Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Abstract

Background
Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions.

Objective
The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets.

Methods
In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets.

Results
Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found.

Conclusions
Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

Full text

Original Paper
Identifying Insomnia From Social Media Posts: Psycholinguistic
Analyses of User Tweets
Ahmed Shahriar Sakib1, BSc; Md Saddam Hossain Mukta2, PhD; Fariha Rowshan Huda1, BSc; A K M Najmul Islam3,
PhD; Tohedul Islam1, MSc; Mohammed Eunus Ali4, PhD
1American International University-Bangladesh, Dhaka, Bangladesh
2United International University, Dhaka, Bangladesh
3LUT University, Lappeenranta, Finland
4Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
Corresponding Author:
Md Saddam Hossain Mukta, PhD
United International University
Madani Ave
Natun Bazar
Dhaka, 1216
Bangladesh
Phone: 880 1712 095216
Email: saddam@cse.uiu.ac.bd
Abstract
Background: Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during
the night. As social media have become a ubiquitous platform to share users’thoughts, opinions, activities, and preferences with
their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems,
including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research
gaps in predicting insomnia from word usage patterns and correlations between users’insomnia and their Big 5 personality traits
as derived from social media interactions.
Objective: The purpose of this study is to build an insomnia prediction model from users’psycholinguistic patterns, including
the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets.
Methods: In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia
patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the
words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a
double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived
from user tweets.
Results: Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs
are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage
patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg,
“damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg,
“crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores
for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high
conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and
insomnia was also found.
Conclusions: Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into
a software system can help family members detect insomnia problems in individuals before they become worse. The software
system can also help doctors to diagnose possible insomnia in patients.
(J Med Internet Res 2021;23(12):e27613) doi: 10.2196/27613
KEYWORDS
insomnia; Twitter; word embedding; Big 5 personality traits; classification; social media; prediction model; psycholinguistics
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Introduction
Background
Insomnia, a type of sleep disorder, is the inability to fall asleep
or stay asleep at night. It is one of the most prevalent mental
health symptoms globally [1]. One study [2] suggests that
approximately 30% of adults worldwide exhibit insomnia
symptoms, like difficulty initiating and maintaining sleep and
waking up too early. People with insomnia might also
experience other problems, such as depression, anxiety, and
excessive alcohol consumption [3].
With the unprecedented growth of smartphone and internet
technologies, social media has now become a ubiquitous
platform that reflects users’ daily activities, preferences, and
beliefs. These social media platforms have become a means to
share health information [4-9] for many users. For example,
Paul et al [10] have stated that Twitter has become a common
place to discuss a wide range of health information, including
insomnia and other mental health conditions such as depression,
stress, and anxiety. Several other studies [10-12] also report
that Twitter is used as a platform to share symptoms [11], seek
help, and exchange advice [12].
As insomnia is a mental health disorder, the illness might have
a strong connection with human personality attributes. In fact,
prior studies [13-15] suggest that insomnia has links with certain
personality traits. Therefore, in this study, we attempted to
derive personality traits from users’ social media interactions
and use these traits along with users’ word usage patterns to
predict insomnia. To the best of our knowledge, our study is
the first to investigate from social media interactions whether
personality traits have an association with insomnia. Predicting
insomnia by analyzing users’ tweets has a number of real-life
applications. For example, friends and parents can identify
problems in their loved ones, while health care providers can
use the system to diagnose insomnia and can build an automated
early warning system.
Insomnia and the Big 5 Personality Traits
Many adults experience short-term (acute) insomnia, which
lasts for days or weeks. Acute insomnia is common and often
brought on by situations such as stress at work, family pressures,
or a traumatic event. Some adults have long-term (chronic)
insomnia that lasts for several months or years [16]. In most
cases, chronic insomnia may be a side effect of other problems
[16]. Insomnia not only reduces individuals’ energy levels but
also degrades their health, work performance, and quality of
life. There are several causes of insomnia [16-18], including
mental health disorders, such as posttraumatic stress disorder.
Antidepressants, asthma medications, and blood pressure
medications can also lead to sleep disorders. Medical conditions
such as chronic pain, cancer, diabetes, heart disease, asthma,
gastroesophageal reflux disease, overactive thyroid, Parkinson
disease, and Alzheimer disease can also cause insomnia. High
consumption of caffeine, nicotine, or alcohol may also prevent
sleep and lead to insomnia [16].
Personality differentiates individuals in their patterns of
thinking, feeling, and behaving [19]. The Big 5 personality scale
is a common scale for measuring personality [19,20]. The Big
5 model has 5 different personality traits: openness,
conscientiousness, extroversion, agreeableness, and neuroticism.
People with a high level of openness have a tendency to reflect
on ideas, innovate, and appreciate values. People with high
conscientiousness are cautious and meticulous and tend to seek
achievement. People with high extroversion have a tendency
to seek excitement and show positive emotions. People with
high agreeableness tend to be sympathetic, trusted, and merciful
to others. Neurotic individuals show negative emotions, such
as anxiety, inhibition, anger, and depression. Personality traits
are important factors that are associated with disorders [21].
