# Revealing Patient-Reported Experiences in Healthcare from Social Media using
the DAPMAV Framework

Curtis Murray
The University of Adelaide
Adelaide, SA 5005
<EMAIL_ADDRESS>
&Lewis Mitchell
The University of Adelaide
Adelaide, SA 5005
<EMAIL_ADDRESS>
&Jonathan Tuke
The University of Adelaide
Adelaide, SA 5005
<EMAIL_ADDRESS>
&Mark Mackay
The University of South Australia
Adelaide, SA 5000
<EMAIL_ADDRESS>

###### Abstract

Understanding patient experience in healthcare is increasingly important and
desired by medical professionals in a patient-centred care approach.
Healthcare discourse on social media presents an opportunity to gain a unique
perspective on patient-reported experiences, complementing traditional survey
data. These social media reports often appear as first-hand accounts of
patients’ journeys through the healthcare system, whose details extend beyond
the confines of structured surveys and at a far larger scale than focus
groups. However, in contrast with the vast presence of patient-experience data
on social media and the potential benefits the data offers, it attracts
comparatively little research attention due to the technical proficiency
required for text analysis. In this paper, we introduce the Design-Acquire-
Process-Model-Analyse-Visualise (DAPMAV) framework to equip non-technical
domain experts with a structured approach that will enable them to capture
patient-reported experiences from social media data. We apply this framework
in a case study on prostate cancer data from /r/ProstateCancer, demonstrate
the framework’s value in capturing specific aspects of patient concern (such
as sexual dysfunction), provide an overview of the discourse, and show
narrative and emotional progression through these stories. We anticipate this
framework to apply to a wide variety of areas in healthcare, including
capturing and differentiating experiences across minority groups, geographic
boundaries, and types of illnesses.

_K_ eywords Patient Experience $\cdot$ Social Media $\cdot$ Natural Language
Processing $\cdot$ Topic Modelling $\cdot$ Sentiment Analysis $\cdot$ Prostate
Cancer

##  1 Introduction

Health systems in countries like Australia represent a significant investment
of public resources [1]. There has been increasing interest in not only the
activity that such systems generate, but also in better understanding and
measuring patient experience and patient outcome [2, 3, 4, 5]. Historically,
there has been a focus on using mortality as a measure of outcome [6]. If
greater or fewer people die while in the care of a facility, the facility can
estimate that it is performing worse or better respectively. Alone, however,
this outcome provides an insufficient view of healthcare for complete
performance monitoring, as it overlooks many other aspects of healthcare, and
potentially capturing ill performance once lives have already been lost [7,
8]. To attain a more holistic approach to performance monitoring, the
Australian Health Performance Framework (AHPF) identifies indicators of health
system performance as accessibility, appropriateness, continuity of care,
effectiveness, efficiency and sustainability, and safety [9]. As such, these
indicators are incorporated into health systems performance monitoring and are
effectively measured by hospitals. However, the perspective of a hospital
alone is incomplete for a comprehensive assessment of the health system’s
performance; there are some aspects of care where patients are the best judges
of their health-related outcomes [10].

###  1.1 Patient-reported experiences are useful

Areas of healthcare where patients may be the best judge include health-
related quality of life, physical function, psychological well-being,
subjective symptoms such as pain, social function, and cognitive function
[10]. The World Health Organisation has identified the importance of patient
engagement in benefiting healthcare systems, processes, and policies [11].
Patient-reported outcome measures (PROMs) and patient-reported experience
measures (PREMs) have been developed to capture patient feedback and address
the gap in healthcare performance monitoring by harnessing the patient’s
perspective [12]. This feedback is vital in providing quality healthcare that
matches the public’s expectations. It facilitates the alignment of resources
with patient priorities and can act as an accountability structure [13]. Due
to its importance, patient-reported feedback is used in performance indicators
for many institutions [14].

###  1.2 Traditional methods of capturing patient-reported experience overlook
insightful information

Surveys have been the traditional tools of choice for capturing patient-
reported experiences. For example, the Australian Commission on Safety and
Quality in healthcare released the Australian Hospital Patient Experience
Question Set (AHPEQS) – a core set of satisfaction questions – as a tool that
hospitals can use to capture patient-reported experiences [15]. This survey
captures aspects of patient feedback related to general satisfaction in a
multiple-choice format. For example, Question 1 states ‘My views and concerns
were listened to’, with the prescribed responses ‘Always’, ‘Mostly’,
‘Sometimes’, ‘Rarely’, ‘Never’, and ‘Didn’t apply’. Other questions relate to
needs being met, feeling cared for, feeling involved, feeling informed about
care, communication, pain management, confidence in safety and care,
unexpected harm, and overall quality of care, all with similar programmed
answers. The full set of questions is provided in Appendix A in Table 4.

While surveys such as the AHPEQS capture specific areas of patient feedback,
they are often restricted by their predefined scope and generality. These
rigid surveys can result in blind spots in understanding the patient journey.
For example, the AHPEQS asks whether ‘individual needs were met’, but there is
no scope to capture the patient’s specific needs. The AHPEQS also asks how
often the patient ‘felt confident in the safety of their treatment and care’,
yet does not facilitate the patient to outline when or where they felt unsafe.
Deployment of these surveys is time-consuming, involving research and approval
to ensure that the survey meets specific needs. While this is to the benefit
of a survey in its ability to capture necessary information, it leads to a
delayed deployment. Hence the survey may miss critical, time-dependent data.
To ensure that patient-reported experiences are captured more thoroughly
through survey, sufficiently long surveys are necessary. Survey length,
however, is negatively associated with respondent rate [16]; balance must be
struck between respondent rate and survey length. Another tool that plays a
role in capturing patient-reported experience is the focus group. Focus groups
have more freedom in their scope than surveys and hence have may have fewer
blind spots. However, they are time-consuming, difficult to scale to get a
population-level understanding of patient experience, and lack
generalisability to the broader population [17]. The primary purpose of the
focus group in this setting is to be an exploratory tool that can direct
survey questions to align with areas of patient concern [18]. In order to
effectively capture patient experience, patients need freedom to discuss
relevant issues that allow for unconstrained reports of patient experience and
patient outcome at an appropriate scale that neither surveys nor focus groups
offer.

###  1.3 Social media is a rich source of information

Social media hosts vibrant forums featuring the discussion of topics related
to social issues [19, 20]. These discussions are information-rich and may
provide meaningful insights into how complex issues are perceived at
individual and community levels. As users determine discussion topics and
their details, the discussion often corresponds to social issues relevant to
their respective communities [21]. The broad coverage of issues is directly
aligned with areas of social concern. As many of these insights have the
potential to be highly valuable, there is a growing demand for their capture
[22, 23, 24]. Social media is, therefore, rapidly being adopted by researchers
as a tool to explore these social issues to uncover valuable insights into
public opinion [25, 26, 27]. However, social media data is unstructured. This
lack of structure introduces a challenge, as structured data is far easier to
analyse at scale. Technological improvements in natural language processing
aid in revealing latent structure in otherwise unstructured data. While this
means researchers have pathways to analyse social media and other unstructured
data at scale, this pathway is often inaccessible to those without a technical
background. There is a need for guidance in analysing unstructured data for
researchers with a non-technical background.

###  1.4 Social media hosts patient-reported experiences

Discussion around healthcare is widespread on social media platforms such as
Twitter, Facebook, and Reddit [28, 24]. Here, patients have the scope to
discuss what is important to them in as much detail as they want. Due to its
anonymity and lack of direct face-to-face interactions, social media has been
evidenced to elicit high levels of self-disclosure in people, who tend to
reveal themselves more intimately than in face-to-face interactions [29]. This
level of self-disclosure may make social media a rich source of information
for revealing personal patient-reported experiences to researchers. Healthcare
systems may utilise this information to improve their facilities and
procedures. Researchers have called for mining social media data as a
complementary tool to improve healthcare systems [24]. To aid future
researchers in deciding which platform to gather patient experience data from,
we summarise the research use of Twitter, Facebook, and Reddit and assess
their appropriateness in the context of patient-reported experiences.

