Exploring Media Portrayals of People with
Mental Disorders using NLP
Swapna Gottipati
, Mark Chong
, Andrew Lim Wei Kiat
and Benny Haryanto Kawidiredjo
School of Information Systems, Singapore Management University, Singapore
Lee kong Chian Business School, Singapore Management University, Singapore
Keywords: Media Portrayal, Mental Illness, Stigmatization, NLP, Sentiment Analysis, Machine Learning.
Abstract: Media plays an important role in creating an impact in society. Several studies show that news media and
entertainment channels, at times may create overwhelming images of the mental illness that emphasize
criminality and dangerousness. The consequences of such negative impact may impact the audience with
stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental
disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders
(MDs) using text analytics and natural language processing. To date, most studies on media portrayal of
people with MDs have been conducted in developed Western countries. This study found that media articles
on MDs in Singapore were largely negative in sentiment; even quotes from experts contain aspects of stigma.
In addition, crime-related articles on MDs accounted for a significant portion of the corpus. Our model is also
extended to detect positive health articles that discuss recovery and motivation. We further developed a stigma
classifier based on the machine learning algorithms and text mining techniques. The classifier based on the
XGBoosts performed best with an F1-score around 76%.
Mental disorders (MDs) affect a significant segment
of society. According to a survey by the Institute of
Mental Health, one in seven Singaporeans have
experienced an MD in their lifetime (Choo, 2020).
Despite the prevalence of MDs, the afflicted are still
stigmatized: outreach programs have found that
persons with MDs in Singapore felt excluded from
contributing meaningfully to society and did not feel
they could fulfil their personal potential. Media
coverage was cited as one major factor contributing
to this deep-seated stigma (Understanding the
Quality”, 2020).
This study aims to examine Singapores news
portrayal of persons with MDs using text analytics
and natural language processing. To date, the bulk of
studies on media portrayal of people with mental
disorders (MDs) has been conducted in developed
countries such as the U.S., U.K., Canada, and
Australia. No similar study has been conducted in
Singapore, making this the first of its kind. This study
makes two contributions to the literature: First, it
provides editors and journalists with important
principles for creating non-stigmatizing stories about
persons with MDs. Second, this study’s approach for
identifying “aspects of stigma” in news articles can
be used by the editorial staff in the newsroom to weed
out stigmatizing messages and thus eliminate their
negative effects.
1.1 Stigma and MDs
Stigma has been defined as “a social construction that
devalues marked members of a community” (Smith,
2007, pp. 235). It involves the categorization of a
person into a group based on a distinguishing
characteristic or mark (Brown et al., 2003; Dovidio et
al., 2003) and results in discrimination, prejudice, or
stereotyping (Smith, 2007). As stigmas are shared
between members of a community, they impact
personal and group interactions (Smith, 2007).
Stigmas and the ensuing negative social attitudes
surrounding those suffering from mental disorders
(MDs) have been key health and social problem
(Corrigan & Penn, 1999). Large portions of
Gottipati, S., Chong, M., Kiat, A. and Kawidiredjo, B.
Exploring Media Portrayals of People with Mental Disorders using NLP.
DOI: 10.5220/0010380007080715
In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 708-715
ISBN: 978-989-758-490-9
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
communities were found to hold highly prejudiced
views and hostile attitudes toward these people
(Bagley et al., 2005; Dahaf, 1997). These negative
attitudes violate the civic rights, self-image, and
family life of those afflicted with MDs, and can
ultimately interfere with their social integration into
the community (Klin & Lemish, 2008).
Stigmatization “often results in the creation of laws
that (a) identify marks (or stigmata), (b) socially
isolate marked groups into geographical locations,
and (c) remove the rights of the marked groups and
other peoples’ obligations to them” (Smith, 2007, p.
235). This process can constrain the stigmatized
person’s access to health care, employment,
education, and housing (Brown et al., 2003; Miller &
Major, 2003], which can directly or indirectly impair
physical health and even lead to death (Smith, 2007).
At its most extreme, the stigmatized may even face
eviction from their communities, have their homes set
on fire (Wiener et al., 2000), or killed (Gilbert, 2010).