For example, prior research has found that conscientiousness
and neuroticism are related to insomnia [22,23]. Personality is
also the key factor for dropout and treatment resistance in
cognitive behavioral therapy for insomnia [24].
Social Media and Insomniac Patterns
A few studies have been conducted to predict insomnia by
analyzing the content of social media. Michael et al [10]
described a method that uses Twitter for public health research.
They exploited the Ailment Topic Aspect Model to create
structured disease information from tweets that they used for
public health metrics. These authors [25] also reported that early
detection of disease outbreaks, medication safety, health
behaviors, and individual well-being can be investigated through
social media data, and applied traditional natural language
processing tools to analyze social media content. Rice et al [26]
found that young people may be at risk of negative consequences
from new technologies and online media. Therefore, social
media platforms are important for measuring young people’s
mental health states. Andrew et al [27] conducted a study to
identify common mental health topics from popular social media
platforms and identified common mental health topics such as
anxiety, depression, and sleep problems. Jamison-Powell et al
[9] completed a study on discussions of insomnia on Twitter.
Through an analysis of 18,901 tweets, they discovered that when
the word “insomnia” appears in users’ tweets, they are likely
to convey strong negative health information. These authors
mainly conducted their analysis over 2 different themes: coping
with insomnia and describing experiences with insomnia. For
the first theme, users share symptoms and coping strategies on
Twitter, while for the second theme, users share frustration.
However, the authors did not build a prediction model and did
not explore the association between users’ personality and
insomnia. McIver et al [28] examined how 2 groups of Twitter
users—sleep and nonsleep groups—were active on social media.
They found that the nonsleep group showed negative sentiment
over social media. Suarez et al [29] conducted a study on the
real-time streaming of Spanish tweets for insomnia prediction
by analyzing 54,432 tweets and built a classifier whenever
insomnia phrases appeared. They used term frequency-inverse
document frequency to find features of n-grams and then applied
different classifiers including support vector machines and
k-nearest neighbors.
It is clear that prior studies suffer from the following deficits
in research: prior studies largely built classifiers based on
explicit insomnia phrases (eg, “insomnia,” “sleepless”) rather
than the linguistic cues and relationships between the words,
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the authors did not build any novel machine learning models,
and, to the best of our knowledge, no study predicted insomnia
based on users’ personalities from social media interactions.
The aim of this paper was thus to address these research gaps.
Research Objectives
The main objective of this study is to predict insomnia by
analyzing users’psycholinguistic characteristics (eg, word usage
patterns) and Big 5 personality traits as derived from their
tweets. In this study, we built a rigorous classification model
to predict users’ insomnia from their interactions on Twitter.
We collected a total of 4,198,683 tweets from 1574 users and
classified our training data set into 2 classes: individuals who
suffer from insomnia and individuals who do not suffer from
insomnia. We then conducted psycholinguistic-based analyses
of users’ tweets by using 2 popular tools: Empath [30] and
Linguistic Inquiry and Word Count (LIWC) [31]. We also
analyzed users’ tweets by using the bidirectional encoder
representations from transformers (BERT) [32] word embedding
technique to investigate semantic relationships between words.
We carefully conducted both psycholinguistic- and word
embedding–based analyses to find insights into users’ tweets.
We then integrated users’personalities with the psycholinguistic
models. Finally, we built double-weighted, ensemble-based
classification models by combining psycholinguistic-, word
embedding–, and personality-based models.
In summary, our study provides the following contributions: a
large insomnia data set consisting of 1574 users from different
geographical locations; novel insomnia classification models
comprising psycholinguistic, word embedding, and Big 5
personality attributes as derived from users’tweets; and a novel
and rigorous double-weighted, ensemble-based classification
model to predict insomnia.
Methods
In this paper, we performed the 5 following steps to predict
insomnia by analyzing users’ word use patterns in tweets: (1)
user selection—first, we found a total of 1574 users by using
Twitter’s advanced search technique [33] and divided the users
into 2 groups, users with sleep disorders and users with no sleep
disorder; (2) linguistic analysis—we performed 2 different types
of linguistic analyses, psycholinguistic-based analysis and word
embedding–based analysis; (3) correlation analysis—we found
correlations between users’psycholinguistic patterns and sleep
disorders by using Fisher’s latent Dirichlet allocation (LDA)
[34]; (4) model building—we built 3 different machine learning
models by using psycholinguistic features, word embedding
attributes, and the Big 5 personality traits; and (5) ensemble of
the models—finally, we built a double-weighted ensemble
model by integrating the previous models. These steps are
further detailed in the subsequent subsections.
Data Collection
We collected tweets from a total of 1574 users. We searched
phrases such as “insomnia” and “sleepless” by using Twitter’s
advanced search technique. We divided the users into 2 groups:
Insomnia Yes and Insomnia No. We then manually annotated
the files of the users’tweets with the Insomnia Yes and Insomnia
No labels.
We confirmed that randomly selected users were neither
organizational nor celebrity profiles. We manually checked
whether Insomnia Yes users disclosed their own sleep disorders.