###  1.5 Social media research in patient-reported experiences

Table 1 shows that there are over 15,000 Google Scholar search results for
articles related to _patient experience_. Examples of these can be seen for
Facebook [30], Reddit [31, 32, 27], and Twitter [33, 34, 35].

There is a rapid growth of social media research related to patient-reported
experiences. Figure 1 shows exponential growth in the number of Google Scholar
search results for a combined search of _Natural Language Processing_ and
_patient experience_. These Google Scholar searches were made using the
following search terms under conditions that will be subsequently described.

       [social media platform] AND ‘natural language processing’ OR ‘NLP’ AND
       ‘patient experience’ OR ‘patient experiences’ OR ‘patient reported experience’ OR
       ‘patient reported experiences’

The text [social media platform] was replaced with ‘Facebook’, ‘Reddit’, and
‘Twitter’. The search was filtered by date to find the cumulative number of
articles that had been published by each successive year.

Figure 1: Cumulative Google Scholar search results for articles related to
_Natural Language Processing_ and _patient experience_ for social media
platforms; Facebook, Reddit, and Twitter. A binary logarithm transformation
has been applied to the y-axis.

###  1.6 Social media research overview

Table 1: Number of daily active users [36, 37, 38] and Google Scholar search results for Facebook, Reddit, and Twitter using search terms _patient experience_ and or _Natural Language Processing_. Retrieved 29 June, 2022. Table 3 shows the exact Google Scholar search terms, corresponding to the column _Search Key_ |  Search Key (See Table 3) | Facebook | Reddit | Twitter
---|---|---|---|---
Daily Active Users |  - | 1,920,000,000 | 50,000,000 | 229,000,000
|  - | 6,790,000 | 2,150,000 | 7,580,000
|  Patient Experience | 17,400 | 15,200 | 17,700
Google Scholar |  Natural Language Processing | 82,300 | 20,900 | 119,000
|  Patient Experience + Natural Language Processing | 1,050 | 260 | 1,130

Twitter, a popular social media platform, is often the subject of research
[39]. On Twitter, users post online in the form of tweets. Prior to October
2018, these tweets were limited to 140 characters and now are limited to 280
characters. Tweets may contain hashtags that allow users to tag keyword
phrases that correspond to specific topics or issues. Reddit, colloquially
known as the ‘front page of the internet’, is another social media platform
that receives attention from researchers [40]. Discussion on Reddit occurs in
a different form to that on Twitter. On Reddit, users post to communities that
cover specific topics, known as subreddits. A user may create a new post to a
subreddit or may post a reply to another user’s post or reply. Facebook is
another social media platform that attracts attention from researchers. On
Facebook, discussion often occurs in the form of posts and replies made to
users’ walls, groups, businesses’ walls, and other pages.

Twitter is the most frequent subject of research, with approximately 1.1 times
and 3.5 times more Google Scholar search results than Facebook and Reddit
respectively, seen in Table 1. One possible mechanism for Twitter’s research
popularity over Reddit’s could be Twitter’s greater popularity, having 4.49
times more daily users than Reddit, also seen in Table 1. With almost ten
times more users than Twitter, Facebook is underrepresented in research
articles per daily active user. This underrepresentation may be explained by
restrictions imposed on Facebook’s data accessibility in response to the 2018
Cambridge Analytica data scandal [41, 42]. The widespread use and data
policies of Twitter and Reddit make them suitable platforms for research over
other platforms such as Facebook, whose data policies are more prohibitive for
research [43].

Twitter has no prescribed community structure for the discussion of specific
topics. Topics on Twitter are optionally discussed through the use of
hashtags. This approach allows posts to be identified as belonging to multiple
topics simultaneously. Reddit, contrastingly, has no obvious method for cross-
topic discussion. Reddit’s structure instead necessitates posting to exactly
one subreddit. This subreddit usually hosts most of the discussion around that
topic, creating a sense of community. One study noticed that in the context of
the ‘Me too’ movement, discussion on Twitter was less personal than on Reddit
and instead focused on encouraging others to speak up [44]. Reddit’s lack of
post character limit that Twitter enforces may make it a more suitable
platform for deeper discussion surrounding healthcare, as users do not need to
restrict the content they share to fall under the character limit.

###  1.7 Challenges of using social media data

Surveys offer a highly structured medium to collect public opinion data. This
structure results from targeted questioning in conjunction with predefined
multiple-choice answers. The structured format allows responses to be tallied
into a format that lends itself to analysis. Free-text data, however, lacks
the structure possessed by surveys and imposes a significant obstacle in
eliciting patient-reported experiences from social media data [45]. Natural
Language Processing (NLP) models aim to address this lack of structure by
using statistical and machine learning models to uncover latent structure
[46]. This structure reveals patterns or trends across corpora that allow for
an at-a-glance understanding of the content of the documents that close
reading is not needed to obtain. Once this latent structure has been
uncovered, we can enjoy data ripe for analysis.

###  1.8 The need for a guiding framework

Natural language processing tools are usually applied by researchers with a
technical background. Many of these tools are left inaccessible to a non-
technical person. Many healthcare researchers and practitioners without the
background necessary to know how these tools should be applied, yet the domain
knowledge to make the most of them, are unable to mine111To analyse and draw
insights from data at scale. social media text to uncover patient-reported
experiences. This dichotomy motivates the need for a generalised reproducible
framework that facilitates healthcare researchers to exploit free-text
narratives. We introduce the Design-Acquire-Preprocess-Model-Analyse-Visualise
(DAPMAV) framework to meet this need. The DAPMAV framework outlines the
complete process from the design of the problem to the visualisation of
results for text analysis from social media discourse.

This framework will be explored through the lens of prostate cancer
discussion. Aside from non-melanoma skin cancers, prostate cancer is
Australia’s most commonly diagnosed cancer (21,808 diagnoses in 2009). It is
also the fourth leading cause of death amongst Australian men (3,294 deaths in
2011) [47].

Figure 2: Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework
for analysis of social media discussion relating to patient-reported
experiences.

##  2 DAPMAV Framework

###  2.1 D - Design

The design stage is the first stage in the DAPMAV process, summarised in the
first column of Figure 2. In the design stage, we choose a relevant social
media data source. In this example, we will walk through the steps to find
relevant data on Reddit. Following this choice, we select the time frame to
collect the posts in.

One way to select the subreddit would be to use the Reddit search bar to
search for the overall theme of the discussion and select a relevant community
from the communities tab. Consider for this example that we are interested in
observing prostate cancer discussion. We type ‘Prostate Cancer’ into the
search bar. This is seen in Figure 3(a). The results of this search are seen
in Figure 3(b), where we have navigated to the communities tab. We notice a
specific prostate cancer community called ‘/r/ProstateCancer’. We can click on
this community to be taken to the subreddit (Figure 3(c)). We see posts made
by Reddit users, an _About Community_ , as well as _flairs_ , and post tags
entered by users that can be filtered. We can explore the posts to ensure they
are relevant to the conversation we want to capture. If so, we move on to
specifying our timeline.

(a) Using the Reddit search bar to search for Prostate Cancer. Once the search
term is entered, we can click _Search for ‘Prostate Cancer’_ to perform the
search. Results are seen in Figure 3(b).

(b) Reddit search results for _Prostate Cancer_ , performed in 3(a). The first
result is a community titled /r/ProstateCancer. We click on this entry to
navigate to the /r/ProstateCancer subreddit, seen in Figure 3(c).

(c) Prostate Cancer subreddit resulting from the searches made in Figures
3(a)-3(b). Posts can be seen in the left column (blurred for anonymity), and
details about the community can be seen in the right column.