1.2 The Media and Stigma
Content analysis studies have shown that the negative
framing of MDs in the media contributes to the
public’s negative attitudes toward the afflicted (Sieff,
2003). Several studies (Granello & Pauley, 2007;
Philo et al., 1994; Thornton & Wahl, 1996; Wahl &
Lefkowits, 1989; McGinty et al., 2014; Corrigan et
al., 2004; Philo, 1998) reported a relationship
between exposure to negative portrayals of those with
MDs and negative attitudes toward them, including
fear of persons with MD. This relationship is true not
only of news stories in newspapers and magazines,
but also of fictional stories in soap operas, film, and
dramas (Philo, 1998).
Significantly, stigma surrounding persons with
MDs has been associated with negative outcomes
such as “poor treatment rates, discriminatory housing
and employment practices, and public opposition to
the expansion of mental health services in local
communities” (McGinty et al., 2018, p. 187). A study
of U.K. television’s depictions of MD argued that the
media imagery fuelled the fear of and hostility toward
MDs, which in turn significantly affected policies of
community care (Rose, 1998).
Nonetheless, longitudinal studies have shown that
campaigns can be effective in targeting the stigma
surrounding mental illness. For example, researchers
found favorable changes in attitudes and challenges
to stigma following the 2009 ‘Time to Change’ anti-
stigma program (Evans-Lacko, 2013). The number of
articles in the UK covering mental illness also saw a
substantial increase from 2008 to 2016, with a small
but significant positive change in newspaper
reporting on mental health topics through an observed
increase in anti-stigmatizing articles (Rhydderch et
al., 2016).
This study aims to examine Singapores news
portrayal of persons with MDs using text analytics
and natural language processing. To date, the bulk of
studies on media portrayal of people with mental
disorders (MDs) has been conducted in developed
countries such as the U.S., U.K., Canada, and
Australia. No similar study has been conducted in
Singapore, making this the first of its kind. This study
makes two contributions to the literature: First, it
provides editors and journalists with important
principles/methods for creating non-stigmatizing
stories about persons with MDs. Second, this study’s
approach for identifying “aspects of stigma” in news
articles can be used by the healthcare staff to act upon
when preparing or publishing such articles.
We study two text analytics methods to analyse the
articles. Firstly, rule-based approach as the news
articles should be analysed in a heuristic approach to
process human behaviour in discovering stigma in the
sentences. Secondly, we propose a machine learning
based approach for stigma prediction in news articles.
In this section, we present the methodology (data and
analysis) followed by the solution models.
2.1 Data Collection
The data collection process requires three
components; Sources, data crawling, and article
Data Sources: In consultation with Singapore’s
National Council of Social Services (NCSS), the
scope of the study was narrowed to focus on mental
health-related news articles published by the
Singapore Press Holdings (SPH) and MediaCorp,
Singapore’s largest media organizations.
Specifically, the study focused on The Straits Times
(SPH), The Business Times (SPH) and The New
Paper (SPH), Channel NewsAsia (CNA), and Today.
To commence data collection, a list of mental health-
related terms from a study exploring the portrayal of
mental health in Australian newspapers was used
(Kenez, 2015). These article search keywords were
tweaked to fit the local context (see Table 1).
Crawler: A combination of BeautifulSoup
(Richardson, 2007) and Selenium webdriver
(Narayanan, 2016) was used to crawl for the articles.
Exploring Media Portrayals of People with Mental Disorders using NLP
Article Details: As each website had a different
interface, three different programs were required to
retrieve the following article details; title of the
article, body text of the article, author, publisher,
published date, and section that the article belongs to
2.2 Data Preparation
Recall that we propose two different approaches for
the solution design to detect stigma in the articles.
The rule-based approaches are heavily dependent on
the dictionaries. Further, in our preliminary analysis,
we observed that health-related articles also appear
more often with the crime. Therefore, in the data
preparation stage, we consider these two observations
in our data preparation steps.
Lexicon Preparation: Four dictionaries (Table 1)
were prepared to facilitate data cleaning and analysis:
Stigmatizing phrases: The stigma dictionary
includes phrases such as “crazy”, “lunatic”, “commit
suicide” and “psycho” (Kenez, 2015).
Crime phrases: The crime dictionary includes
words such as “assault”, “victim”, “threaten”, etc
(“Vocabulary University”, 2020). This list aids to
separating crime and non-crime articles.