We found users’tweets and their locations. The following are
a few sample tweets shared by insomniac users on Twitter: “I
wish insomnia wasn’t a part of my life” and “my insomnia is
officially the worst I got into bed at 12, slept for 2 hours, and
am still wide awake at 6:30 AM.” These tweets indicated that
these users suffered from insomnia. If we found several such
tweets (28.60 times on average) in the newsfeed or tweets of a
user, we labeled that user as Insomnia Yes.
For Insomnia No users, we randomly selected users who did
not have these search phrases in their tweets. We also manually
checked whether these users shared any sleeping-related issues,
and if they did, we discarded them from the list. After labeling
Insomnia Yes or No on the users’csv files containing the tweets,
we removed text such as “insomnia” and “sleepless” from the
users’tweets. By removing these phrases, we created a bias-free
data set that contained cues regarding users’ insomnia without
explicitly mentioning these phrases. Users who used contractions
tend to have higher levels of fluency in their pronunciation,
while users who do not use contractions are largely nonnative
speakers. People may contract many words every day but mainly
focus on the word “not” during contraction [35]. For the above
reasons, we kept contracted words in our tweets to indicate a
difference between the patterns of writing between native and
nonnative speakers. Later, we conducted our full analysis again.
It is important to note that we only focused on users who express
their insomnia on Twitter. If they are actually suffering from
insomnia, then their word usage pattern could be insightful. We
collected users’tweets, and multiple referees labeled these users
after investigating their writing, their geographic locations, and
the irregular timestamps of the tweets. In this way, we could be
confident that a given user was suffering from insomnia. We
emphasized a users’ concern from their tweet content to make
decisions about their behavior. It is likely that when users
encounter a grave issue, they frequently express their problem
to others. Thus, we labeled a user as Insomnia Yes if we found
a high frequency (average 28.60) of mentions of the problem.
We also found the maximum, minimum, and SD of users’
number of posted tweets regarding the aforementioned problem
to be 363, 8, and 24.57, respectively. These numbers revealed
that some users suffering from extreme insomnia-related
problems continue to tweet at nighttime about their
sleeplessness.
There might be another group of users who have insomnia but
do not disclose the problem in their tweets. We did not consider
those users in our study. We also found a large number of users
who were noninsomniacs. We again manually checked the users’
data and tweet times based on geographic location to confirm
that these users were noninsomniacs. If we had users who were
noninsomniacs but tweeted in different time patterns to regular
users, then we discarded their tweets from our study. As the
number of insomniac users is lower in comparison to
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noninsomniac users, we took less data of noninsomniac users
to make a balanced data set.
It is important to note that there might have been users who had
insomnia but do not disclose it in their tweets. However, we
argue that they are not in large numbers, as previous studies
show that in social media people reveal their actual behavior
without idealization [36] and share private traits [37].
Next, we identified the genders of Twitter users based on various
attributes to investigate correlations between users’gender and
insomniac behavior. First, we manually checked profile pictures
and biographies to identify gender, but many users do not share
their photos in their profiles. Thus, we observed their writing
and the replies of other users to determine their gender from
third person pronouns (ie, “he,” “she,” “him,” and “her”). If we
could not identify the gender from Twitter, we then checked
the other social network accounts of that particular user by
conducting a manual search based on their names and
usernames. For instance, we could identify the genders of
Twitter users based on their pictures on their Instagram or
Facebook profiles. If all the above methods failed to ascertain
the gender, we discarded the user from our list.
We searched for users from those countries that have English
as their first language and were either native or nonnative
speakers. We collected users’ tweets from a total of 6 different
countries: Australia, Canada, Ireland, New Zealand, the USA,
and the UK. Table 1 shows the number of users from both
groups (Insomnia Yes and Insomnia No) collected from different
geographical locations.
Table 2 displays the statistics describing the tweets of our data
set.
Table 1. Twitter users’statistics by location.
Insomnia No users, n (n=754)Insomnia Yes users, n (n=820)Total, n (N=1574)Countries
104108212UK
313162Australia
153181334Canada
446473919USA
141832New Zealand
6915Ireland
Table 2. Tweet statistics.
Insomnia NoInsomnia YesStatistic
1,810,5671,998,683Tweets, n
32503247Maximum number of tweets of a user, n
2626Minimum number of tweets of a user, n
2401.28 (1156.65)2437.42 (1035.42)Average number of tweets of a user, mean (SD)
65,66067,427Maximum word count of a user, n
191195Minimum word count of a user, n
For raw data preprocessing, we discarded the username and
mentions. We kept the retweets of users because the act of
retweeting could indicate the personality traits of users. We
removed hashtags (eg, “#insomnia,”) and converted them into
text (eg, “insomnia”). We also removed URLs and http links
because these text data cannot be analyzed by lexical methods.
These texts also do not produce any sensible numeric vector
for the word embedding method. We did not remove stop words
because some word embedding techniques, like BERT, show
that prepositions facilitate a better understanding of the context.
We removed emojis by using the Python demoji package [38].
For example, we replaced the fire symbol emoji with the word
“fire.” We used the LIWC2015 dictionary for removing
emoticons according to the suggestion of Seabrook et al [39].
