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FRONTIERS IN PSYCHIATRY


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Articles

EDITED BY

CHARLOTTE R. BLEASE



Beth Israel Deaconess Medical Center, Harvard Medical School, United States

REVIEWED BY

MARY V. SEEMAN



University of Toronto, Canada

ANNA Y. KHARKO



Uppsala University, Sweden

LIZ SALMI



Beth Israel Deaconess Medical Center, United States

The editor and reviewers' affiliations are the latest provided on their Loop
research profiles and may not reflect their situation at the time of review.

TABLE OF CONTENTS

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   * Introduction
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   * Results
   * Discussion
   * Limitations
   * Conclusions
   * Data Availability Statement
   * Ethics Statement
   * Author Contributions
   * Funding
   * Conflict of Interest
   * Publisher's Note
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ORIGINAL RESEARCH ARTICLE

Front. Psychiatry, 18 November 2021 | https://doi.org/10.3389/fpsyt.2021.737684


ASSESSMENT OF ANTIPSYCHOTIC MEDICATIONS ON SOCIAL MEDIA: MACHINE LEARNING STUDY

Miguel A. Alvarez-Mon1,2,3, Carolina Donat-Vargas4,5, Javier Santoma-Vilaclara6,
Laura de Anta3, Javier Goena7,8, Rodrigo Sanchez-Bayona9, Fernando Mora3,10,
Miguel A. Ortega1,2*, Guillermo Lahera1,2,11,12, Roberto
Rodriguez-Jimenez12,13,14, Javier Quintero3,10 and Melchor Álvarez-Mon1,2,15
 * 1Department of Medicine and Medical Specialities, University of Alcala,
   Alcala de Henares, Spain
 * 2Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
 * 3Department of Psychiatry and Mental Health, Hospital Universitario Infanta
   Leonor, Madrid, Spain
 * 4Cardiovascular and Nutritional Epidemiology, Institute of Environmental
   Medicine, Karolinska Institute, Stockholm, Sweden
 * 5IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain
 * 6IBM Data and AI Expert Labs and Learning, London, United Kingdom
 * 7Department of Psychiatry and Clinical Psychology, University of Navarra
   Clinic, Pamplona, Spain
 * 8Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
 * 9Hospital Universitario 12 de Octubre, Unidad de Cáncer de Mama y
   Ginecológico, Madrid, Spain
 * 10Department of Legal and Psychiatry, Complutense University, Madrid, Spain
 * 11Department of Psychiatry, University Hospital Principe de Asturias, Alcalá
   de Henares, Spain
 * 12CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid,
   Spain
 * 13Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas 12),
   Madrid, Spain
 * 14Universidad Complutense de Madrid (UCM), Madrid, Spain
 * 15Service of Internal Medicine and Immune System Diseases-Rheumatology,
   University Hospital Príncipe de Asturias (CIBEREHD), Alcalá de Henares, Spain

Background: Antipsychotic medications are the first-line treatment for
schizophrenia. However, non-adherence is frequent despite its negative impact on
the course of the illness. In response, we aimed to investigate social media
posts about antipsychotics to better understand the online environment in this
regard.

Methods: We collected tweets containing mentions of antipsychotic medications
posted between January 1st 2019 and October 31st 2020. The content of each tweet
and the characteristics of the users were analyzed as well as the number of
retweets and likes generated.

Results: Twitter users, especially those identified as patients, showed an
interest in antipsychotic medications, mainly focusing on the topics of sexual
dysfunction and sedation. Interestingly, paliperidone, despite being among one
of the newest antipsychotics, accounted for a low number of tweets and did not
generate much interest. Conversely, retweet and like ratios were higher in those
tweets asking for or offering help, in those posted by institutions and in those
mentioning cognitive complaints. Moreover, health professionals did not have a
strong presence in tweet postings, nor did medical institutions. Finally,
trivialization was frequently observed.

Conclusion: This analysis of tweets about antipsychotic medications provides
insights into experiences and opinions related to this treatment. Twitter user
perspectives therefore constitute a valuable input that may help to improve
clinicians' knowledge of antipsychotic medications and their communication with
patients regarding this treatment.




INTRODUCTION

Schizophrenia is a psychiatric disorder characterized by a multidimensional
psychopathology that includes positive, negative and mood symptoms, as well as
cognitive impairment. Additionally, it is commonly associated with impairments
in social and occupational functioning (1). Schizophrenia and psychotic
disorders are among the world's leading causes of disability (2). However,
evidence-based psychosocial interventions, in conjunction with pharmacotherapy,
can help patients achieve recovery (3, 4).