Medical phrases: The medical dictionary
includes words such as “depression”, “depressive”,
“phobia”, “phobic”, etc. This list was created from the
synonyms of the stigma words using Wordnet
(George, 1995).
Recovery phrases: The recovery dictionary
includes words such as “confidence”, “coping”,
“determination”, etc (Kenez, 2015).
Data Cleaning: The initial results were extensive
but yielded many articles that were not related to
mental health. Thus, several filtering steps were
undertaken to focus on mental health articles.
1. Article Filtering: Preliminary filtering of
articles was done by removing articles found in
sections that are not related to mental health. The
remaining articles were then ranked on the basis of
the number of keyword matches. Articles with fewer
than two mental health keyword matches were found
not to be related to mental health. These articles either
mentioned mental health in passing or were using the
term in a different context. These articles were
dropped from the corpus. Finally, these articles were
manually inspected to ensure that they are related to
mental health. After pruning, a final corpus of 1930
articles remained.
Table 1: Keywords used for the data collection and content
analysis (Examples).
Addiction Disorder, Behavioural Disorder, Bipolar,
Breakdown, Depressed, Depressing, Depression,
Depressive, Depressive Disorder, Despair, Distress,…
Article search ke
Bonkers, Commit Suicide, Craziness, Crazy, Deranged,
Freak, Gila, Halfwit, Insane, Loco, Loony, Lunacy, Lunatic,
Mad, Madman, Madness, Maniac, Maniac, Mentally Ill,
Nutcase, Nuts, Psycho, Psychotic, Siao, Unbalanced,..
Stigma dictionary
Acceptance, Actualisation, Adapt, Adapted, Autonomous,
Autonomy, Confidence, Confident, Contentment, Cope,
Coping, Determination, Determined, Efficacy, Esteem,
Fulfilment, Happiness, Happy, Meditate, Meditation, ..
Recovery or wellness dictionary
anxiety, anxious, anxiousness, bipolar, breakdown, clinical,
compulsive, dementia, depress, depressed, depressing,
depression, depressive, disorder, dying, headache, hoard,
hoarder, hurt, injury, manic, mental, nervous, obsessional,..
Medical dictionary
Abuse, Accomplice, Accused, Accuser, Aggravated assault,
Alcohol, Alert, Alias, Alibi, Alienate, Allegation,
Ammunition, Appeal, Armed, Arraignment, Arrest,
Arsenal, Arson, Art forgery, Assailant, Assault, Attack..
Crime dictionary
2. Article Categorization: In this step, the
characteristics of crime versus non-crime articles in
the context of mental health reporting were assessed.
To do this, a crime dictionary (see Table 1) was used
to identify crime articles. A detailed inspection
concluded that articles with fewer than three crime
keyword matches can be confidently classified as
non-crime. However, it was not possible to
confidently classify a document having more than
three crime keyword matches as being related to
crime. Therefore, the rest of the articles had to be
manually determined with the aid of the crime
2.3 Stigma Analysis Methods
In our study on news articles, the analysis is
conducted on both the article-level and quote-level.
1. Article-Level Analysis:
The article-level analysis examined the title,
subtitle as well as the first section of each article.
Based on feedback from NCSS, journalists put the
heaviest effort into crafting these sections of an
article. Similarly, readers tend to focus on the title,
subtitle, and first section, as their attention spans
decline in tandem with the length of the article.
Therefore, these elements are extracted from each
HEALTHINF 2021 - 14th International Conference on Health Informatics
2. Quote-Level Analysis:
The quote-level analysis examines only the
quotes in an article. Each quote in an article is
extracted and analyzed on multiple levels. Readers
also tend to read short, highlighted quotes as the
articles might be lengthy. The quotes are usually
placed under the pictures to attract the attention of
3.1 Rule-based Solution Model
Figure 1 shows the rule-based solution model used in
this study to discover the stigmatizing articles.
A. Text Extraction
In this step, for each article, we extract quotes using
python regular expressions such as quotations. The
quotes are used for quote analysis. Similarly, for
article analysis, we extract the title, sub-title, and first
section from the article text. In our preliminary
analysis, the first paragraphs mostly summarise the
information in the news. Moreover, the audience
usually read the first paragraph and the quotations in
the news articles.