Figure 3: Process of finding a relevant subreddit.

The _About Community_ box on the right-hand side of Figure 3(c) shows that the
subreddit was created on August 19, 2010. This date may be important as some
subreddits may be too new for their intended research purposes.

###  2.2 A - Acquire

Data acquisition is the second stage of the DAPMAV process, summarised in the
second column of Figure 2. In the data acquisition process, we retrieve the
Reddit posts in the subreddit that we wish to analyse from the period
specified in the design phase. We obtain the Reddit posts using the Pushshift
Application Protocol Interface (API), an interface to the Pushshift archiving
platform that provides researchers access to Reddit data [48]. The choice of
the Pushshift API may be favourable to Reddit’s API as it offers a greater
rate of post-acquisition [48].

Social-media datasets are vast and may have entries in the order of millions
or billions. Computationally demanding algorithms inhibit the rapid
exploration of large text datasets. To avoid lengthy processing, we can thin
out our text data to reduce the size of the dataset, if necessary. As a means
of thinning out our dataset, we can sample the documents in our corpus without
replacement. The number of samples we take will determine the speed of our
analysis. The amount that is thinned out, if thinning is necessary at all,
will be determined by the researcher’s intent. If, in the later analysis, we
find results lack specificity, we may want to thin the dataset out less.

Reddit posts also may be tagged in certain ways. As an example, the subreddit
‘/r/COVID19Positive’ can have posts tagged with the flairs ‘Question-for
medical research’, ‘Tested Positive - Family’, ‘Presumed Positive - From
Doctor’, ‘Tested Positive - Me’, ‘Question-to those who tested positive’, and
finally ‘Tested Positive’. These flairs give us additional data about posts we
may want to consider. In particular, if we are only interested in posts
relating to a particular flair, we can filter out the other flairs to reduce
the run time of the models and potentially improve the specificity of the
results. However, in cases with limited data, including additional yet largely
relevant flairs may improve the model’s performance.

###  2.3 P - Preprocess

Preprocessing is a preliminary form of data processing prior to the principal
analysis that prepares the data to be in a form that is suitable, effective,
and efficient for the required analysis [49]. Efficiency is primarily centred
around finding a representation of the data that reduces the feature space
while maintaining as much relevant information as possible. In this section,
we outline basic procedures typically used for text analysis [50, 51].

####  2.3.1 Tokenize

Tokenization is the process of deconstructing text into a list of distinct
units, called tokens [50]. A token may be a unigram (a single word), a bi-gram
(two consecutive words), more generally, an n-gram (n consecutive words), a
sentence, and many other things.

####  2.3.2 Remove Stopwords

When conducting text analysis, we may wish to remove a list of words, denoted
stopwords, from the corpus [46]. Words that are often considered stopwords are
common words that add little contextual information. Examples of stopwords
include ‘the’, ‘and’, ‘of’, and ‘in’. Removal of these words decreases the
dimensionality of the data, thereby reducing the computational complexity and
hence the time taken to model.

####  2.3.3 Remove Small Documents

Excessively small documents may be removed in text preprocessing [52]. Small
documents may not contain sufficient information to be valuable, depending on
the context of the data and the research question. Including small documents
may dilute the effectiveness of the results while also increasing the
computational complexity. Researchers will benefit from assessing their needs
based on the context of their problem and the size of their dataset when
deciding whether or not to set a minimum document length threshold.

####  2.3.4 Remove Uncommon Words

Removal of uncommon words is another common preprocessing technique to reduce
the feature space [53]. Word frequencies typically follow a Zipfian
distribution, and are heavily tailed, with a large number of rare words [54].
As a result, removing only exceedingly rare words (for example, those with
less than three occurrences) can significantly reduce the feature space
without compromising critical information.

###  2.4 M - Model

####  2.4.1 Topic Modelling

Large amounts of unstructured text data pose a challenge to researchers as
their high complexity masks information. Tools in natural language processing
(NLP) allow information to be uncovered and presented in a structured format
that we can then analyse. Topic modelling is one such NLP tool that uncovers
information in unstructured text data. Topic modelling can be viewed as a
dimension reduction tool that projects high-dimensional text data onto low-
dimensional spaces, peeling back complexity to reveal interpretable structure
[46]. This dimension reduction occurs through the summarisation of text into
topics or conversational themes. Documents (which can be social media posts)
are reduced to mixtures of topics, providing an overview of their contents.
Topics are represented by collections of words that are used in similar
contexts. Figure 4 shows the topic modelling process, taking documents,
finding appropriate topics, and then representing the documents as mixtures of
these topics.

Figure 4: Topic Modelling Overview

####  2.4.2 Sentiment Analysis

Sentiment analysis is another natural language processing tool that, similar
to topic modelling, reduces the complexity of documents. However, instead of
deconstructing documents into topics, sentiment analysis identifies emotions
conveyed in the text.

###  2.5 A - Analyse

In a grounded theory approach, where we let the data drive the research,
analysis and visualisation are heavily intertwined. Observations made from the
data through visualisation often evoke further questions. These questions can
be used to cultivate hypotheses that may be answered through analysis. Results
from the analysis may be explored through further visualisation. This further
visualisation may lead to more questions, hypotheses, and further analysis to
complete the cycle.

Topic modelling allows us to take our social media posts and uncover the
topics that people discuss. The levels of discussion for each topic tell us
how often particular themes are discussed. Profiling documents in this way
yields an overview of the overall themes being discussed, themes in individual
documents, themes in documents belonging to specific groups, longitudinal
trends, and more, depending on the questions we want to ask. Similarly,
sentiment analysis results can be analysed to see overall sentiment, sentiment
in specific posts or groups of posts, and temporal changes.

###  2.6 Visualise

To produce a topic word cloud, we make use of the topic distributions found
from topic modelling. These tell us the probability that a word is in a topic,
$P(\text{word}|\text{topic})$. Visualisation of these probabilities through
word clouds illustrates words contained in the topic. The size of a word in a
word cloud relates to the probability that the word is selected from the
topic.

####  2.6.1 Dimension Reduction

Visualising topic interactions allows for the exploration of related themes.
Topic interactions may be found in multiple ways. The network’s approach to
topic modelling uses a hierarchical stochastic block model (hSBM), and
inherently captures topic interactions through a matrix of community-to-
community interactions [51, 55, 56]. This matrix tells the probability of
edges between and within communities. Topic interactions may alternatively be
found through their correlations. An effective way to quickly visualise these
interactions is to convert the interactions into a measure of dissimilarity
and produce a topic map by performing a dimension reduction of the high-
dimensional topic interaction data down to two dimensions. Traditional
approaches such as principal components analysis (PCA) and multidimensional
scaling (MDS) attempt to preserve the pairwise distance structure of the high-
dimensional data in a lower-dimensional projection. Modern approaches such as
t-distributed stochastic neighbour embedding (t-SNE) and uniform manifold
approximation (UMAP), which focus on preserving local distances, not global
distances, typically perform well, especially when viewing local structures.
These approaches may not be as appropriate as MDS when the importance is on
considering the global structure [57, 58, 59].

##  3 /r/ProstateCancer Data

We follow the DAPMAV process to acquire $4074$ posts and replies from the
Reddit community /r/ProstateCancer between 2019 and 2021. These consist of
$631$ posts and their $3443$ replies. The mean number of non stopwords in the
posts is $50.6$, and $31.2$ for their replies ($34.2$ combined mean). The
longest post consists of 738 non stopwords. We restricted posts and replies to
consist of at minimum ten non stopwords to remove documents with few words, as
inspection showed documents with fewer than 10 non stopwords to have largely
uninformative discussion. The prostate cancer stories posted to Reddit often
feature mentions of the subject’s age. String detection using REGEX was used
to estimate subjects’ ages (in decade brackets). Figure 5 illustrates the
results of this. In addition to age, subjects of the narrative are identified
by male family member names, seen in Figure 6 REGEX was used to estimate the
identity of the male family members: brother, father, grandfather, husband,
son, and uncle.