Model Building
We built the classification models from 3 different techniques:
(1) a psycholinguistic-based model (ie, LIWC and Empath), (2)
a word embedding–based model (ie, BERT), and (3) a Big 5
personality–based model. First, we describe the performance
of predicting Insomnia Yes and Insomnia No for each of the
independent models. We then describe the process of building
our novel ensemble model from these 3 independent models.
Since we used word-embedding techniques for deep
learning–based models, we did not need a feature selection
method for this classification category. We carefully selected
important features for the other 2 approaches because irrelevant
features can weaken the accuracy of a model [40].
Feature Selection
In our data set, our independent variables consisted of analysis
scores from users’tweets as generated by psycholinguistic tools
(ie, LIWC and Empath), word-embedding methods (ie, BERT),
and the Big 5 personality traits. Our dependent variable was
Insomnia Yes and Insomnia No. As our independent variables
were continuous while the dependent variable was categorical,
we applied Fisher’ linear discriminant analysis [34] for our
feature selection method.
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As mentioned earlier, we used 2 different psycholinguistic
techniques, LIWC and Empath, to analyze users’ tweets.
Initially, we used the LIWC-based approach using LIWC2015,
which classifies approximately 90 different features from texts
into 7 different categories, where each category contains
hundreds of words [41]. These categories include summary
language variables (analytical thinking, clout, authenticity, and
emotional tone), general descriptor categories (words per
sentence, percent of target words captured by the dictionary,
etc), standard linguistic dimensions (articles, auxiliary verbs,
etc), word categories tapping psychological constructs (affect,
cognition, etc), personal concern categories (work, home, leisure
activities, etc), informal language markers (assents, fillers, swear
words, netspeak), and punctuation categories (periods, commas,
etc).
We first considered LIWC scores to be independent variables
and Insomnia Yes and Insomnia No as dependent variables. We
applied Fisher’s linear discriminant analysis by using SPSS
statistical software (IBM Corp) to find the relevant features.
Table 3 shows the correlation coefficient between users’LIWC
scores and Insomnia Yes and Insomnia No according to Fisher’s
linear discriminant analysis. The predictors with larger (>1.0)
scores are better predictors. Therefore, these scores are helpful
in deciding which variables have more effect when building the
classification models [42].
Table 3. Fisher’s correlation coefficient between LIWCacategories and insomnia categories.
Insomnia NoInsomnia YesLIWC category
47.83848.117i
123.253123.587negate
74.52174.714swear
18.46618.731health
–31.479–31.599drives
17.34217.453focuspresent
16.15716.856SemiC
–38.057–38.140cogproc
20.99119.972sad
37.32337.245affiliation
18.52417.692anx
23.87322.654death
83.24683.284social
–16.901–17.013Analytic
aLIWC: Linguistic Inquiry and Word Count.
Second, we used an Empath-based approach to address the
shortcomings of the LIWC-based approach. LIWC can only
analyze a total of 6400 dictionary words. A dynamic deep
learning–based approach was used to analyze these words with
Empath. We analyzed the text with Empath by using the
empath-client Python implementation package [43]. Empath
draws connotations between words and phrases by using deep
learning–based neural embedding across more than 1.8 billion
words of modern fiction. Given a small set of seed words that
characterize a category, Empath uses its neural embedding to
discover new, related terms and then validates the category with
a crowd-powered filter. Empath analyzed text across 200
built-in, prevalidated categories we generated from common
topics in our data set, like neglect, government, and social
media. We analyzed the tweets of each user by using Empath
and considered the outcome of the tweets as our independent
variables; meanwhile, Insomnia Yes or Insomnia No was taken
as the dependent variable. Although LIWC and Empath are
highly correlated (r=0.906), we found no correlation between
Empath and our insomnia classification categories. LIWC has
a total of 93 psycholinguistic categories, whereas Empath has
a total of 200 word categories. When these 93 LIWC word
categories were divided between 200 Empath word categories,
which might not have been evenly distributed across the
categories, Empath showed weak correlation coefficients.
Therefore, we ultimately did not integrate the Empath word
categories with our combined model.
Third, to find correlations of users’ sleeping patterns with the
Big 5 personality traits, we initially computed users’personality
scores. We computed the Big 5 personality traits [20] of the
users from their tweets by using the IBM Watson Personality
Insight API (application programming interface) [44]. Arnoux
et al [8] have shown that the IBM Watson Personality Insight
API performs well in comparison to other techniques. Other
prior studies [45,46] have also used the API and demonstrated
reasonable performance. We found that the majority of the traits
had a high score of 1.00 and low score of 0.01. We also observed
that insomniac users had average openness, conscientiousness,
extraversion, agreeableness, and neuroticism scores of 0.61,
0.29, 0.52, 0.56, and 0.83, respectively.