B. String Phrase Match
In this step, we perform the textual match of the
keywords using the stigma dictionary to discover the
presence or absence of stigmatizing phrases. We
compiled a list of keywords and their associated
synonym using Wordnet (George, 1995) These
keywords include ‘crazy’, ‘lunatic’, and ‘psycho’.
C. Data Normalization
In sentiment extraction, terms are usually tagged as
negative or positive. In the process of obtaining the
sentiment scores, the medical terms commonly found
in mental health reporting such as depression, panic,
and anxiety are judged to be negative by the sentiment
lexicon. Therefore, such words are filtered through
the use of the medical phrase dictionary.
Figure 1: Rule-based Solution Design.
D. Sentiment scoring
Sentiment analysis computationally identifies the
writer’s feelings and attitude towards a particular
topic i.e. positive, negative, or neutral. Three types
of lexicon-based sentiment analyses are compared:
Lexicon based UIC Sentiment Analysis (Liu, 2015),
TextBlob Sentiment Analysis (TextBlob, 2020), and
Vader Sentiment Analysis (Hutto and Gilbert, 2014).
a) Lexicon: Liu, 2015 compiled a list of words
for sentiment detection (Liu, 2015). It consists of
around 6,800 positive and negative sentiment words
in the English language. An article’s score is the sum
of the scores of all its words.
b) TextBlob: This sentiment module has a
default implementation, NaiveBayesAnalyzer – an
NLTK classifier trained on movie reviews. TextBlob
returns 2 values, ‘Polarity’ and “Subjectivity”.
c) VADER: This is a rule-based and lexicon
sentiment analysis tool and unlike the typical bag-of-
words model, VADER also implements the
grammatical and syntactical rules. We consider the
compound score to identify the overall sentiment.
E. Data Visualization
Consolidation of the results is achieved by computing
the overall outputs from the stigmatizing phrase
analysis step and the sentiment analysis step. Both
steps help to indicate the presence of stigma in a given
text. The Tableau dashboard is used to visualize and
analyze the results.
3.2 Machine Learning Model
The machine learning approach for this study
attempts to develop a stigma classification model
using supervised learning techniques. Ground truth of
manually labeled mental health articles will be
formulated and be used to train the models. With
input from NCSS, a Google Form survey was
designed to conduct the labeling process. The survey
contains the following questions:
1. Do you think that the title of the article is
If yes, please identify the words/phrases in the
title that are stigmatizing
2. Do you think that the main (body) of the
article is stigmatizing?
If yes, please identify sentences in the main article
that are stigmatizing. Then identify a few specific
words/phrases in the main article that are stigmatizing
3. Is this article crime-related?
If yes, does the article imply that the person's
mental health condition caused the crime? If you
answered yes to the above question, what are the
problematic statements/phrases?
Questions 1 and 2 attempt to identify whether the
title or the body of the article is stigmatizing.
Exploring Media Portrayals of People with Mental Disorders using NLP
Question 3 is only relevant to crime-related articles
and attempts to find out if a correlation is made
between the mental health condition and crime.
The classifier development process would
involve text preprocessing, model training/tuning,
and model selection. The best performing model
would then be selected based on our selected
performance metric, F1-Score and applying cross-
validation. In this study, we implemented four
classifiers (Christopher, 2006) as described below.
A. Random forest: RF is a classification
algorithm that works by forming multiple decision
trees at training and testing it outputs the class that is
the mode of the classes (classification).
B. Log-regression: Logistic regression (LR)
statistical method is used for analyzing the dataset
and produces a binary outcome. It is a specific
category of regression and it is used in the best way
to predict the binary and categorical output. The LR
is the fast prediction algorithm.
C. Support vector machines: SVM classifier
represents the instances as a set of points of 2 types in
N-dimensional space and generates an (N - 1)
dimensional hyperplane to separate those points into
2 groups. SVM attempts to find a straight line that
separates those points into 2 types and is situated as
far as possible from all those points.
D. XGBoosts: XGBoost is a decision-tree-
based ensemble machine learning algorithm that uses
a gradient boosting framework. It provides a stronger
regularization framework that constrains overfitting
to overcome this shortcoming. Therefore, it has
gained much popularity recently and has become a
state-of-the-art machine learning algorithm.