Figure 5: Proportions of time each age range is mentioned.

Figure 6: Proportions of time male figures are mentioned.

##  4 Results

###  4.1 Topic Modelling

Global word frequencies from a discussion give a broad overview of the
discourse. While there is something to be gained from these word frequencies
alone, they often struggle to capture the full context of the discussion they
are involved in. Topic modelling captures this context by forming communities
of words that are frequently used in the same context. We identify topics from
the prostate cancer discourse that illustrate several revealing aspects of the
discussion. These topics are manually summarised into a one- to two-word
summary of what the topic appears to represent. Table 2 shows this summary,
the corresponding figure reference number, and the density of the topic.

Network topic modelling reveals the hierarchical structure of topics discussed
in the prostate cancer data. In the supplementary materials, an interactive
application displays this structure in a radial tree, where each root node is
a word whose size is given by its empirical use, and hierarchical topic
membership is defined by connections to parent nodes [60]. This topic
structure allows for a detailed view of the discussion by breaking down the
conversation into distinct topics which cover a particular aspect of the
conversation. The following subsections will contain results relating to
select topics found through the discourse and relate them to aspects of
healthcare.

Our topic model reveals a hierarchical structure with four layers. The first
(most general) layer is a single topic further divided into two highly general
topics in the second layer. The third layer contains twenty relatively general
topics and the fourth layer 107 highly specific topics. We show six select
topics of the twenty from the third layer in Figure 7.

(a) Prognostic staging topic

(b) Prognostic scoring topic

(c) Diagnosis topic

(d) Prostate function topic

(e) Sexual function topic

(f) Prostatitis topic

Figure 7: Six topics found through topic modelling of the/r/ProstateCancer
discourse.

####  4.1.1 Prognostic Staging

Prostate screening is often encouraged for men with an increased risk for
prostate cancer. Digital rectal examinations (DRE) and prostate-specific
antigen (PSA) tests are the most common screening tests. Abnormalities in
these screenings, such as an enlarged prostate or elevated levels of PSA,
indicate a potential risk for prostate cancer. If abnormalities are present,
further diagnostic procedures are often referred. These include imaging
procedures such as magnetic resonance imaging (MRI) and ultrasound, as well as
prostate biopsy, wherein a tissue sample is removed from the prostate and
analysed to determine if cancerous cells are present. These tests are often
conducted by a urologist. We see that this specific aspect of the diagnostic
pathway (except PSA testing) is captured in the ‘prognostic staging’ topic,
shown as a word cloud in Figure 7(a), with a density of 0.051, indicating the
topic corresponds to 5.1% of the discourse.

####  4.1.2 Prognostic Scoring

Prostate biopsy detects the presence of cancerous cells. If cancerous cells
are detected, the biopsy tissue samples (cores) are analysed and given a
Gleason score to determine how far the tissue deviates from healthy prostate
tissue. If staging results are significant, further tests may be recommended
to determine the spread of cancer beyond the prostate. These tests include
bone scan, computed tomography (CT) scan, MRI, ultrasound, and positron
emission tomography (PET) scan. Discourse relating to this aspect of the
prostate cancer diagnostic process is evidenced in the ‘prognostic scoring’
topic and captured in the word cloud in Figure 7(b). The topic density for
this topic is 0.028.

####  4.1.3 Diagnosis

Discussion on the diagnosis of prostate cancer forms a topic, represented by
the word cloud in Figure 7(c). It has a density of 0.042, indicating that 4.2%
of the informative words in the discussion are drawn from this topic. The
diagnosis discussion is characterised by the notable words ‘diagnosed’ and
‘diagnosis’. This discussion appears to be often framed from the perspective
of a child of the patient, with the paternal words ‘dad’, and ‘father’ being
prominent words in the topic. Associated words ‘spread’, ‘aggressive’, and
‘stage’ indicate this topic corresponds to discussion of the stage that the
cancer has reached. Words such as ‘bones’, ‘lungs’, and ‘metastasised’ may
indicate late-term cancer that has begun to spread to other sites in the body.
As part of the diagnosis topic, we see some discussion related to the outlook
and treatment methods available, noted by the words ‘prognosis’, ‘curable’,
‘treatable’, ‘opinions’, ‘decisions’, and ‘decision’.

####  4.1.4 Prostate Function

Figure 7(d) evidences discussion on /r/ProstateCancer evolving into
conversation related to sensitive topics, such as incontinence and erectile
dysfunction (ED), two major side effects of the removal of the prostate in a
procedure known as a radial prostatectomy (RP). This topic appears with a
density of 0.050, indicating that it corresponds to 5% of the discourse. The
use of the words ‘nerve’, ‘erectile’, ‘dysfunction’, ‘ed’, ‘impotence’, and
‘ability’ can all be interpreted in the context of erectile dysfunction. There
is a presence of the words ‘lost’, ‘loss’, and ‘shrunk’, which appear to
indicate that there is a worsening in the condition, and conversely,
‘returned’, ‘return’, ‘remission’, ‘improvement’, ‘increasing’, ‘amazing’, and
‘wonderful’. We see that discussion around penile shrinkage, a result of a
loss of blood flow after prostatectomy, is captured in this topic through the
former words, as well as ‘length’.

####  4.1.5 Sexual Intercourse

Figure 7(e) shows discussion related to sexual intercourse with a density of
0.051 and appears highly related to the topic in Figure 7(d). Whereas the
topic in Figure 7(d) appears to show discussion on sexual function and the
lack thereof, the topic in Figure 7(e) appears to go into more detail about
the sexual experience of people with prostate cancer.

####  4.1.6 Prostatitis

Figure 7(f) illustrates discussion related to prostatitis symptoms,
inflammation of the prostate, commonly due to bacterial infection. These
symptoms may include urinary pain and burning, difficulty passing urine,
‘dribbling’ (leaking urine), increased frequency of urination, blood in urine,
generalised pain in the abdomen and genitals, and fever [61]. This discussion
is observed through the use of the topic words ‘prostatitis’, ‘urine’,
‘urinating’, ‘urination’, ‘peeing’, ‘flow’, ‘stream’, ‘difficulty’, ‘weak’,
‘dribble’, ‘frequent’, ‘pain’, ‘painful’, ‘discomfort’, ‘hurt’, ‘burning’, and
‘fever’.

####  4.1.7 Other Topics

Many other topics are included in the online supplementary materials as word-
clouds [62]. We found the topics primarily corresponded to five main
categories, which we manually labelled as; ‘diagnosis’, ‘treatment’,
‘symptoms’, ‘experience’, and ‘general’.

###  4.2 Topic Landscape

The topic-topic interactions tell us the likelihood of topic co-occurrence,
giving insights into the relationships between topics. As the network approach
to topic modelling is built on a bipartite document-word network, within and
between topic interactions are of probability zero. Edges in the network occur
between words and documents, and hence the communities of words that form
topics are connected only through their connections to communities of
documents. We therefore use the two-step interaction matrix to retrieve topic-
topic interactions, propagating edges from topics to documents, and back to
topics. This interaction matrix gives a matrix of probabilities for topic co-
occurrence, considering interactions within and between topics. We convert
this to a dissimilarity measure by taking the negative co-occurrence rates.
Using UMAP, we perform a dimension reduction from the topic-space to a two-
dimensional space that seeks to preserve local distances between topics. This
two-dimensional topic representation produces a map of the topics, seen in
Figure 8. We note that here we are considering topics at the lowest (most
specific) level in the topic hierarchy. We show the three most common words
for each topic, scale the words’ sizes to correspond to their relative
frequencies, and colour according to the classifications made.