Antipsychotic medications are the first-line treatment for psychosis-related
disorders and have been shown to be effective in treating associated symptoms
and behaviors (5). First- and second-generation antipsychotic medications are
comparable in clinical efficacy, with the exception of clozapine, which
demonstrates the best results toward treatment-resistant schizophrenia (6, 7).
However, they differ from one another in other characteristics such as dosing,
the route of administration, the side effects and the cost (8). All these
factors influence the selection of the antipsychotic drug to be used, as well as
adherence to a medication regimen, ultimately affecting a medication's
effectiveness. For example, weight gain, diabetes, and dyslipidemia are more
often associated with second-generation antipsychotics (SGAs), while
first-generation antipsychotics (FGAs) generally have a greater risk of
extrapyramidal side effects (8, 9).

In patients with a poor response to medication or repeated relapses,
non-adherence should be considered (10, 11). Side effects, along with a limited
awareness of one's schizophrenia and the need for treatment, are frequently the
primary cause of non-adherence, and thus need to be addressed (3). However, many
patients hide these issues from their doctor, with the reasons for doing so
poorly understood yet nevertheless significant, such as the feeling of shame in
disclosing these aspects or the fear of being judged (11).

Traditional research on patients' experiences to treatment has principally
relied on surveys or interviews (12–14). However, the analysis of social media
posts has emerged as an important tool capable of gathering a more comprehensive
understanding of those factors involved in the therapeutic process and in the
experience of the disease (15, 16). It also allows for greater insights into the
opinions of others apart from just the patients themselves, thereby providing
additional data from a wider range of perspectives, including those patients
that are reluctant to visit a medical professional (15–17). In addition, social
media conversations are generated within a more casual and spontaneous
environment than those that take place during a medical appointment; thus, they
may be more likely to reflect true beliefs (18–20).

Furthermore, numerous studies have also demonstrated that individuals living
with schizophrenia use popular social media platforms at comparable rates to the
general population (21). In addition, individuals with mental illness appear to
use social media to share their illness experiences or seek advice from others
with similar conditions (21). However, less is known about whether people with
psychosis talk about antipsychotic medication experiences over social media.
Thus, our aims were to (1) investigate the frequency of online communications
about antipsychotic medications among Twitter users; (2) characterize the type
of users participating in these conversations; (3) determine the main thematic
content of Twitter posts and the interest they generated; and (4) analyze
references to specific antipsychotic medications.


METHODS


SEARCH STRATEGY AND COLLECTION OF TWITTER DATA

In this observational quantitative and qualitative study, we focused on
searching for tweets that referred to antipsychotics. We collected all posted
tweets using the following list of keywords: amisulpirida, solian, haloperidol,
haldol, pimozida, orap, sulpirida, dolmatil, flufenacina, modecate, sulpirirda,
dogmatil, clorpromacian, largactil, levomepromazine, sinogan, aripiprazol,
abilify, clozapine, leponex, nemea, clozaril, olanzapina, zyprexa, paliperidone,
invega, quetipiana, seroquel, risperidone, risperdal, brexpiprazole, rexulti,
loxapina, adasuve, lurasidone, latuda, ziprasidone, geodon, and zeldox. We
selected keywords according to the most frequently prescribed antipsychotics in
Europe and the United States. We have included both generic names and brand
names in the selection. We grouped the keywords into 13 categories, with each
category corresponding to a different antipsychotic medication, thus allowing
for greater ease when comparing one medication with another. Antipsychotic
medications were referred to by either their generic name or by their brand
name.

The inclusion criteria for tweets were: (1) Being public; (2) Containing any of
the previously mentioned keywords; (3) Being posted between January 1st 2019 and
October 31st 2020 and; (4) Containing text in English or Spanish. A 22 month
period was chosen to avoid any potential bias in the content of the tweets. For
instance, we wanted to be sure to prevent content from being affected by the
season of the year, a particular event, or a special circumstance (publication
of any relevant scientific article related to any of the antipsychotic
medications being analyzed, the mention of or a relationship to any medical,
psychiatric or pharmacological conferences, etc.). In addition, we obtained the
number of retweets and likes each tweet generated as an indicator of user
interest on a given topic, the date and time of each tweet, a permanent link to
the tweet and each user's profile description. Tweet Binder, the search engine
we employed, allows access to 100% of all public tweets that match certain
criteria.


