Applications of quantitative social media listening to patient-centric drug development

Opinion Mining of Erowid's Experience Reports on LSD and Psilocybin-Containing Mushrooms

BACKGROUND

Psychedelics are gaining attention for their therapeutic potential in modern and personalized medicine. Online forums such as Erowid provide valuable user insights, but analyses of these experiences using natural language processing (NLP) remain scarce.

OBJECTIVE

This study aims to utilize NLP, including sentiment and lexicon analysis, to examine user-generated experience reports on psilocybin-containing mushrooms and LSD from the Erowid forum.

METHODS

Data from 2188 Erowid users (1161 psilocybin mushrooms and 1027 LSD) was collected via automated web scraping with XPath, CSS selectors, and Selenium WebDriver. The dataset included report titles, substances, and demographics. Sentiment analysis utilized BERT, RoBERTa, and VADER models. Preprocessing involved tokenization, lemmatization, part-of-speech tagging, and stop-word filtering. Lexicon analysis identified themes through recurring n-grams, visualized using Python.

RESULTS

User demographics revealed comparable ages for psilocybin mushrooms (23.8 ± 0.9 years) and LSD users (20.0 ± 0.6 years), with a predominance of male users. The BERT model predominantly labeled experiences as negative (unfavorable), particularly for mushroom users (p = 0.001). VADER indicated more positive experiences for mushroom users (p < 0.001), while RoBERTa mainly classified experiences as negative or neutral. Significant gender differences were found only with VADER, where more male users expressed positive opinions about psilocybin mushrooms (74.09% versus 65.52%, p < 0.021). The VADER model yielded more polarized results, whereas RoBERTa's cautious classifications indicate its suitability for analyzing lengthy and complex psychedelic reports. Further, RoBERTa outperformed other transformer-based models, achieving the highest accuracy. Lexicon analysis revealed emotional, sensory, and temporal themes, with psilocybin reports emphasizing introspection and time dilation phenomenon, while LSD reports highlighted memory issues and cognitive disorientation.

CONCLUSIONS

Sentiment analysis showed that VADER produced more polarized results, while RoBERTa offered cautious classifications with the highest accuracy. Lexicon analysis revealed shared themes, with mushroom reports focusing on introspection and time dilation perception, while those of LSD emphasized cognitive disturbances. This study highlights the value of these analyses in understanding psychedelic experiences, informing harm reduction, and guiding policy-making.

Social media listening study to understand the journey and unmet needs of patients living with post-stroke spasticity

PURPOSE

Stroke survivors may develop spasticity (post-stroke spasticity [PSS]) that can challenge activity and participation. Recognising the needs and expectations of people with PSS is crucial for enhancing care. This study is the first to employ social media listening to explore the experiences, unmet needs, and quality of life (QoL) of people with PSS.

MATERIALS AND METHODS

A subset of 417 patient-centric PSS-related posts published on major social media platforms was identified for analysis from 31 600 retrieved.

RESULTS

Posts mainly discussed patient journey (centred around treatment options and management techniques), the impact of PSS on QoL, and patient interactions with healthcare practitioners (HCPs). Widely used treatment options had associated negative sentiments due to perceived inefficacy and lack of long-term effectiveness (botulinum neurotoxin) or side effects (oral anti-spasticity medications). Perceptions of treatment options and expected treatment outcomes influenced satisfaction with treatment and HCP interactions. Poor perceived treatment efficacy generally resulted in dissatisfaction with HCP interactions and seeking peer opinions online. Identified unmet needs focused on need for satisfactory treatment options, well-informed HCPs, and better patient education.

CONCLUSIONS

The study highlights the need for improved education for patients, caregivers, and HCPs regarding PSS and better communication between patients and HCPs to manage treatment expectations.

Real-world insights of patient voices with age-related macular degeneration in the Republic of Korea and Taiwan: an AI-based Digital Listening study by Semantic-Natural Language Processing

BACKGROUND

In this era of active online communication, patients increasingly share their healthcare experiences, concerns, and needs across digital platforms. Leveraging these vast repositories of real-world information, Digital Listening enables the systematic collection and analysis of patient voices through advanced technologies. Semantic-NLP artificial intelligence, with its ability to process and extract meaningful insights from large volumes of unstructured online data, represents a novel approach for understanding patient perspectives. This study aimed to demonstrate the utility of Semantic-NLP technology in presenting the needs and concerns of patients with age-related macular degeneration (AMD) in Korea and Taiwan.

METHODS

Data were collected and analysed over three months from January 2023 using an ontology-based information extraction system (Semantic Hub). The system identified patient "stories" and extracted themes from online posts from January 2013 to March 2023, focusing on Korea and Taiwan by filtering the geographic location of users, the language used, and the local online platforms. Extracted texts were structured into knowledge graphs and analysed descriptively.