Figure 8: Topic landscape founds using UMAP with dissimilarity based on the negative co-occurrence rate between topics. The top three words from each topic at the lowest level in the topic hierarchy are shown and classified. Table 2: Table showing the most prevalent word in the topic (topic identifier) and the overall usage of the topic in the /r/ProstateCancer discourse. Topic | Figure | Usage density
---|---|---
Sexual function | 7(e) | 0.051
Prognostic staging | 7(a) | 0.051
Prostate functioning | 7(d) | 0.050
Diagnosis | 7(c) | 0.042
Prostatitis | 7(f) | 0.031
Prognostic scoring | 7(b) | 0.028

###  4.3 Topic Progressions and Emotional Arc

The average positional structure of topics may reveal the general trends that
/r/ProstateCancer posts follow. We may gain insight into how these stories are
told by capturing these trends. To find this positional structure, we find
density estimates for the positions of topics throughout documents. First, we
find normalised word positions in documents as the window of the document that
the word occupies. For example, in a document consisting of two words: ‘Hello
world.’, the word ‘Hello’ occupies the range $[0,0.5)$ and the word ‘word’
occupies the range $[0.5,1]$. The words can be replaced with their topics, and
the positional densities taken as the relative frequency of the positional
occupancy of the topic (relative to the topic’s total use), say $\hat{t}$,

$p\left(x\right|\hat{t})={\displaystyle\frac{\displaystyle\sum_{d}\mathbbm{1}_{\hat{t}=t^{d}_{x}}}{\displaystyle\sum_{d}\int_{0}^{1}\mathbbm{1}_{\hat{t}=t^{d}_{x}}dx}},$

where $t^{d}_{x}$ is the topic of document $d$ at position $x$, and
$\mathbbm{1}_{\hat{t}=t_{x}^{d}}$ is the indicator function,

$\displaystyle\mathbbm{1}_{\hat{t}=t_{x}^{d}}=\begin{cases}1,\quad\text{if
}\hat{t}=t_{x}^{d},\\\ 0,\quad\text{otherwise.}\end{cases}$

Figure 9 shows stacked topic-position densities for each topic found at the
second-lowest level of the topic hierarchy. The density stacks are arranged in
ascending order of median position, i.e. topics discussed more frequently
early (on average) are at the top of the stack. The lower half of Figure 9
shows the average emotional arc of the prostate cancer discourse by plotting
the average sentiment against the normalised position through document [63].
The most dominant-topic progression throughout the collective narrative is
highlighted by segmenting the arc into the topics with the highest density at
the respective positions.

Figure 9: Distribution of topic discussion throughout the text.

##  5 Discussion

###  5.1 DAPMAV Framework

The DAPMAV framework introduced in this paper provides a structured approach
for analysing patient-reported experiences found on social media. We follow
the principles of this framework to elicit patient-reported experiences from
prostate cancer discourse on the Reddit community /r/ProstateCancer.

The inductive reasoning underpinning this discussion – in which real-world
data is explored to generate ideas – is established in grounded theory.
Uncovering structure in vast qualitative data sources reduces complexity. The
uncovered structure reveals themes in the data. These themes can be codified
to identify core concepts. Storylines can be built from the core concepts,
which form the basis for model and hypothesis development. Tools in NLP allow
us to uncover structure in online collections of patient-reported experience
narratives. We demonstrate the utility of the DAPMAV framework to find and
qualitatively analyse patient-reported experience discourse from social media
discussions on prostate cancer.

###  5.2 Topic Modelling

Topic modelling distills the Reddit prostate cancer discourse into many
themes. We codify these themes into five classifications. Two of the five
classifications of discussion are related to clinical care. In particular,
topics can be classified as ‘diagnosis’, representing topics corresponding to
discussion surrounding diagnosis and healthcare processes in the typical
diagnostic pathway for prostate cancer, such as family history, screening,
exams, biopsy, and diagnosis. The second of the clinical care topic
classifications is the ‘treatment’ classification for topics related to
treatment pathways. The treatment pathway discourse is characterised by topics
around active surveillance, prostatectomy, radiation therapy, androgen
deprivation therapy, and others. The presence of these clinical care topics is
important as it offers a new perspective on patient experience across
diagnostic and disease treatment pathways. Two other classifications capture a
more patient-centric arm of discussion, with communication of symptoms being
one classification and patient experience being the other. Some examples of
the symptom-related topics are erectile dysfunction, sexual function,
incontinence, pain, and anxiety. These are topics of a highly sensitive nature
and reveal an intimate level of detail in their discussion that may be
difficult to obtain in a survey. The patient experience classification
encompasses topics such as quality of life, support, and questions. The final
classification, ‘general’, captures the remaining discussion and pertains to
topics that may occur in many contexts, such as the mention of prostate
cancer.

One area of prostate cancer symptom discussion that was identified relates to
sexual dysfunction, an unfortunate reality for many people who suffer from
prostate cancer. Those who experience sexual dysfunction due to prostate
cancer often indicate that the dysfunction negatively affects their quality of
life. However, this suffering is frequently made in silence, as sexual
dysfunction may be viewed an embarrassing, emasculating illness with a high
degree of perceived stigma [64, 65]. The intimate and unconstrained discussion
related to sexual dysfunction that can be found online offers a rare window
into the covert experiences faced by those who suffer from it. Topics seen in
Figure 7(d) and Figure 7(e) capture the rich essence of these stories with
expressive language and contribute to over $10\%$ of the total
/r/ProstateCancer discourse.

Topic modelling allows batch processing of the prostate cancer corpus and
reveals insightful characteristics of the nature of the corpus. We gain an
overview of each element of discussion and, importantly, draw comparisons
between these elements and the real world. We qualitatively summarise the
discourse by identifying topics with tangible aspects of healthcare. This
identification allows for a qualitative exploration of patient views on these
areas of healthcare and the ability to challenge our current views on them by
comparing and contrasting our prior understanding with observations made.

###  5.3 Topic Landscape

Analysis of specific topics provides specific insights related to the topics
but does not capture the general theme of conversation. To summarise the
entire discourse, we show a landscape of topics in Figure 8. This landscape
forms a map of topics whose geographical regions correspond to clusters of
topics that frequently occur together. These topics are represented with their
three most prevalent words. Topics relating to patient experience are central
to the map and shown in green. General topics are most prevalent in the top
right of the figure and are coloured black. Topics relating to symptoms are
shown in red and form a region on the left side of the figure that extends
towards the top middle. The relative closeness of symptom and experience
topics illustrates the frequent interactions between these types of topics.
The treatment topic in purple is dispersed throughout the right half of the
landscape and heavily interwoven with the diagnosis topic. This diagnosis
topic is coloured yellow and extends from the bottom middle to the middle
right. As UMAP produces this dimension reduction, global structure insights
are traded off for greater visualisation of local structures.

###  5.4 Topic Progressions and Emotional Arc

The average sentiment of the prostate cancer discourse (Figure 9) exhibits a
fall-rise pattern. This pattern is one of six basic story arcs, commonly known
as the ‘man-in-a-hole’ plot. This plot follows a typical trajectory of
tragedy, followed by resolution. The fall, or tragedy, is characterised by the
diagnosis topic, leaving the sentiment to bottom out and stay negative for
most of the discourse. This bottoming-out corresponds to peaks in general
discussion of prostate cancer, followed by a peak in the discussion of
symptoms. During the rise, we see a peak in the use of topics related to
patient experience, such as quality of life. This characterisation of the
collective discourse into diagnosis, general discussion, and discussion of
symptoms, followed by discussion of experience, is not entirely surprising. It
follows a likely trajectory through healthcare. First, being diagnosed, then
learning about the condition, experiencing symptoms as a result of the
condition worsening or as side effects of treatment, and finally closing with
some discussion on their experience. However, this pathway is somewhat
incomplete, as discussion on treatment does not feature in this timeline. Note
that this is due to the discussion on treatment being relatively evenly
dispersed throughout the timeline. There are no strong peaks in the discussion
of treatment across the timeline; hence, the peaks for other topic
classifications are more prominent in comparison.