CONTENT ANALYSIS PROCESS

All 30,603 retrieved tweets were included in the dataset. First, we randomly
selected 1,500 of the tweets to be considered for content analysis. Secondly, we
created a codebook based on our research questions, our previous experience in
analyzing tweets, and what we determined to be the most common tweet themes
(22–25). Third, JG and LA analyzed 300 tweets separately to test the suitability
of the codebook. Discrepancies were discussed between the raters and with
another two authors (MAAM and MAM), and after revising the codebook the raters
then proceeded to manually code the remaining 1,200 tweets.

Tweets were categorized as classifiable or unclassifiable. We considered a tweet
as non-classifiable when its content did not provide enough information or if it
was written in a language other than English or Spanish. Each of the tweets
considered classifiable was rated according to the area of clinical interest
mentioned or discussed in the text of the tweet, such as quality of life, mood
and anxiety, sedation, metabolic disturbances and extrapyramidal symptoms,
sexual dysfunction, and cognitive complaints. In addition, tweets with
non-medical content were rated as commercial, economic, ask/offer help,
trivialization, or non-specific. We classified as trivialization those tweets
that included mockery or joking, associated treatment with undesirable
attributes or associated treatment with grossly inaccurate stereotypes.
Moreover, the following characteristics were also analyzed: if the tweet
included a link to a health care provider (either a hospital, health
institution, university, or pharmaceutical company), if it mentioned a
scientific article or specific aspects of posology, or if it expressed a
personal opinion, mentioned a famous person or stated the use of an
antipsychotic to treat a particular disorder. Finally, users were classified
into 5 categories: patient, relative or friend of a patient, health
professional, health institution, and Twitter-user interaction (when at least
two different users participated in a conversation but were classified into
different categories). We determined the nature of the users that posted tweets
according to the information available: content of the tweet (use of pronouns,
disclosure of personal or family experiences, professional information provided,
etc.), user profile description or Twitter handle (this section was especially
informative in the case of health institutions since the majority refer to their
social media accounts in their profile description or their Twitter handle). In
those cases in which the nature of the user was not possible to know, they were
considered as indeterminate.

The coding categories used were not mutually exclusive. In the case of finding
content that was repeated exactly or posted almost identically in different
tweets, those postings were classified in the same way as the first tweet
encountered.


MULTILINGUAL MACHINE LEARNING CLASSIFIER

The goal of the initial tagging of 1,500 tweets was to provide data to train,
test, and validate Machine Learning classifiers so that any extracted tweet
classifications could be inferred. To train the classifiers, the transformer
multilingual model xlm-roberta neural net was used in conjunction with a
“k-train” library to deploy it (26, 27). The following additional features were
generated to improve understanding of the selected set: the number of tokens
that the sentence contained, the total length of the tweet in terms of
characters, the language of the tweet and the extracted hashtags from the tweet.
Additionally, on order to improve the Machine Learning classifier performance,
we generated a clean text that took any mentions (@) and hyperlinks out of the
tweet so that it became more readable.

Out of the 1,500 manually labeled tweets, we reserved 10% to use as a blind set
for model validation so that the setup we employed for training the classifier
constituted 80% training and 20% validation. Initially, training was done on the
classifiable feature and then out of those tweets that were labeled
classifiable, the remaining classifications were trained. The weighted average
F1 score of the training validation against the blind dataset was above 0.80 in
all cases except for the User and Interest categorization, which was slightly
lower.

These analyses were performed using Python 3.7 and the libraries “pandas,”
“numpy,” “json,” and “k-train.”


ETHICAL CONSIDERATIONS

This study received the approval of the University of Alcala Research Ethics
Committee and was compliant with the research ethics principles of the
Declaration of Helsinki (7th revision, 2013). However, this study did not
directly involve human subjects nor include any intervention but instead used
only publicly available tweets. Nevertheless, we have taken care not to reveal
any usernames and to avoid citing any tweets that could reveal them.


STATISTICAL ANALYSIS

The frequency distribution (percentage) of tweets, retweets, and likes according
to certain characteristics of each tweet, such as the content included or the
antipsychotic drug mentioned, were displayed across several figures and tables.
Because the sample of tweets corresponds to all tweets from the selected period,
and are not just a representative sample of these tweets, the causal inference
p-values do not apply. The accuracy of the different tweet distributions
obtained from the Multilingual Machine Learning models is reflected by the
weighted average F1 score (a combination of precision and recall; the closer the
score is to 1, the less possibility of classification error). As well,
retweet-to-tweet and like-to-tweets ratios according to user type, the area of
clinical interest and non-medical aspects were calculated.

