Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.

1. Unlocking
Hidden Insights
for Pharma with
Social Media
Listening
Swadhin Khawas
Global Head of Solutions,
Healthcare and Life Sciences
Pranjal Chaturvedi
Subject Matter Expert, Life Sciences

2. 1
How Social Media Analytics Is Transforming Pharma Decision Making (forbes.com)
Personalization is becoming central to
the healthcare and life sciences industry.
Both Healthcare Providers (HCP) and
patients are seeking tailored information
from various sources1
to make
well-informed decisions about treatment
plans. This shift means that pharma
manufacturers must analyze not only sales
and marketing data but also information
from external sources such as patient
advocacy groups and social media.
A well-crafted social media strategy can
be pivotal in adapting to the changing
trends in medical consumerism and
patient-centricity – directly affecting
brand perception and sales. This includes
social media listening, a powerful
method for understanding consumer
behavior by analyzing social posts of
physicians, patients and caregivers on
different channels.
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3. Social media listening is evolving, driven by rapid technological advancements, the proliferation of new
platforms and changing customer sentiments. In this dynamic ecosystem, three key practices are enabling
more targeted social listening.
At the height of the COVID-19 pandemic, social media listening proved invaluable for tracking real-world
data and outcomes reported by consumers and patients. Accurately analyzing this data allowed many
organizations to strategically invest in developing the right assets.
Harnessing social media data can help pharma companies gain
valuable business insights aligned with their strategic business
objectives in clinical R&D, sales, marketing and other areas.
However, data from open sources can be complex, with multiple
interpretations and hidden themes. For instance, the text produced
by social listening tools may comprise many elements (such as
patient / HCP profile, phenotype and details of the disease and
treatment) that can shape the context in different ways.
To overcome this challenge, the topic modeling method1
, which
leverages neural network models and unsupervised learning, is
essential for extracting meaningful insights from social data.
While exploring social listening trends and challenges, the paper
demonstrates how topic modeling can uncover hidden semantics,
trends and projections by analyzing structured and unstructured data.
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Key Trends Driving Effective
Social Media Listening
Leveraging Social
Media Data for
Business Intelligence
Empowering Systems
with AI and ML
Artificial Intelligence (AI) and Machine
Learning (ML) techniques make it
easier to process and analyze large
data volumes from multiple sources.
Automated topic-mining techniques
help identify topics, relationships and
trends across various records,
documents and periods.
Integrating Sales and
Marketing Strategies
with CRM systems
The seamless integration of
various marketing, competitive
intelligence and business
intelligence tools gives
enterprises a 360-degree view
of customer preferences across
geographies and demographics.
Leveraging Sentiment
Analysis
Combining sentiment analysis
with social media listening
enables pharma companies to
track consumer sentiments
toward rival offerings.
1 2 3
1
How Social Media Analytics Is Transforming Pharma Decision Making (forbes.com)

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2
5 Challenges and Solutions to Improve Social Media Monitoring Strategy - The Crowdfire blog (crowdfireapp.com)
With an increasing digital footprint, social media is
important in evaluating new disease areas, patient
requirements, HCP preferences and brand
performance. However, data from such open
sources comes with a recognized set of challenges.
The Challenges of
Open Source Data2
3. Fake Data and Inactive Followers
For strategic growth, pharma companies need reliable
insights extracted from authentic data. With vanity
metrics increasing rapidly, ensuring a pool of credible data
while analyzing social media posts can be challenging.
Data Privacy
Although most social media data can be safely
obtained and used from open and controlled
sources, there is a risk of inadvertently sharing
sensitive information, such as Intellectual Property
(IP) and personal identifiers.
The collection and analysis of such data are governed
by strict regulations meant to safeguard proprietary
information, ensure patient safety and protect
consumer data privacy. While leveraging social media,
pharma companies must devise the right controls to
ensure compliance with data privacy policies.
1. Large Volumes and
Unstructured Data
The influx of unstructured data from various
platforms, sources and self-help communities
makes it challenging and time-consuming to
identify relevant conversations and aid business
decisions. However, topic modeling and
sequential techniques can organize this data
into relevant topics for better understanding.
4. No Quick Results
It generally takes six to nine months to extract meaningful results
from a vast amount of unstructured data collected from varied
sources. Therefore, patience and strong investments are required to
plan this strategy for the long-term.
Some of these challenges can be mitigated using the right strategy.
The following section outlines how we leveraged social media data
with the right model and methodology to deliver the right outcomes.