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Building Classifiers
After feature selection of all the techniques was completed, we
first built a classification model with LIWC. To build the
classification model, we considered 14 different LIWC
categories of words as initial features. Table 3 shows the features
for the LIWC-based approach. The features are i, negate, swear,
health, drives, focuspresent, SemiC, cogproc, sad, affiliation,
anx, death, social, and Analytic. A potential problem can arise
when collinearity is present among the features. To remove
collinearity among independent LIWC features, we computed
the correlation among the features by using the R regression
subset selection package “leaps” [47]. We found that the
following features were collinear: drives, sad, SemiC,
focuspresent, and affiliation. We discarded these collinear
features from our feature list. Finally, we conducted
classification with the relevant features using a 10-fold
cross-validation with 10 iterations. We built our classification
model with several classifiers that included Naive Bayes,
AdaBoost, random forest, support vector machine, and Gaussian
process.
After this, we used a linguistic model that is capable of finding
contextual relationships between words in a sentence. To this
end, we use the BERT [32] model which is pretrained on a large
corpus of sentences. The model learns to produce a powerful
internal representation of words as embeddings. We used the
sentence-transformers [48] library to generate BERT vectors
by using a pretrained model. We grouped 2 sets of preprocessed
data sets, one without applying lemmatization and punctuation
marks and the other after applying all the preprocessing
techniques.
We used the BERT vector as input for our convolutional neural
network (CNN)–based deep learning model [49]. As CNN is a
nonlinear machine learning model and the BERT embedding
vector has a large input feature (768 × 1), we trained our model
with this deep learning–based architecture. The CNN model
contains 2 hidden layers. In the first hidden layer, we added the
leaky ReLU (rectified linear unit) [50] activation function. In
the next hidden layer, we added a dropout for regularization of
the model with a tanh activation function. Finally, we added a
dense layer with a softmax activation function [51] and used
the Adam optimizer with binary cross-entropy loss. We split
the training and test data sets by 70% and 30%, respectively,
and built the classification model using a 10-fold
cross-validation with 10 iterations. With this configuration, the
accuracy was 67% and 58% for the training and test data sets,
respectively.
Next, we built our classification model by using the Big 5
personality traits. Based on the date summarized in Table 4, we
selected 3 relevant Big 5 traits: conscientiousness, neuroticism,
and agreeableness. We built the classification model with the
relevant traits using a 10-fold cross-validation with 10 iterations.
We built our classification model with several classifiers that
included Naive Bayes, AdaBoost, random forest, support vector
machine, and Gaussian process by again splitting the training
and test data sets by 70% and 30%, respectively.
Table 4. Fisher’s correlation coefficient between personality traits and Insomnia Yes and Insomnia No.
Insomnia No (Fisher’s score)Insomnia Yes (Fisher’s score)Personality trait
29.22731.885Conscientiousness
17.33628.168Neuroticism
–20.785–19.137Openness
–1.61–1.93Extraversion
3.1322.175Agreeable
Building Weighted Ensemble Classifiers
We finally combined the previous classification models to
increase the strength of our prediction model. To determine a
unified final insomnia label, we combined all the independent
models, including, LIWC, BERT, and the Big 5 personality
traits. It was necessary to prioritize the approaches based on
their performance. We ordered the approaches by assigning
weights to each model. We used these weights to build our final
ensemble model. To build our ensemble model, we performed
the following 2 steps: computing weights of each approach from
both the training and test data sets and combining the models
with a double-weighted linear ensemble technique.
We then determined the weights of each approach (ie, WLIWC,
WBERT, and WBig5 personality traits) from both the training and
test data sets. We occasionally observed that a model showed
better strength with the training data set while the model
performed weakly for the test data set. Therefore, we paid close
attention to the performance of our models in both the training
and test data sets. This way, we could bring greater diversity to
the weights to build our ensemble model. To this end, we first
ran the classification model over the training data set of 944
Twitter users (944/1574, 60.0% of the total data set). We ran
the classification models by using LIWC, BERT, and the Big
5 personality traits. We used both linear (eg, random forest,
Naive Bayes) and nonlinear (ie, CNN) models during training.
We ranked the strength of these classification models by
checking their root mean square error (RMSE) scores. The lower
the RMSE score, the higher the weight of a model (linear or
nonlinear). To compute the weight, we subtracted the RMSE
score from 1 (W=1–RMSE).
We again ran the classification models over the test data set of
630 Twitter users (630/1574, 40.0% of the total data set). We
also applied both linear and nonlinear techniques by using
LIWC, BERT, and the Big 5 personality trait approaches. We
then ranked the weights (1–RMSE for the test data set) of these
approaches. Figure 1 shows the detailed process of producing
weights from both the training (60%) and test (40%) data sets.
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In the figure, we indicate weight as W, which indicates that we
subtracted RMSE from 1 for the model. In our double-weighted
method, we combined the weights of each type by using convex
combination techniques [52] for both the training and test data
sets. For the training data set, we obtained weights of 0.52, 0.47,
and 0.50 from LIWC, BERT, and the Big 5 traits, respectively.
In contrast, for the test data, we achieved weights of 0.38, 0.50,
and 0.36 from LIWC, BERT, and the Big 5 traits, respectively.
These weights were generated from linear and nonlinear
classification models of LIWC, BERT, and the Big 5 personality
traits.