4.1 Rules-based Evaluations
In this section, we first present the results of the
evaluation of the steps in the rule-based model.
Table 2: Sentiment classification results.
Model Precision Recall F1-Score Accuracy
Vader 0.71 0.77 0.73 0.73
Textblob 0.59 0.61 0.58 0.63
Lexicon 0.7 0.69 0.58 0.61
As depicted in Table 2, Vader sentiment classifier
is the most optimal one in terms of the F1- Score
which is the harmonic mean between the recall and
the precision. Another curious fact is that positive and
neutral labels are effectively separable by Vader
unlike the negative variables. This could be because
extreme negative keywords indicating the stigma and
crime are removed from the data processing stage.
From this analysis, we choose Vader as the best tool
and continued with the sentiment classification for all
the articles.
4.2 Machine Learning Evaluations
We asked the human judges to label 154 articles. Out
of 154 articles being labeled, we found that 22% of
the title to be stigmatizing. We then combined these
titles with the stigmatizing sentence found in the
article body and formed the labeled dataset. This
dataset consists of a total of 218 sentences and titles,
with 99 out of 218 sentences found to be stigmatizing.
For our supervised learning model, we use TFIDF
(Term Frequency Inverse Document Frequency) as
features. All the classification models were tested and
we used Cross-Validation and F1-Score as
performance metrics to evaluate and select the best
model. Cross-validation with 5 folds was used because
our dataset is small. In this method, the dataset is
divided into five folds. Each run, one-fold will act as a
validation set and the rest will act as the training set.
F1-Score evaluation metric is selected as our
evaluation metric since it considers both the precision
and recall of the test to compute the score. We
perform parameter tuning on all the models using
Random Grid Search with F1 score as our target. The
parameters corresponding to the best F1 score were
then employed to train our model. Table 3 depicts the
model results.
Table 3: Stigma classifier performance.
Model Precision Recall F1-score accuracy
RF 0.824 0.667 0.737 0.773
LR 0.786 0.524 0.629 0.705
SVM 0.833 0.476 0.606 0.705
XGBoost 0.762 0.762 0.762 0.772
XGBoost yielded the best F-Score of 0.762 and it
is selected as our machine learning model. Our
parameters for the model are as follows; 'subsample':
0.8, 'min_child_weight': 1, 'max_depth': 11, 'gamma':
1. Stigma classifier based on XGBoosts is suitable to
predict the stigma on the news articles at the sentence
5.1 Exploratory Analysis of Articles
The article statistics are shown in Figure 2. The
quarter-to-quarter distribution of mental health-
HEALTHINF 2021 - 14th International Conference on Health Informatics
related articles remained fairly consistent throughout
the period under study, averaging around 130 articles
each quarter. The number of articles peaked in Q3 of
2016 this can be attributed to an increase in the
number of editorials on suicide awareness after the
Samaritans of Singapore (SOS) annual report showed
that the number of suicide cases in 2016 was the
highest in recent years.
Figure 2: Distribution of articles over the years.
The Straits Times accounted for nearly half the
total number of articles produced during the period.
The next three publishers, Today Online, Channel
NewsAsia, and The New Paper each published
roughly the same number of articles. The Business
Times had the least number of articles related to
mental health, with just 39 articles across the three-
year period (see Figure 2b).
5.2 Stigma Analysis in Articles
5.2.1 Sentiment Analysis and Crime
Articles about crime-related mental health issues
made up about 41% of all articles on mental health
The overall sentiment of mental health-related
articles tends to be negative – negative articles made
up more than half of the corpus (see Figure 3).
Distribution of articles by
Distribution of crime
articles b
Figure 3: Article statistics by types and sentiments.
Of 41% of crime articles, about 88% of articles
are negatively polarised. This is expected as the
nature of crime words is usually negative. A high
proportion of crime articles could influence the public
to equate MDs with criminality. In the next sub-
sections, we shall study the stigma in the articles.
5.2.2 Stigma Analysis
1. Analysis – Article level
Depression was the most frequently mentioned
MD (see Figure 4). According to the World Health
Organization, depression is the leading cause of
disabilities worldwide and the most common MD in
Singapore (“Depression Vocabulary”, 2020),
affecting one in sixteen Singaporeans. The next two
most common MDs were anxiety and trauma.