##  6 Conclusion

###  6.1 DAPMAV Framework

In this paper, we motivated the need for a generalised reproducible framework
for conducting social media analysis of healthcare discourse. To meet this
need, we propose the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV)
framework. A case study is conducted to demonstrate the utility of the
framework, where social media discussion on prostate cancer on
/r/ProstateCancer is explored. We follow a grounded theory approach to
qualitatively analyse our data in this case study. The findings of this case
study outlined demonstrate the effectiveness of the DAPMAV framework and
motivate its use for uncovering patient-reported experiences in other areas of
health. In general, grounded theory justifies the use of these tools to
explore data. Qualitative explorations such as these uncover patient reported
experiences, allowing for freedom that would be difficult to achieve in a
survey. Wider patient perspectives can be considered; there are no constraints
determining which aspects of patient experiences we find. In this approach, we
let the patients tell us what is important. It is not the ideas of the domain
expert that drive the research; it is the patient’s discussion about what is
important that drives this research. This research perspective is receptive to
forming new ideas based on what we observe and can lead to developing new
research questions that are aligned with public interest.

###  6.2 Case Study: /r/ProstateCancer

We use a network approach to topic modelling to show detailed discussion
topics in prostate cancer narratives. These topics are identified with aspects
of health and healthcare at macro and micro levels, and codified. In doing so,
we observe that /r/ProstateCancer is a rich data source for intimate
discussion of sexual dysfunction and sexual experience. This finding
demonstrates that social media discussions of prostate cancer, in particular
/r/ProstateCancer, may be useful in future research that considers sexual
dysfunction and sexual experience through the eyes of the patient.

A summary of the discourse is presented in a graphic overview that links
related topics together in space and shows which classifications of discussion
these topics belong to. This graphical overview provides an explication of the
themes of discussion. It can be considered a map of the concepts at the core
of the patients’ concerns. For this reason, this map may be of value to
healthcare practitioners – allowing them to better understand patient concerns
through patient eyes – as well as healthcare researchers, who may find that
this map generates research questions.

Emotional understanding of the narratives is found through sentiment analysis.
On a global level, the /r/ProstateCancer discourse follows a fall-rise
emotional arc. We show that the fall is characterised by discussion related to
diagnosis. In general, we find that the narratives follow a path that
corresponds with a typical clinical path; the initial discussion is focused on
the diagnostic pathway, the discussion on symptoms and treatment is more
concentrated towards the middle, and the end is characterised mainly by the
discussion on experience.

###  6.3 Limitations

While the framework does not remove the need for technical proficiency in
undertaking the analysis, it provides a structure that anyone can adopt.
Methods such as network-based topic modelling can be computationally demanding
when dealing with vast quantities of data. We propose that to overcome
computational concerns, data can be sampled. Yet, with advances in
computational power, these may become issues of the past. While social media
gives everyone a voice, socioeconomic factors influence different
demographics’ presences online. This introduces biases into the results we
find, and as such, it is important to keep these biases when drawing
conclusions from these result. However, the DAPMAV framework enables us to
serve minority demographics if they participate in specific online
communities, as applying this framework to those communities would allow for a
greater understanding of their unique issues. Geographic health system
boundaries are drawn where social media boundaries are not. We have analysed
prostate cancer data from a global source, finding a global view of prostate
cancer experience. Local health systems will face a variety of challenges.
Some of these challenges will be unique; however, many will be reflective of
what we find across the globe. By analysing data from various countries, the
DAPMAV framework would allow us to consider the differences across countries.
Topics capture a theme of discussion using a family of words; however, the use
of a word from a topic does not necessarily guarantee that the word is being
used in the context of the topic. For example, the word ‘dad’ appears in the
diagnosis topic and hence has a strong association with the discussion
surrounding diagnosis. However, the word ‘dad’ may be freely used in another
context. This potential misassociation does not mean that topics are not
useful or informative; on the contrary, they are. Topics arrive from an
optimal compression of information and hence provide a useful simplifying
representation of discussion that assists in forming an overview of the themes
in a discussion and the words used in the themes. For an overview to be
useful, it must simplify a concept. No simplification comes without some loss
of information, for there is no such thing as a free lunch. Instead, a balance
must be struck between retaining enough information for insights to be drawn
and reducing cumbersome complexity that detracts from understanding.

###  6.4 Future Research

Several areas of future research have been outlined in this paper. We
reinstate those and identify some potential other areas for future research:
apply the DAPMAV framework more broadly to other cancers or health issues to
harness patient-reported experiences, apply the DAPMAV framework to
geographically identified data to compare patient experience similarities and
differences across borders, apply the DAPMAV framework to minority groups’
data to gather insights about relevant issues. /r/ProstateCancer is home to
intimate discussion on sexual dysfunction and sexual experience, which may be
a valuable resource to other researchers working on prostate cancer or patient
experience.