Figure 1. Weight computation and building ensemble model for predicting insomnia. BERT: bidirectional encoder representations from transformers;
LIWC: Linguistic Inquiry and Word Count.
Finally, we combined the weights that we found from the
double-weighted method by using the convex combination
method. Equation 1 presents the final insomnia ensemble
classification result (IFinal) from the previous 3 different models.
where YLIWC, YBERT, and YBig5 refer to insomnia prediction results
through use of LIWC, BERT, and the Big 5 personality traits,
respectively; and WLIWC, WBERT,and WBig5 denote the weights
generated from LIWC, BERT, and the Big 5 personality traits,
respectively, from both the training and test data sets through
use of the convex combination technique.
Results
Overview
Our study is the first study to build a novel ensemble learning
model to predict insomnia by analyzing a large number of
tweets. Prior studies [28,29] investigated the pattern of
insomniac behavior by using only a limited number of tweets.
Furthermore, the authors explicitly considered phrases such as
“insomnia” and “sleepless” in their data sets. In contrast, in our
study, we discarded these explicit phrases when predicting users’
sleeping issues, which makes our study different and more
robust than the previous studies. In this section, we report the
performance of our independent and final ensemble-based
classifiers, discuss the incorporation of the emotional variability
among the insomniac and noninsomniac users from their tweets,
and present the distribution of insomniac users and the
variability of their Big 5 personality scores. Finally, the
correlation between users’genders and their insomniac behavior
is discussed.
Performance of Independent and Ensemble Classifiers
First, we investigated the performance of our independent and
ensemble-based classifiers. Table 5 shows the performance of
the classifiers. We found that the Gaussian process classifier
had the best average performance (area under the curve [AUC]
75.3%) in predicting users’ insomnia.
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Table 5. Strength of the classification model for predicting insomnia by LIWC categories and the Big 5 personality traits.
Big 5 personality traits
LIWCa
Classifier, insomnia class
AUCFPRTPR
AUCd
FPRc
TPRb
Random forest
0.6490.4750.6860.7470.3340.716Yes
0.6490.3140.5250.7470.2700.678No
Naive Bayes
0.5850.5910.7460.6940.4720.740Yes
0.5850.2540.4090.6940.2600.536No
SVM e
0.6040.6740.8830.6800.2660.632Yes
0.6040.1170.3260.6800.3710.732No
AdaBoost
0.5990.6790.8360.6940.4140.699Yes
0.5990.1640.3210.6940.3140.579No
Gaussian process
0.6660.5420.7650.7540.3830.747Yes
0.6660.2340.4570.7540.3760.713No
aLIWC: Linguistic Inquiry and Word Count.
bTPR: true-positive rate.
cFPR: false-positive rate.
dAUC: area under the curve.
eSVM: support vector machine.
Our final ensemble classification model for insomnia, IFinal,
achieved an AUC of 78.8% and 76.91% from the training and
test data sets, respectively, outperforming the previous
independent models. When observing the performance of the
ensemble-based classifiers on the training and test data sets, we
found that the performances in the test set were similar to those
of the training data set. The receiver operating characteristic
curves for the training and test data sets on our ensemble-based
classifiers are displayed in Figure 2.
Figure 2. Receiver operating characteristic curves for insomnia classification for the (A) training set and (B) test set.
Emotional Variability Among Insomniac and
Noninsomniac Users
Our study investigated whether emotional variability exists
between insomniac and noninsomniac users based on their
tweets. From our observation, we found that users’insomniac
behavior and their psycholinguistic categories were correlated.
We randomly selected 20 insomniac and 20 noninsomniac users.
We extracted users’ anxiety-related words, (eg, “worried,”
“fearful,” “nervous,” “tense”) and converted their scores into a
range from 0 to 1 by using the max-min normalization technique.
Figure 3 presents the variability in the use of these words
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between the insomniac and noninsomniac users. We also
observed that on average, insomniac users exploited
anxiety-related words 10% more than did the noninsomniac
users. Carrera et al [53] showed in their study of 200 college
students that a significant association can be found between
difficulty sleeping and fear of death. In our study, we also found
that insomniac users are likely to post death-related words in
their tweets. We observed that insomniac users write more
“social words.” These users tend to spend more time socializing
with others when they face difficulty sleeping. Our study also
showed that insomniac users tend to display a lack of analytical
thinking.
Figure 3. Usage of words, (A) “Anxious,” (B) “Death,” (C) “Drives,” (D) “Analytic,” (E) “Sad,” and (F) “Social,” related to the LIWC category and
their mean scores between insomniac and noninsomniac users. LIWC: Linguistic Inquiry and Word Count.
Visualization of the Correlation Between Big 5
Personality Traits and Insomniac Users
Users’Big 5 personality traits and insomniac behavior showed
correlations. Table 6 shows the distributions indicating that
agreeableness might have a weak correlation with insomniac
behavior. However, conscientiousness had a strong correlation
with insomniac behavior (Fisher’s score 31.88). The users who
had high insomnia were more likely to be more conscientious.
In contrast, Wissar et al [54] reported that lower
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conscientiousness is associated with greater insomnia severity.