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How can we apply topic modeling to
automatically extract insights from published
medical texts and social media data? To answer
this question, we downloaded publicly available
abstracts from PubMed on a respiratory disorder.
We then combined these with data collected from
social media groups to identify specific topics and
how they trend over time.
We chose the Bi-directional Encoder Representations
from Transformers (BERT)-based topic modeling
owing to its wide adoption and demonstrated
success. BERT is one of the most popular models
for handling sequential inputs with numerous
variations. It uses three embeddings – token
embeddings, segment embeddings and position
embeddings – to compute input representations.
BERT is pre-trained on two unsupervised Natural
Language Processing (NLP) tasks, namely
Masked Language Modeling (MLM) and Next
Sentence Prediction (NSP)3
. It can be fine-tuned
by adding layers to create state-of-the-art models
for various NLP-specific tasks, such as Named
Entity Recognition (NER) classification or
question answering.
We used the sequential technique to automatically
identify topics in the disease dataset. We ran entity
recognition models to classify abstracts into diseases,
treatments and organisms related to the disease area.
We also identified trends for these entities over time.
Use Case: Topic Modeling and a
Pre-trained, State-of-the-Art Model
For experimentation, we used a dataset of
approximately 9,000 text documents on Chronic
Obstructive Pulmonary Disease (COPD). We
extracted stop words (i.e., the most commonly
used words) and bigrams to understand word
association and removed all invalid records. Topics
were then identified using sentence transformers,
which generated word embeddings. We also
reduced the dimensionality of these embeddings.
Documents with similar topics were clustered
together to identify relevant topics.
We defined the importance of a word using
c-TF-IDF (class – Term Frequency – Inverse
Document Frequency) – allowing us to identify
key phrases contained in the topics. For example,
words such as exercise, respiratory, capacity
and muscle, and phrases such as plasma
inflammatory biomarkers, air pollution exposure
and cytometry-guided pharmacotherapy were
picked up.
The results were plotted using different charting
techniques. The entity plots helped identify trends
by topic and how each progressed or receded.
With these results, emerging entities and topics
could be determined using percentage change in
the normalized count.
We were thus able to identify relevant topics
and important named entities. These were
cross-validated and accepted by functional experts.
3
A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez, Ł Kaiser and I Polosukhin, “Attention Is All You Need,”
Advances in Neural Information Processing Systems, Vol. 30, 2017

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With this method, pharma companies can track quarterly trends for topics of interest and obtain more
granular insights. Hidden trends and meaningful insights can be drawn from bibliographical and historical
news articles and other textual repositories. Furthermore, the analysis can be drilled down to enable
comparisons based on temporal and subjective information. The percentage change can also be plotted to
capture and visualize changes in topics or named entities.
This approach can be customized for different data sources, such as social media data, medical data and
news articles. What sets this model apart is that it is scalable and built on Databricks. Any ad-hoc analysis can
be defined and easily incorporated into the existing system.
Figure 1: Correlation graph depicting relationships between identified topics
Figure 2: Trends of identified topics for the COPD dataset
index
value
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
20
40
60
80
100
120
variable
Physical Training and
Rehabilitation
Disease Diagnosis and
Prevalence
Hospital Care and Cost
Disease State
COPD Study
Treatment and Therapy
Patient Care
Tests
COPD Genetics
arterial
stiffness
term oxygen
therapy
COPD
Tests
Patient Care
Disease State
COPD Study
Disease Diagnosis
and Prevalence
Physical Training
and Rehabilitation
Treatment
and Therapy
COPD Genetics
Hospital Care
and Cost
three cuts method
81mkrypton
and 99mtc
cytometry guided
pharmacotherapy
influenza virus
infections
type diabetes
mellitus
treatment of copd
lung cancer
comorbidities
of copd
induces cardiac
troponin
dose combinations
approved
B2 agonist
corticosteroid
airway remodeling
randomized
controlled trials
discovered
and treated
spirometry
test result
exacerbation of copd
immune infiltration
analysis
coronary artery
by pass
air pollution
exposure
sphingolipids
in copd
cigarette smoke
inhalation
unstoppable
pathological event
red cell deformability
early onset
desaturation
oxygen therapy
“ciliopd cliopathy”.
quantitative ct
densitometry
positive pressure
ventilation
in stable
patients
living with
oxygen
treatable traits
of endothelial
dysfunction
deficient
hedgehog
signalling
clinical
perspectives

7. In conclusion, unsupervised topic modeling can be pivotal in unearthing topics, identifying named entities
and tracking changes over time. Notably, the system can be customized, allowing for refined insights based
on the case at hand.
Learn more on how we can leverage social media listening to help your pharmaceutical teams craft
the right brand strategy at Life Sciences | Pharma | WNS.
Harnessing the power of social media data can be a bit like navigating uncharted waters – it is tricky, nuanced
and overwhelming. However, it is an important tool for pharma companies to stay relevant as more millennials
and Gen Z become their customers. To set sail effectively, the firms need a course drawn up by domain experts.
The Way Forward for Pharma Companies
1. Set a Clear Goal
Start the journey by setting the goal and
precise, easily measurable metrics.
All campaigns must be linked to the defined
goals, with a clear path to potential earnings.
Leveraging available social media monitoring
tools, the set goals should be diligently tracked
to get the desired output. The outcome should
be helpful from the ROI, brand health and
brand reputation perspectives.
3. Leverage Surveys and Polls
Rather than traversing the social media
landscape, actively engage with the customers.
Use polls and surveys to get direct customer
feedback. Additionally, set up a dedicated
social media team to proactively address
customer needs. This active monitoring
helps acquire data appropriate
for sentiment analysis and brand
perception management.
2. Identify the Right Audience
Next, utilize various social media analytics
tools, including advanced listening tools,
to help determine the right demographic,
considering needs, age, location, gender
and product usage.
4. Capitalize on
Specialized Expertise
To truly excel, consider deploying
a semi-automated social media
insight generation application
that requires minimal
manual intervention.
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8. with WNS
Co-create to
outperform
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