, Personality, and Emotions in Social

Media, pages 98–104.

Shepherd, S. (1995). Continuous authentication by analysis

of keyboard typing characteristics.

Solanki, R. and Shukla, P. (2014). Estimation of the user’s

emotional state by keystroke dynamics. International

Journal of Computer Applications, 94(13).

Stathopoulou, I.-O., Alepis, E., Tsihrintzis, G. A., and

Virvou, M. (2010). On assisting a visual-facial af-

fect recognition system with keyboard-stroke pattern

information. Knowledge-Based Systems, 23(4):350–

356.

Zimmermann, P., Guttormsen, S., Danuser, B., and Gomez,

P. (2003). Affective computing—a rationale for mea-

suring mood with mouse and keyboard. Interna-

tional journal of occupational safety and ergonomics,

9(4):539–551.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

482