We also find that users who had high neuroticism scores were
more likely to have severe insomnia. Table 6 shows that
insomniac users have moderate scores in agreeableness, are
likely to be high in conscientiousness, and are highly neurotic.
Table 6. The distribution of correlated Big 5 personality trait scores among insomniac users.
Percentage, n (%) (N=820)Traits by range
Agreeableness
77 (9.4)0.0–0.2
164 (20.0)0.2–0.4
188 (22.9)0.4–0.6
214 (26.1)0.6–0.8
177 (21.6)0.8–1.0
Conscientiousness
111 (13.5)0.0–0.2
213 (26.0)0.2–0.4
376 (45.9)0.4–0.6
88 (10.7)0.6–0.8
32 (3.9)0.8–1.0
Neuroticism
9 (1.1)0.0–0.2
38 (4.6)0.2–0.4
79 (9.6)0.4–0.6
125 (15.2)0.6–0.8
569 (69.4)0.8–1.0
Insomnia and Gender Correlation
Seabrook et al [39] showed that gender might have an
association with mental health problems, such as depression.
Reasoning that insomnia is a mental health problem [55] and
motivated by previous studies [39,56], we also investigated the
association between users’ gender and their insomnia-related
problems. We discovered that the number of insomniac and
noninsomniac male users was 309 and 363, respectively. We
further observed that the number of insomniac and noninsomniac
female users was 511 and 391, respectively. From our data set,
we conducted chi-square (P<.001) tests based on gender and
insomnia where the degrees of freedom ([number of rows–1]
× [number of columns–1]) in the contingency table were 1. Our
result showed that gender and insomnia were correlated.
After careful observation, we found that among 511 of all female
users 6.8% (n=35 users) were suffering from pregnancy- or
postpartum-induced insomnia. These users’ tweets contain
words related to “pregnant,” “postpartum,” and “cosleep.” Later,
we applied LDA [57] over the tweets from these users’
pregnancy or postpartum times. LDA is a topic modeling
technique that automatically organizes a large corpus to discover
hidden themes. From users’ patterns of tweeting, we could
estimate the possible time frame for their pregnancies. Table 7
presents the extracted 5 major topics from their tweets: sleep,
night, tired, child, and weird.
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Table 7. Distribution of major topics, including (A) sleep, (B) child, (C) night, (D) tired, and (E) weird, extracted from a group of insomniac users’
tweets during their pregnancy or postpartum periods.
Distribution (%)Topic
Topic A
0.085Sleep
0.077Problem
0.075Suffer
0.045Little
0.060Anxiety
0.057Dream
0.037Escape
0.036Spiritual
0.035Random
0.030Morning
Topic B
0.081Birth
0.071Breast
0.070Nursing
0.076Happiness
0.062Lucky
0.079Potty
0.050Scare
0.048Check
0.052Patience
0.058Angel
Topic C
0.072Tonight
0.070Night
0.061Dinner
0.060Noise
0.072Think
0.048Party
0.051Event
0.042Pizza
0.050Worry
0.049Carry
Topic D
0.076Tired
0.068Suffer
0.061Watch
0.060Stressful
0.059Minute
0.070Break
0.043Hyperemesis
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Distribution (%)Topic
0.042Pregnancy
0.040Battle
0.039Sport
Topic E
0.074Weird
0.062Stare
0.060Disgust
0.056Crazy
0.072Bitch
0.050Shame
0.070People
0.049Employee
0.069Dislike
0.068Pregnant
Discussion
Emotional Linguistic and Insomniac Behavior
As discussed earlier, users’ linguistic patterns and Insomnia
Yes or Insomnia No indications were strongly correlated.
Insomniacs tended to use negative categories of LIWC words
(eg, “no,” “not,” “never”). Some people with strong
temperament frequently use swear words (eg, “damn,” “piss”).
In Bonnet et al’s [55] study, insomniacs showed hyperarousal
(an abnormal state of responsiveness) due to increased secretion
of corticosteroids and adrenaline and an elevated metabolic rate.
In another study, Bonnet et al [58] showed that insomniacs
experience mood alternation and chronic psychological
activation. The following is a sample tweet from an Insomnia
Yes user that endorses our finding: “It sucks when you realize
that the people closest to you are the most toxic.” They also use
anxious (eg, worried, fearful, nervous) and sad (eg, crying, grief,
sad) categories of LIWC words in their tweets. Freeman et al
[59] showed that insomnia has a connection with anxiety,
depression, and being worried, and these people are likely to
use death-related words (eg, “bury,” “coffin,” “kill”). Harrison
et al [60] reported that insomniacs generally exhibit fear of death
and show anxiety about the uncertain (eg, “maybe,” “perhaps,”
“guess”). The following tweet is an example this: “I feel dead
and I hate everybody.” Our study also adds to these findings by
analyzing the correlation between LIWC categories of words
found in the users’ tweets and their sleeping patterns. Hiller et
al [61] showed that cognitive processes (eg, the psychobiological
inhibition model) play an important role in understanding and
treating insomnia. We obtained a few correlations, such as
focuspresent and SemiC, which cannot be intuitively explained.