According to the content of the tweet, we used simple logistic regressions to
calculate the probability of retweeting or liking a tweet. These results were
presented as an odds ratio (OR) with 95% Confidence Intervals (CI). These
analyses were conducted with the software packages STATA v16 (StataCorp) and MS
Excel.


RESULTS


SEDATION AND SEXUAL DYSFUNCTION ARE THE MOST COMMON AREAS OF CLINICAL INTEREST
IN ANTIPSYCHOTIC RELATED TWEETS

For this work, we collected 30,603 tweets including the keywords antipsychotic
medications from January 1st 2019 to October 31st 2020. According to the
inclusion criteria of the codebook, a total of 22,092 tweets were considered
classifiable.

First, we analyzed the content of those tweets referencing the specific areas of
clinical interest studied (Figure 1A), with 5,415 out of the 22,092 tweets
analyzed featuring related content. There was a significant difference in
distribution (P < 0.05) of the number of tweets between the different categories
studied, the predominant majority being related to sexual dysfunction and
sedation. Additionally, there was a lower frequency of tweets related to
cognitive complaints and mood or anxiety. Those related to metabolic
disturbances or extrapyramidal symptoms represented the minority of tweets.


FIGURE 1

Figure 1. Different percentages (%) of tweets according to areas of clinical
interest (A) and non-medical aspects (B).




We also analyzed the content of those tweets including no medical aspects of
antipsychotic medications (8,517 tweets) and we classified them according to
four categories: commercial activities, economic issues, ask for or offer help
and trivialization) (Figure 1B). We found a significant distinction in the
number of tweets between these four categories with the highest frequency of
postings related to drug trivialization followed by those asking for or offering
help (P < 0.05). Those related to commercial activities and economic issues
represented the minority of tweets.

Further investigating certain tweet characteristics, we found that 18.87% of the
22,092 tweets studied included a link to a health care provider (either a
hospital, health institution, university, or pharmaceutical company), 10.55%
referenced a scientific article and 10.44% were related to specific aspects of
posology. Moreover, 14.1% of the tweets included a personal opinion while 6.4%
mentioned a famous person. Lastly, 17.7% mentioned the use of antipsychotics for
treating a particular psychiatric disorder.


PATIENTS ARE THE MOST ACTIVE TWITTER USERS WITH A DIFFERENTIAL PATTERN OF AREAS
OF INTEREST IN ANTIPSYCHOTIC RELATED CONVERSATIONS

From investigating the types of users that posted tweets, 7,188 tweets were
posted by users identified as patients (72.1%), health institutions (17.2%),
patients' friends or relatives (7.0%), and health care professionals (3.7%).
Seven thousand six hundred and fifty-five tweets were interactions between two
or more Twitter users. From the remaining 4,465 tweets, the users were
considered unclassifiable.

Subsequently, we investigated areas of clinical interest in those tweets posted
by different types of users, finding significant differences between them (P <
0.05) (Figure 2). Patients, for example, focused the content of their tweets on
the specific clinical aspects of antipsychotic medications, specifically sexual
dysfunction and sedation. In contrast, almost all of the tweets posted by health
institutions concerned non-specific issues. However, the distribution of
postings related to areas of clinical interest stemming from patients' friends
or relatives, health professionals and Twitter interactions was similar, with a
predominance centered on cognitive complaints.


FIGURE 2

Figure 2. Areas of clinical interest related to antipsychotic medications by the
type of user posting. Percentages (%) were calculated with respect to the total
number of tweets posted by each type of user.





TWEETS ASKING FOR OR OFFERING HELP, REFERRING TO COGNITIVE IMPAIRMENT OR BEING
POSTED BY HEALTH INSTITUTIONS GENERATED THE GREATEST INTEREST

We investigated the interest generated by those tweets related to antipsychotic
medications by quantifying the number of retweets and likes generated by each
tweet. In doing so, we found that the probabilities of a tweet being retweeted
or liked were distinct between the different categories of users. Tweets posted
by health institutions accumulated the highest median of retweets per tweet,
which was three times higher than that observed in tweets posted by health care
professionals or patients' relatives and friends (Table 1). Not insignificantly,
those tweets posted by patients obtained the lowest median of retweets per
tweet. By contrast, when we analyzed the probability of tweets receiving likes,
we found a noticeable similarity among the different types of users.
Nevertheless, tweets posted by health professionals obtained twice as many likes
as those posted by patients, health institutions and patients' relatives (Table
1).


TABLE 1

Table 1. Retweet-to-tweet and like-to-tweet ratios per type of user.