Hoarding was the least reported MD.
The color coding represents how positively or
negatively each MD was portrayed; a negative
portrayal is shown in red and a positive portrayal is
shown in green. Personality disorder, paranoia and
brief psychotic disorder were the most negatively
portrayed MD. Hoarding was the only mental
condition with a neutral sentiment. “Mentally ill”,
“psychotic” and “commit suicide” are the top three
frequently used terms to label people. At the article
level, we observed 1.5% of titles are stigmatized,
1.55% of sub-titles are stigmatized and 6.6% of first
sections are stigmatized. Though the numbers are
very low, given the impact of stigma, detecting such
articles during the editorial stage is critical.
Figure 4: Distribution of the type of mental health
2. Analysis – Quote level
An analysis of the quotes in each article was
conducted to determine if the quotes contain
stigmatizing elements. The result of a sample quote
analysis is shown in Figure 5.
In this example, the word “crazy” in the quote
perpetuates the negative stereotype that people with
MDs are divorced from reality, irrational, or
incapable of making decisions.
Exploring Media Portrayals of People with Mental Disorders using NLP
Our model extracted 9914 quotes in total and we
observed only 20 quotes which are 0.002% of quotes
are stigmatized. This indicates that in Singapore's
new articles, the stigma is low in terms of the quoted
content compare to the article title and first sections.
Figure 5: Quote analysis.
5.3 Media Positive Impact Analysis
Articles on wellbeing and recovery help to educate
the public about mental health conditions, decrease
discrimination towards persons with mental
conditions, and encourage help-seeking behavior. To
find these articles, keywords from a recovery
dictionary were used. In total, these articles made up
about 15% of all mental health reports. These articles
are more likely to be positive in sentiment and
frequently appear in the lifestyle and commentary
sections of the media (see Figure 6).
Figure 6: Recovery and wellbeing articles.
This study shows that mental health-related articles in
Singapore were primarily negative in sentiment and
crime-related articles accounted for a significant
portion (40%) of the corpus (see Figure 3). The high
proportion of articles associating MDs with crime is
troubling, as it may influence the public to equate
MDs with danger and violence. To complicate
matters, the quotes – including those given by experts
contain stigma aspects. As experts have been rated
as the most credible source of information (Edelman,
2020), their words carry disproportionate weight.
As media articles have the potential to promote
mental health and contribute to de-stigmatization
(Klin & Lemish, 2008), editors can play a more
proactive gatekeeping role and counteract the largely
negative portrayal of MDs with articles on wellbeing
and recovery, which accounted for only 15% of the
corpus. In addition, they (and their newsrooms) can
use the solution model in this study to identify and
weed out stigmatizing elements in their articles about
There are three limitations to this study. First,
although a keyword-based approach was used to filter
the articles, it is possible that the keyword list is non-
exhaustive. This may have resulted in the unintended
exclusion of some mental health articles from the
study. Second, only the first section of each article
was analyzed for stigma. Future studies can overcome
this limitation by applying the model to the entire
article text. Finally, our medical dictionary is limited
to medical mental illness terms only. There might be
other medical terms in the articles which may affect
the sentiment scores of the mode. In particular, they
may have impacted the negative scoring by the
sentiment model. A machine-learning approach can
overcome this limitation by training the model with
both the stigma and non-stigma datasets.
This study analyzed Singapore media articles to shed
light on media stigmatization of MDs in the country.
It proposes a rule-based solution model based on text
mining and NLP techniques which can automatically
identify aspects of stigma in media articles. In
addition to flagging stigmatizing articles, the model
can identify specific sentences and quotes that are
stigmatizing. Such a model can help editors to remove
stigmatizing elements before the publication of an
article. At the same time, however, it can identify
positive articles (e.g. about recovery help and help-
seeking behavior). The model’s dual advantage can
empower editors to address the negative tone that is
prevalent in media coverage of MDs. Last, this model
can be used to run a course for journalists who cover
MDs. Even though this study focused on news
articles, the methods of analysis and solution model
can be extended to other media, such as social media,
blogs, opinion articles, and expert reviews.
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