We did not find a correlation between Empath categories of
words and users’ sleeping patterns. It is interesting to observe
that none of these categories showed any significant correlation
between Empath word categories and users’ sleeping habits.
Fast et al [62] reported that LIWC and Empath word categories
are correlated (r=0.906). LIWC has a total of 93 word categories
that are distributed across a total of 200 word categories in
Empath. When a word category subsumes a greater number of
individual words, then the chance of 2 features being correlated,
such as LIWC and insomnia class labels, increases. In contrast,
when the number of words decreases in a category, such as in
Empath, then the possibility of being correlated decreases. In
our study, we observed that although LIWC and Empath were
correlated, this correlation might not have been sustained in a
transitive case. For example, although Empath → LIWC and
LIWC → Featurex are true, Empath → Featurexdoes not exist
in our data set.
The Big 5 Personality Traits and Insomniac Behavior
We discovered that users’ personalities and their sleeping
patterns have strong correlations and that users with high
neuroticism are more likely to have insomnia. They tend to go
through depression, social introversion, repression, and
intolerance in terms of somatic health problems. The following
is a sample tweet supporting this finding: “Ralph Northam needs
to fucking RESIGN already. I have zero tolerance for him.” A
prior study [63] also reported similar observations related to
the correlation between neuroticism and insomnia. Assessing
neuroticism may allow for the early detection of catastrophic
situations of insomnia. We also observed that users with high
conscientiousness scores had strong correlations with insomniac
patterns. The following tweet from our data set exemplifies this
finding: “I cannot control others actions but I can control mine
and my reactions.” Larsgaard et al [64] noticed that
conscientiousness in people might be correlated with reduced
sleeping behavior due to their self-inhibition and the
meticulousness in their daily activities.
In our study, we also found a correlation, albeit a weak one,
between users’agreeableness and insomnia. The following tweet
from an Insomnia Yes user is an example of this: “understand
people who talk crap about others thru social media. Listen
Linda, why aren’t you handling it like an adult?” Dekker et al
[65] found that neuroticism, agreeableness, and openness are
directly associated with the insomnia severity index. In our
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study, we discovered that openness is negatively correlated with
users’ sleeping patterns. Tsaousis et al [66] also demonstrated
that openness and insomnia are negatively correlated with each
other. However, several studies [67-70] have demonstrated that
openness is unrelated to sleep quality.
In our study, there was negative correlation between extraversion
and insomnia. Gray et al [69] found there to be no correlation
between users’ extraversion and insomniac patterns. Our
observations of the relationship between the Big 5 traits and
insomnia largely overlap with prior findings [63-65,67-70],
which suggest that investigating users’ insomnia patterns
through social media can leverage our manual effort without
asking an individual directly about their sleeping issue.
Furthermore, understanding and inspecting one’s personality
traits may provide clues to underlying causes of vulnerability
to developing insomnia.
Pregnancy and Insomniac Behavior
According to our finding, users share their depression during
their pregnancies and share their experiences during their
postpartum periods. We observed that the major supporting
topics were night, suffer, problem, and birth, among others. We
also found a few topics that described parents’
postpartum-related anxiety. Important topics that we found from
users’tweets were birth, breast, tired, suffer, and random, among
others. Previous studies also support our findings [71,72].
Conclusions
In this study, we investigated the associations between the
psycholinguistic and personality traits of users’with insomnia.
We captured both the word usage and semantics of users’tweets
through the LIWC and BERT linguistic models and developed
2 models to predict insomnia from LIWC features and BERT
word embedding. We then built our third machine learning
model that used derived personality traits to predict insomnia
from tweets. Finally, we built a rigorous ensemble model by
combining the 3 separate models. Our ensemble classifier
showed strong prediction potential (AUC 78.8%). The classifier
was built by using a novel, double-weighted ensemble technique
that outperformed the independent classifiers. We plan to
improve our classifier by integrating more data from social
networks, such as friend lists, time of tweets, gender, workplace,
time spent on activities, etc. We also plan to analyze tweets in
different languages.
Conflicts of Interest
None declared.
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Abbreviations
API: application programming interface
AUC: area under the curve
BERT: bidirectional encoder representations from transformers
CNN: convolutional neural network
LDA: latent Dirichlet allocation
LIWC: Linguistic Inquiry and Word Count
ReLU: rectified linear unit
RMSE: root mean square error
Edited by R Kukafka, G Eysenbach; submitted 30.01.21; peer-reviewed by M Antoniou, PH Makkonen; comments to author 17.03.21;
revised version received 12.05.21; accepted 05.10.21; published 09.12.21
Please cite as:
Sakib AS, Mukta MSH, Huda FR, Islam AKMN, Islam T, Ali ME
Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets
J Med Internet Res 2021;23(12):e27613
URL: https://www.jmir.org/2021/12/e27613
doi: 10.2196/27613
PMID:
©Ahmed Shahriar Sakib, Md Saddam Hossain Mukta, Fariha Rowshan Huda, A K M Najmul Islam, Tohedul Islam, Mohammed
Eunus Ali. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.12.2021. This is an
open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic
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be included.
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