Furthermore, we investigated the number of likes and retweets generated by each
area of clinical interest (Table 2). Tweets related to cognitive complaints were
clearly those with the highest ratios of being retweeted and liked, followed by
those referencing mood and anxiety and sedation. Finally, those tweets concerned
with quality of life, metabolic disturbances and extrapyramidal symptoms, and
sexual dysfunction had a similar average number of retweets and likes per tweet.
Additionally, we investigated the probability of a tweet of being retweeted or
liked depending on non-medical aspects. We found that tweets asking for or
offering help were clearly retweeted more than those either referring to
commercial or economic issues and trivialization toward a certain drug. That
being said, tweets related to drug trivialization still received the most likes
(Table 3).


TABLE 2

Table 2. Retweet-to-tweet and like-to-tweet ratios per area of clinical
interest.



TABLE 3

Table 3. Retweet-to-tweet and like-to-tweet ratios per non-medical aspects.




Finally, we assessed which characteristics were more associated with engagement
(Figure 3). We found that those tweets including a link to a health care
provider had almost three times greater odds of being retweeted than those
tweets that did not include such a link. As well, tweets that referred to a
psychiatric diagnosis had a greater probability of being retweeted than those
tweets that did not. On the other hand, tweets that mentioned a famous person or
specific aspects of posology, or that expressed a personal opinion, had lower
probabilities of being retweeted. By contrast, the probabilities of a tweet
being liked maintained a different pattern even though most of the associated
characteristics being analyzed had a modest effect on the probability of a tweet
being retweeted or liked.


FIGURE 3

Figure 3. ORs and 95% CIs for retweeting (A) and liking (B) a tweet according to
specific characteristics.





THE FREQUENCY OF TWEETS RELATED TO ANTIPSYCHOTIC MEDICATIONS WAS HETEROGENEOUS

Of the 22,092 tweets included in our research, we investigated those
specifically related to each of the 13 antipsychotic medications studied. The
number of mentions of each drug followed a heterogeneous pattern of
distribution, ranging from 14.3% for olanzapine to 1.2% for sulpiride (Figure
4). Of note, aripiprazole and paliperidone, which are among the newest
antipsychotic medications, only accounted for 15.0% of the total number of
tweets.


FIGURE 4

Figure 4. Distribution of tweets by antipsychotic medications.




We also analyzed the type of user who posted tweets related to antipsychotic
medications. For example, the percentage of tweets posted by users identified as
patients was greater in lurasidone (57.0%), aripiprazole (56.6%), quetiapine
(51.7%), brexpiprazole (43.6%), and paliperidone (41.0%) (Figure 5). The
percentage of tweets posted by users identified as friends and relatives of
patients or by health professionals was low overall, with the exception of
clozapine (10.0 and 6.6%, respectively). Tweets posted by health institutions
held higher percentage totals when related to sulpiride (59.6%) and
levomepromazine (54.5%) but constituted a total below 10% when referencing all
other drugs. Lastly, Twitter user interaction represented the category with the
highest percentage of tweets related to haloperidol (58.0%), chlorpromazine
(52.2%), risperidone (43.6%), or olanzapine (41.8%).


FIGURE 5

Figure 5. Tweets related to each antipsychotic medication by the type of user
posting. Percentages (%) were calculated with respect to the total number of
tweets generated on each antipsychotic medication.





MOST RECENT MEDICATIONS DID NOT ATTRACT MUCH ATTENTION AMONG TWITTER USERS

We measured the number of retweets and likes generated by those tweets related
to each of the antipsychotic medications studied. We found that the
retweet-to-tweet and like-to-tweet ratios between the different drugs were
distinct (Figure 6). For instance, levomepromazine was the drug with the highest
retweet-to-tweet ratio (13.3), whereas haloperidol and aripiprazole were the
drugs with the highest like-to-tweet ratios (9.8 and 9.4, respectively).
Moreover, we found that brexpiprazole, lurasidone, paliperidone, quetiapine, and
ziprasidone generated a retweet-to-tweet ratio below 1.


FIGURE 6

Figure 6. Retweet-to-tweet and like-to-tweets ratios per each antipsychotic
medication.