## References

  * of Health and Welfare [2021] Australian Institute of Health and Welfare.  Health Expenditure Australia 2019-20, 2021.  URL https://www.aihw.gov.au/reports/health-welfare-expenditure/health-expenditure-australia-2019-20.  Accessed 2022-05-24.
  * Arah et al. [2003] Onyebuchi A Arah, Niek S Klazinga, Diana MJ Delnoij, AHA Ten Asbroek, and Thomas Custers.  Conceptual frameworks for health systems performance: a quest for effectiveness, quality, and improvement.  _International Journal for Quality in Health Care_ , 15(5):377–398, 2003.
  * Murray and Frenk [2000] Christopher JL Murray and Julio Frenk.  A framework for assessing the performance of health systems.  _Bulletin of the World Health Organization_ , 78:717–731, 2000.
  * Anhang Price et al. [2014] Rebecca Anhang Price, Marc N Elliott, Alan M Zaslavsky, Ron D Hays, William G Lehrman, Lise Rybowski, Susan Edgman-Levitan, and Paul D Cleary.  Examining the role of patient experience surveys in measuring health care quality.  _Medical Care Research and Review_ , 71(5):522–554, 2014.
  * Manary et al. [2013] Matthew P Manary, William Boulding, Richard Staelin, and Seth W Glickman.  The patient experience and health outcomes.  _The New England Journal of Medicine_ , 2013.
  * McDonald [2001] Lynn McDonald.  Florence Nightingale and the early origins of evidence-based nursing.  _Evidence-Based Nursing_ , 4(3):68–69, 2001.  ISSN 1367-6539.  doi: 10.1136/ebn.4.3.68.  URL https://ebn.bmj.com/content/4/3/68.
  * Lilford and Pronovost [2010] Richard Lilford and Peter Pronovost.  Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away.  _BMJ_ , 340, 2010.
  * Barker et al. [2012] Anna Barker, Kerrie Mengersen, and Anthony Morton.  What is the value of hospital mortality indicators, and are there ways to do better?  _Australian Health Review_ , 36(4):374–377, 2012\.
  * The National Health Information and Performance Principal Comittee [2017] The National Health Information and Performance Principal Comittee.  The Australian Health Performance Framework, 2017.  URL https://www.aihw.gov.au/getmedia/1c95574c-ac07-4126-8b7c-31eb29d9b381/OOS318_Attachment-1.pdf.aspx.  Accessed: 2022-06-29.
  * Németh [2006] Gunnar Németh.  Health related quality of life outcome instruments.  _European Spine Journal_ , 15(1):S44–S51, 2006\.
  * Organization et al. [2017] World Health Organization et al.  _Technical series on safer primary care._ World Health Organization, 2017.
  * Weldring and Smith [2013] Theresa Weldring and Sheree M.S. Smith.  Article Commentary: Patient-Reported Outcomes (PROs) and Patient-Reported Outcome Measures (PROMs).  _Health Services Insights_ , 6:HSI.S11093, 2013.  doi: 10.4137/HSI.S11093.  URL https://doi.org/10.4137/HSI.S11093.  PMID: 25114561.
  * of Australia [2019] Commonwealth of Australia.  _Royal Commission into Aged Care Quality and Safety_.  2019\.  URL agedcare.royalcommission.gov.au/publications/Pages/default.aspx.  Accessed: 2022-05-18.
  * [14] Australian Institute of Health and Welfare.  Patient experience of health care.  URL aihw.gov.au/reports/australias-health/patient-experience-of-health-care.  Accessed: 2022-05-18.
  * on Safety and in Health Care [2019] Australian Commission on Safety and Quality in Health Care.  Australian Patient Experience Question Set, 2019.  URL safetyandquality.gov.au/our-work/indicators-measurement-and-reporting/australian-hospital-patient-experience-question-set.  Accessed: 2022-05-18.
  * Fan and Yan [2010] Weimiao Fan and Zheng Yan.  Factors affecting response rates of the web survey: A systematic review.  _Computers in human behavior_ , 26(2):132–139, 2010.
  * Vicsek [2010] Lilla Vicsek.  Issues in the Analysis of Focus Groups: Generalisability, Quantifiability, Treatment of Context and Quotations.  _Qualitative Report_ , 15(1):122–141, 2010.
  * Masadeh [2012] Mousa A Masadeh.  Focus group: Reviews and practices.  _International Journal of Applied Science and Technology_ , 2(10), 2012.
  * Fernández-Luque Luis [2015] Bau Teresa Fernández-Luque Luis.  Health and Social Media: Perfect Storm of Information.  _hir_ , 21(2):67–73, 2015.  doi: 10.4258/hir.2015.21.2.67.  URL http://www.e-sciencecentral.org/articles/?scid=1075777.
  * Rainie et al. [2012] Lee Rainie, Aaron Smith, Kay Lehman Schlozman, Henry Brady, Sidney Verba, et al.  Social media and political engagement.  _Pew Internet & American Life Project_, 19(1):2–13, 2012.
  * Bowen et al. [2017] Glenn A Bowen, Nickesia S Gordon, and Margaret K Chojnacki.  Advocacy through social media: Exploring student engagement in addressing social issues.  _Bowen, GA, Gordon, NS, & Chojnacki, MK (2017). Advocacy Through Social Media: Exploring Student Engagement in Addressing Social Issues. Journal of Higher Education Outreach and Engagement_, 21(3):5–30, 2017.
  * Zhou et al. [2018] Lina Zhou, Dongsong Zhang, Christopher C. Yang, and Yu Wang.  Harnessing social media for health information management.  _Electronic Commerce Research and Applications_ , 27:139–151, 2018.  ISSN 1567-4223.  doi: https://doi.org/10.1016/j.elerap.2017.12.003.  URL https://www.sciencedirect.com/science/article/pii/S1567422317300960.
  * Pappa and Stergioulas [2019] Dimitra Pappa and Lampros K Stergioulas.  Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions.  _International Journal of Data Science and Analytics_ , 8(2):113–135, 2019.
  * Greaves et al. [2013] Felix Greaves, Daniel Ramirez-Cano, Christopher Millett, Ara Darzi, and Liam Donaldson.  Harnessing the cloud of patient experience: using social media to detect poor quality healthcare.  _BMJ quality & safety_, 22(3):251–255, 2013.
  * Anstead and O’Loughlin [2015] Nick Anstead and Ben O’Loughlin.  Social media analysis and public opinion: The 2010 UK general election.  _Journal of computer-mediated communication_ , 20(2):204–220, 2015.
  * Dodds et al. [2011] Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, and Christopher M. Danforth.  Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter, 2011.
  * Murray et al. [2020] Curtis Murray, Lewis Mitchell, Jonathan Tuke, and Mark Mackay.  Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit.  _arXiv preprint arXiv:2005.10454_ , 2020.
  * Househ et al. [2014] Mowafa Househ, Elizabeth Borycki, and Andre Kushniruk.  Empowering patients through social media: the benefits and challenges.  _Health Informatics Journal_ , 20(1):50–58, 2014\.
  * Joinson and Paine [2007] Adam N Joinson and Carina B Paine.  Self-disclosure, privacy and the Internet.  _The Oxford handbook of Internet psychology_ , 2374252, 2007.
  * A. Rahim et al. [2021] Afiq Izzudin A. Rahim, Mohd Ismail Ibrahim, Kamarul Imran Musa, Sook-Ling Chua, and Najib Majdi Yaacob.  Assessing patient-perceived hospital service quality and sentiment in malaysian public hospitals using machine learning and facebook reviews.  _International Journal of Environmental Research and Public Health_ , 18(18):9912, 2021.
  * Okon et al. [2020] Edidiong Okon, Vishnutheja Rachakonda, Hyo Jung Hong, Chris Callison-Burch, and Jules B Lipoff.  Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics.  _Journal of the American Academy of Dermatology_ , 83(3):803–808, 2020.
  * Du et al. [2020] Chris Du, Wai Lee, Dena Moskowitz, Alvaro Lucioni, Kathleen C Kobashi, and Una J Lee.  I leaked, then I Reddit: experiences and insight shared on urinary incontinence by Reddit users.  _International Urogynecology Journal_ , 31(2):243–248, 2020.
  * Hawkins et al. [2016] Jared B Hawkins, John S Brownstein, Gaurav Tuli, Tessa Runels, Katherine Broecker, Elaine O Nsoesie, David J McIver, Ronen Rozenblum, Adam Wright, Florence T Bourgeois, et al.  Measuring patient-perceived quality of care in US hospitals using Twitter.  _BMJ quality & safety_, 25(6):404–413, 2016.
  * Bovonratwet et al. [2021] Patawut Bovonratwet, Tony S Shen, Wasif Islam, Michael P Ast, Steven B Haas, and Edwin P Su.  Natural language processing of patient-experience comments after primary Total knee arthroplasty.  _The Journal of Arthroplasty_ , 36(3):927–934, 2021.
  * Clark et al. [2018] Eric M Clark, Ted James, Chris A Jones, Amulya Alapati, Promise Ukandu, Christopher M Danforth, and Peter Sheridan Dodds.  A sentiment analysis of breast cancer treatment experiences and healthcare perceptions across twitter.  _arXiv preprint arXiv:1805.09959_ , 2018.
  * Meta Platforms [2022] Inc. Meta Platforms.  Meta reports first quarter 2022 results, 2022.  URL https://s21.q4cdn.com/399680738/files/doc_financials/2022/q1/Meta-03.31.2022-Exhibit-99.1_Final.pdf.  Accessed: 2022-06-29.
  * Inc. [2021] Reddit Inc.  Reddit by the Numbers, 2021.  URL https://www.redditinc.com/press.  Accessed: 2022-06-29.
  * Twitter [2022] Inc. Twitter.  Twitter Announces First Quarter 2022 Results, 2022.  URL https://s22.q4cdn.com/826641620/files/doc_financials/2022/q1/Final-Q1%e2%80%9922-earnings-release.pdf.  Accessed: 2022-06-29.
  * Karami et al. [2020] Amir Karami, Morgan Lundy, Frank Webb, and Yogesh K Dwivedi.  Twitter and research: a systematic literature review through text mining.  _IEEE Access_ , 8:67698–67717, 2020.
  * Medvedev et al. [2017] Alexey N Medvedev, Renaud Lambiotte, and Jean-Charles Delvenne.  The anatomy of Reddit: An overview of academic research.  In _Dynamics on and of Complex Networks_ , pages 183–204. Springer, 2017.
  * Bruns et al. [2018] Axel Bruns, Anja Bechmann, Jean Burgess, Andrew Chadwick, Lynn S Clark, William H Dutton, and M Zimmer.  Facebook shuts the gate after the horse has bolted, and hurts real research in the process.  _Internet Policy Review_ , 25, 2018.
  * Schroepfer [2018] Mike Schroepfer.  An update on our plans to restrict data access on Facebook.  _Facebook Newsroom_ , 4, 2018.
  * Walker et al. [2019] Shawn Walker, Dan Mercea, and Marco Bastos.  The disinformation landscape and the lockdown of social platforms.  _Information, Communication & Society_, 22(11):1531–1543, 2019.  doi: 10.1080/1369118X.2019.1648536.  URL doi.org/10.1080/1369118X.2019.1648536.
  * Manikonda et al. [2018] Lydia Manikonda, Ghazaleh Beigi, Huan Liu, and Subbarao Kambhampati.  Twitter for sparking a movement, reddit for sharing the moment:# metoo through the lens of social media.  _arXiv preprint arXiv:1803.08022_ , 2018.
  * Tanwar et al. [2015] Mona Tanwar, Reena Duggal, and Sunil Kumar Khatri.  Unravelling unstructured data: A wealth of information in big data.  In _2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions)_ , pages 1–6. IEEE, 2015.
  * Blei et al. [2003] David M Blei, Andrew Y Ng, and Michael I Jordan.  Latent Dirichlet Allocation.  _Journal of Machine Learning Research_ , 3(Jan):993–1022, 2003.
  * of Health and Welfare [2013] Australian Institute of Health and Welfare.  Prostate cancer in Australia. cancer series No. 79, 2013.
  * Baumgartner et al. [2020] Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn.  The pushshift Reddit dataset.  In _Proceedings of the International AAAI Conference on Web and Social Media_ , volume 14, pages 830–839, 2020.
  * García et al. [2015] Salvador García, Julián Luengo, and Francisco Herrera.  _Data preprocessing in data mining_ , volume 72.  Springer, 2015.
  * Silge and Robinson [2017] Julia Silge and David Robinson.  _Text mining with R: A tidy approach_.  " O’Reilly Media, Inc.", 2017.
  * Gerlach et al. [2018] Martin Gerlach, Tiago P Peixoto, and Eduardo G Altmann.  A network approach to topic models.  _Science Advances_ , 4(7):eaaq1360, 2018.
  * Wang et al. [2018] Wenxin Wang, Yi Feng, and Wenqiang Dai.  Topic analysis of online reviews for two competitive products using latent Dirichlet allocation.  _Electronic Commerce Research and Applications_ , 29:142–156, 2018.  ISSN 1567-4223.  doi: https://doi.org/10.1016/j.elerap.2018.04.003.  URL https://www.sciencedirect.com/science/article/pii/S1567422318300383.
  * Nguyen et al. [2015] Dat Quoc Nguyen, Richard Billingsley, Lan Du, and Mark Johnson.  Improving topic models with latent feature word representations.  _Transactions of the Association for Computational Linguistics_ , 3:299–313, 2015.
  * Piantadosi [2014] Steven T Piantadosi.  Zipf’s word frequency law in natural language: A critical review and future directions.  _Psychonomic bulletin & review_, 21(5):1112–1130, 2014.
  * Peixoto [2014] Tiago P Peixoto.  Hierarchical block structures and high-resolution model selection in large networks.  _Physical Review X_ , 4(1):011047, 2014.
  * Peixoto [2017] Tiago P Peixoto.  Nonparametric Bayesian inference of the microcanonical stochastic block model.  _Physical Review E_ , 95(1):012317, 2017.
  * Cox and Cox [2008] Michael AA Cox and Trevor F Cox.  Multidimensional scaling.  In _Handbook of data visualization_ , pages 315–347. Springer, 2008\.
  * Van der Maaten and Hinton [2008] Laurens Van der Maaten and Geoffrey Hinton.  Visualizing data using t-SNE.  _Journal of Machine Learning Research_ , 9(11), 2008.
  * McInnes et al. [2018] Leland McInnes, John Healy, and James Melville.  UMAP: Uniform manifold approximation and projection for dimension reduction.  _arXiv preprint arXiv:1802.03426_ , 2018.
  * Murray [2022a] Curtis Murray.  Prostate discourse topic network, 2022a.  URL curtismurray.tech/apps/topic_network.html.  Accessed 18 May 2022.
  * Litwin [2002] Mark S Litwin.  A review of the development and validation of the National Institutes of Health Chronic Prostatitis Symptom Index.  _Urology_ , 60(6):14–18, 2002.
  * Murray [2022b] Curtis Murray.  DAPMAV Online Supplementary Materials, 2022b.  URL https://github.com/curtis-murray/DAPMAV.  Accessed 01 Oct 2022.
  * Reagan et al. [2016] Andrew J Reagan, Lewis Mitchell, Dilan Kiley, Christopher M Danforth, and Peter Sheridan Dodds.  The emotional arcs of stories are dominated by six basic shapes.  _EPJ Data Science_ , 5(1):1–12, 2016.
  * Fergus et al. [2002] Karen D Fergus, Ross E Gray, and Margaret I Fitch.  Sexual dysfunction and the preservation of manhood: Experiences of men with prostate cancer.  _Journal of Health Psychology_ , 7(3):303–316, 2002.
  * Ettridge et al. [2018] KA Ettridge, Jacqueline A Bowden, Suzanne K Chambers, David P Smith, Michael Murphy, Susan M Evans, David Roder, and Caroline L Miller.  “Prostate cancer is far more hidden…”: Perceptions of stigma, social isolation and help-seeking among men with prostate cancer.  _European Journal of Cancer Care_ , 27(2):e12790, 2018.