We also analyzed the distribution of tweets referencing an antipsychotic
medication according to areas of clinical interest (Figure 7). Almost 60% of the
tweets referring to quality of life mentioned aripiprazole or lurasidone,
whereas none of the tweets mentioning sulpiride, paliperidone, levomepromazine,
clozapine, chlorpromazine, or brexpiprazole addressed issues related to quality
of life. In regards to mood and anxiety, we found that this type of content was
minimal in tweets mentioning chlorpromazine, clozapine, haloperidol,
levomepromazine, paliperidone, or sulpiride. A similar pattern was observed in
tweets discussing issues related to sedation. Interestingly, tweets mentioning
metabolic disturbances and extrapyramidal symptoms had a similar pattern of
distribution to those tweets discussing sexual dysfunction, with aripiprazole,
lurasidone, and quetiapine accumulating most of the tweets. Finally, those
tweets referencing issues related to cognitive impairment were also distributed
heterogeneously among the different drugs; in this case, haloperidol,
olanzapine, and risperidone received the majority of tweets.


FIGURE 7

Figure 7. Tweets related to each antipsychotic medication by areas of clinical
interest. Percentages (%) were calculated with respect to the total number of
tweets generated on each antipsychotic medication.





DISCUSSION

In this study, we have found that Twitter users show a great interest in
antipsychotic medications focused on sexual dysfunction and sedation, especially
those users identifying themselves as patients. Tweets asking for or offering
help and those posted by institutions obtained the highest retweet-to-tweet
ratio, whereas tweets discussing cognitive complaints obtained the highest
like-to-tweet ratio. Moreover, health professionals did not command a strong
presence in driving Twitter conversations with regards to antipsychotics, nor
did medical institutions. In addition, tweets referring to the trivialization of
certain medications was frequently observed. Interestingly, paliperidone,
despite being among one of the most recent drugs launched on the market,
nevertheless has failed to generate much interest among Twitter users,
accounting for only a small number of tweets.

Furthermore, the medical treatment of patients with chronic diseases has become
an increasing challenge to society, with several factors involved in patient
outcomes (28). These are the efficacy and side effects of medications, access to
medical information and social considerations toward a particular disease and
its treatment, all of which serve to influence patients' attitudes in regards to
treatment (29, 30). Thus, identifying both patients' and the public's areas of
concern about diseases and their treatment in general, along with mental health
conditions in particular, is extremely relevant for improving clinical outcomes
(31). Correspondingly, the analysis of social media platforms such as Twitter is
a recognized tool to explore societal opinions of chronic health conditions
(32). Unfortunately, schizophrenia and psychosis-related disorders are
frequently mischaracterized pejoratively over Twitter (33–36). In fact, a
previous study found that school shooting was among the hashtags most frequently
associated with psychosis, which shows that there are still many people who
mistakenly associate psychosis with aggressiveness or violence (31). This
association of negative hashtags does not occur in other physical illnesses such
as breast cancer or diabetes, nor in other mental illnesses such as Hikikomori
(37). Furthermore, in this study, we have found that antipsychotic medications
are often trivialized in tweets. Collectively, Twitter data shows that patients
don't just suffer from psychosis as antipsychotic treatments are also the
targets of negative feelings and judgments by Twitter users, further
demonstrating the persistence of social stigma toward schizophrenia and its
treatment (38–41). This experience of stigmatizing attitudes against people with
schizophrenia may as a result be internalized, leading to “self-stigma” that
contributes to poor patient adherence to treatment (42, 43).

A comparative analysis of the prevalence of psychosis and the number of tweets
posted related to antipsychotic medications, contrasted with those referencing
treatment received for other chronic diseases, supports the notion of
considerable interest in antipsychotic medications among the Twitter community
(44). Interestingly, our results show that patients with schizophrenia are the
most common users posting content about antipsychotic medications. These results
contrast with the findings reported in research describing other medical
diseases mentioned on Twitter (45). Several reasons may explain this higher use
of Twitter by people living with schizophrenia. First, the anonymity provided by
this social media platform may favor use by persons suffering from
stigmatization. Secondly, the difficulty in personal communication that these
patients exhibit is mitigated through their online interactions. Third, easy
accessibility to Twitter and facility in posting content may also be involved in
the use of this platform by people living with schizophrenia who otherwise might
not possess the willingness and confidence to engage with others in person.

In addition, social media appears to be especially valuable for monitoring the
treatment experiences of individuals with psychotic related disorders who
frequently use online platforms to engage with people facing similar struggles
(46, 47). Our data shows that patients posted tweets related to specific areas
of clinical interest stemming from antipsychotic treatment, namely sedation,
sexual dysfunction, mood, and anxiety. In fact, sexual dysfunction is common
with antipsychotic treatment although its frequency varies among studies. For
example, a 2011 meta-analysis found that around half of those patients taking
clozapine, olanzapine, and risperidone reported sexual side effects, while those
using aripiprazole, quetiapine, and ziprasidone experienced lower rates (48).
However, other clinical trials have reported even lower frequencies of sexual
dysfunction. These differences may be due in part to patients' reluctance to
report sexual side effects during in-person interviews (49). In contrast, from
our Twitter data, sexual dysfunction was one of the most discussed topics, which
reinforces the notion of the platform as a resource that, as a result of its
particular characteristics, can better serve to facilitate patients' disclosure
of sensitive, potentially embarrassing information. The meta-analysis indicated
above also found that among patients who experienced sexual dysfunction
secondary to an antipsychotic medication, switching to aripiprazole was found to
be the most common solution (50). Interestingly, aripiprazole was the
antipsychotic that accounted for the most tweets related to sexual dysfunction.

Patients with schizophrenia have higher rates of depressive and anxiety
disorders than the general population (51). Noticeably, this issue has been
discussed more frequently by the patients themselves than by relatives or
friends, health professionals and health institutions. This is probably due to
the fact that patients are the ones most aware of these particular symptoms. On
the other hand, we found that patients posted less about cognitive complaints
than did their relatives or friends, suggesting that patients suffering from
cognitive deterioration are often less aware of their condition and less prone
to using Twitter. These findings also point to patients' psychopathologies
influencing the type of content they publish over social media (52, 53).

Furthermore, the relatively small number of tweets concerning metabolic
disturbances posted by patients with schizophrenia is worth highlighting.
Psychotic disorders are often associated with altered glucose homeostasis,
insulin resistance, hyperlipidemia, and hypertension (54, 55). This is notable
because resultant cardiovascular diseases are largely responsible for the
shorter life expectancy of people with schizophrenia, which is reduced by more
than a decade when compared with that of the general population (54, 55). Our
observation of the limited interest in metabolic disturbances shown by patients
is consistent with previous studies reporting that people with schizophrenia
often possess other risk factors for cardiovascular disease, in addition to the
use of antipsychotic medications, including a sedentary lifestyle, a poor diet
and smoking (56, 57). Thus, our results emphasize an insufficient awareness of
healthy habits by these patients (58). Additionally, it is troubling that
neither health professionals nor health institutions have dedicated much
attention to the promotion of cardiovascular health, despite metabolic
disturbances being responsible for the excessive mortality of these patients
(59).

Among the Twitter community, we also investigated the interest generated by
tweets related to specific antipsychotic medications. We used the number of
retweets and likes generated by each tweet as our standard of measure (37). Our
results showed that, with regards to user type, health institutions obtained the
highest retweet-to-tweet ratio. Being that they are usually very influential in
the medical field, health institutions tend to have a greater number of
followers than do personal accounts (60). This trend reflects the importance of
having health institutions and professionals involved in health conversations
since patient education is the first step in treatment effectiveness, a fact
that Twitter users appear to appreciate. In regard to content, those tweets
asking for or offering help were the ones most retweeted, further reinforcing
the notion of Twitter serving primarily as a community of users (61). Finally,
concerning specific medications, the ones that generated the most interest were
aripiprazole, levomepromazine, and haloperidol. These results perhaps may be
explained by the fact that Twitter is an international platform, as evidenced by
the high use of aripiprazole in Europe and the United States, while
levomepromazine and haloperidol are commonly employed in other parts of the
world for the treatment of schizophrenia. In fact, a majority of tweets, most
notably from South America, asked for assistance in acquiring these medications.
Probably for their connection to treatment, levomepromazine, and haloperidol
were among the two most retweeted and liked medications.

Previous research has suggested that it may be in the best interest of health
care providers and the pharmaceutical industry to focus on disease diagnosis and
treatment (62, 63). Additionally, it has been noted that some companies have
been especially active in promoting the benefits of their products over social
media (64, 65). In fact, the food and beverage industries have been increasingly
promoting their brands over social media platforms, as well as utilizing posts
to advertise them (66). However, according to our results, only a small
percentage of the tweets studied related to antipsychotic medications promoted
commercial activities. Moreover, paliperidone, despite being one of the latest
drugs launched on the market, accounted for only a small number of tweets. As
well, the content that obtained the highest retweet-to-tweet ratio was that
related to either the asking for or offering of help. These results suggest that
Twitter is frequented by users participating in conversations related to
antipsychotic medications for the primary purpose of seeking out information and
support rather than looking for commercial opportunities.