##  Appendix A Supplementary Materials

Table 4 shows the Australian Hospital Patient Experience Question Set. Table 3
shows the Google Scholar search terms from search keys seen in Table 1.

Table 3: Key for Table 1 showing the exact Google Scholar search term for each Search Key. The term [social media platform] is a placeholder for the social media platform names; Facebook, Reddit, and Twitter. Search Key |  Google Scholar Search Term
---|---
- |  [social media platform]
Patient Experience |  [social media platform] AND ‘patient experience’ OR ‘patient experiences’ OR ‘patient reported experience’ OR ‘patient reported experiences’
Natural Language Processing |  [social media platform] AND ‘natural language processing’ OR ‘nlp’
Patient Experience + Natural Language Processing |  [social media platform] AND ‘patient experience’ OR ‘patient experiences’ OR ‘patient reported experience’ OR ‘patient reported experiences’ AND ‘natural language processing’ OR ‘nlp’
Table 4: Australian Hospital Patient Experience Question Set (AHPEQS), a core set of satisfaction questions that hospitals can use to capture patient-reported experiences [15]. Survey Questions | Response options
---|---
My views and concerns were listened to |  Always Mostly Sometimes Rarely Never Didn’t apply
My individual needs were met (if answer always/mostly, skip to Q4) |  Always Mostly Sometimes Rarely Never
When a need could not be met, staff explained why |  Always Mostly Sometimes Rarely Never
I felt cared for |  Always Mostly Sometimes Rarely Never
I was involved as much as I wanted in making decisions about my treatment and care |  Always Mostly Sometimes Rarely Never
I was kept informed as much as I wanted about my treatment and care |  Always Mostly Sometimes Rarely Never
As far as I could tell, the staff involved in my care communicated with each other about my treatment |  Always Mostly Sometimes Rarely Never Didn’t apply
I received pain relief that met my needs |  Always Mostly Sometimes Rarely Never Didn’t apply
When I was in the hospital, I felt confident in the safety of my treatment and care |  Always Mostly Sometimes Rarely Never
I experienced unexpected harm or distress as a result of my treatment or care ([if answer is no, skip to Q12) |  Yes, physical harm Yes, emotional distress Yes, both No
My harm or distress was discussed with me by staff |  Yes No Not sure Didn’t want to discuss it
Overall, the quality of the treatment and care I received was: |  Very good Good Average Poor Very poor