LIMITATIONS

This study has some limitations. First, since Twitter users tend to be younger
than the general population, our findings may not pertain to older age ranges
(67, 68). Indeed, younger patients in the earlier stages of psychotic disorders
are generally more active, suffer less cognitive impairment and are more likely
to engage over social media. Secondly, we were unable to examine how clinical
characteristics such as symptom severity, illness duration or cognitive
dysfunction found in individuals with psychosis influenced the content of their
social media posts due to a lack of psychiatric evaluation. Third, the codebook
design and text analysis we used imply a degree of subjectivity. However, this
methodology is consistent with previous medical research studies on Twitter and
could be applied to assorted topics by various authors (69–71). Fourth, among
the list of keywords we included both generic names and brand names but those
tweets that included spelling mistakes might have been left out.


CONCLUSIONS

Our results highlight the potential to leverage social media for a better
understanding of patients suffering from schizophrenia and their treatment in a
manner that is more comprehensive, one that not only includes health care
providers and health institutions but also family members, friends, and other
social media users. Although guidelines recommend assessing the efficacy of,
adherence to and tolerability toward treatment during medical consultations, in
the treatment of a patient with schizophrenia, one's individual environment
beyond the confines of a doctor's office is extremely important, especially as
it can frequently extend to include other influences, many of which are found on
social media and are worth analyzing due to the broadened perspective they can
offer.


DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available
by the authors, without undue reservation.


ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the
Research Ethics Committee of the University of Alcala (OE 14_2020). Written
informed consent for participation was not required for this study in accordance
with the national legislation and the institutional requirements.


AUTHOR CONTRIBUTIONS

MAA-M, JQ, and MÁ-M: conceptualization, validation, and resources. MAA-M, CD-V,
JS-V, LA, RR-J, JQ, and MÁ-M: methodology and data curation. MAA-M, CD-V, JS-V,
LA, JG, RS-B, FM, MO, GL, RR-J, JQ, and MÁ-M: formal analysis, investigation
writing—original draft preparation, and writing—review and editing. JQ and
MAA-M: supervision. MÁ-M: project administration and funding acquisition. All
authors have read and agreed to the published version of the manuscript.


FUNDING

This work was partially supported by grants from the Fondo de Investigación de
la Seguridad Social, the Instituto de Salud Carlos III (PI18/01726) (Spain), the
Programa de Actividades de I+D de la Comunidad de Madrid en Biomedicina
(B2017/BMD-3804), Madrid (Spain), and Helekulani SL.


CONFLICT OF INTEREST

JS-V was employed by IBM. RR-J has been a consultant for, spoken at events of,
or received grants from the Instituto de Salud Carlos III, the Fondo de
Investigación Sanitaria (FIS), the Centro de Investigación Biomédica en Red de
Salud Mental (CIBERSAM), the Madrid Regional Government (S2010/BMD-2422 AGES,
S2017/BMD-3740), JanssenCilag, Lundbeck, Otsuka, Pfizer, Ferrer, Juste, Takeda,
Exeltis, Casen-Recordati, and Angelini.

The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.


PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not
necessarily represent those of their affiliated organizations, or those of the
publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.


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Keywords: psychosis, psychiatry, neuropsychopharmacology, antipsychotics,
machine learning, artificial intelligence, pharmacoepidemiology

Citation: Alvarez-Mon MA, Donat-Vargas C, Santoma-Vilaclara J, Anta Ld, Goena J,
Sanchez-Bayona R, Mora F, Ortega MA, Lahera G, Rodriguez-Jimenez R, Quintero J
and Álvarez-Mon M (2021) Assessment of Antipsychotic Medications on Social
Media: Machine Learning Study. Front. Psychiatry 12:737684. doi:
10.3389/fpsyt.2021.737684

Received: 16 July 2021; Accepted: 19 October 2021;
Published: 18 November 2021.

Edited by:

Charlotte R. Blease, Beth Israel Deaconess Medical Center and Harvard Medical
School, United States

Reviewed by:

Anna Y. Kharko, University of Plymouth, United Kingdom
Mary V. Seeman, University of Toronto, Canada
Liz Salmi, Beth Israel Deaconess Medical Center, United States

Copyright © 2021 Alvarez-Mon, Donat-Vargas, Santoma-Vilaclara, Anta, Goena,
Sanchez-Bayona, Mora, Ortega, Lahera, Rodriguez-Jimenez, Quintero and
Álvarez-Mon. This is an open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and
the copyright owner(s) are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these
terms.

*Correspondence: Miguel A. Ortega, miguel.angel.ortega92@gmail.com



Disclaimer: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or
those of the publisher, the editors and the reviewers. Any product that may be
evaluated in this article or claim that may be made by its manufacturer is not
guaranteed or endorsed by the publisher.



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