RESULTS

The patient voice was identified in 133,857 messages (9,620 patients) from the Naver online platform in Korea and included internet chat forums focused on macular degeneration. The most important factors for AMD treatments were effectiveness (1,632/3,401 mentions; 48%), price and access to insurance (33%), tolerability (10%) and doctor and clinic recommendations (9%). Treatment burden associated with intravitreal injection of vascular endothelial growth factor inhibitors related to tolerability (254/942 mentions; 27%), financial burden (20%), hospital selection (18%) and emotional burden (14%). In Taiwan, 444 messages were identified from Facebook, YouTube and Instagram. The success of treatment was judged by improvements in visual acuity (20/121 mentions; 16.5%), effect on oedema (10.7%), less distortion (9.1%) and inhibition of angiogenesis (5.8%). Tolerability concerns were rarely mentioned (26/440 mentions; 5.9%).

CONCLUSIONS

Digital Listening using Semantic-NLP can provide real-world insights from large amounts of internet data quickly and with low human labour cost. This allows healthcare companies to respond to the unmet needs of patients for effective and safe treatment and improved patient quality of life throughout the product lifecycle.

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis

BACKGROUND

While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times.

OBJECTIVE

Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19-related drugs.

METHODS

This study constructed a full pipeline for COVID-19-related drug tweet analysis, using pretrained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of 4 core modules: named entity recognition and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names for time trend analysis, target sentiment analysis to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to dig potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to the COVID-19 pandemic and drug therapies between February 1, 2020, and April 30, 2022.

RESULTS

From a dataset comprising 169,659,956 COVID-19-related tweets from 103,682,686 users, our named entity recognition model identified 2,124,757 relevant tweets sourced from 1,800,372 unique users, and the top 5 most-discussed drugs: ivermectin, hydroxychloroquine, remdesivir, zinc, and vitamin D. Time trend analysis revealed that the public focused mostly on repurposed drugs (ie, hydroxychloroquine and ivermectin), and least on remdesivir, the only officially approved drug among the 5. Sentiment analysis of the top 5 most-discussed drugs revealed that public perception was predominantly shaped by celebrity endorsements, media hot spots, and governmental directives rather than empirical evidence of drug efficacy. Topic analysis obtained 15 general topics of overall drug-related tweets, with "clinical treatment effects of drugs" and "physical symptoms" emerging as the most frequently discussed topics. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use.

CONCLUSIONS

This study shows that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.

Findings from a social media listening study on Chinese patients with Alzheimer's disease: a content analysis

Background

Social media listening is a new approach for gathering insights from social media platforms about users' experiences. This approach has not been applied to analyse discussions about Alzheimer's disease (AD) in China.

Aims

We aimed to leverage multisource Chinese data to gain deeper insights into the current state of the daily management of Chinese patients with AD and the burdens faced by their caregivers.

Methods

We searched nine mainstream public online platforms in China from September 2010 to March 2024. Natural language processing tools were used to identify patients and caregivers, and categorise patients by disease stage for further analysis. We analysed the current state of patient daily management, including diagnosis and treatment, choice of treatment scenarios, patient safety and caregiver concerns.

Results

A total of 1211 patients with AD (66% female, 82% aged 60-90) and 756 caregivers for patients with AD were identified from 107 556 online sources. Most patients were derived from online consultation platforms (43%), followed by bulletin board system platforms (24%). Among the patients categorised into specific disease stages (n=382), 42% were in the moderate stage. The most frequent diagnostic tools included medical history (97%) and symptoms (84%). Treatment options for patients with AD primarily included cholinesterase inhibitors, N-methyl-D-aspartate receptor antagonists and antipsychotics. Both quantitative and qualitative analysis of patients who experienced wandering (n=92) indicated a higher incidence of wandering during the moderate stage of the disease. Most caregivers were family members, with their primary concerns focusing on disease management and treatment (90%), followed by daily life care (37%) and psychosocial support (25%).

Conclusions

Online platform data provide a broad spectrum of real-world insights into individuals affected by AD in China. This study enhances our understanding of the experiences of patients with AD and their caregivers, providing guidance for developing personalised interventions, providing advice for caregivers and improving care for patients with AD.

One size fits all: Enhanced zero-shot text classification for patient listening on social media

Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have enabled the analysis of vast social media datasets, also called Social Media Listening (SML), providing insights not only into patient perspectives but also into those of other interest groups such as caregivers. In this method study, we propose an NLP framework that-given a particular disease-is designed to extract pertinent information related to three primary research topics: identification of interest groups, understanding of challenges, and assessing treatments and support systems. Leveraging external resources like ontologies and employing various NLP techniques, particularly zero-shot text classification, the presented framework yields initial meaningful insights into these research topics with minimal annotation effort.

Artificial intelligence-enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies

This article examines the opportunities and benefits of artificial intelligence (AI)-enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate the patient perspective to improve the quality, relevance, safety, and efficiency of drug development and evaluation. Gathering patient perspectives to support PFDD is aided by the participation of patient groups in communicating their treatment experiences, needs, preferences, and priorities through online platforms. SML is a method of gathering feedback directly from patients; however, distilling the quantity of data into actionable insights is challenging. AI-enabled methods, such as natural language processing (NLP), can facilitate data processing from SML studies. Herein, we describe a novel, trainable, AI-enabled, SML workflow that classifies posts made by patients or caregivers and uses NLP to provide data on their experiences. Our approach is an iterative process that balances human expert-led milestones and AI-enabled processes to support data preprocessing, patient and caregiver classification, and NLP methods to produce qualitative data. We explored the applicability of this workflow in 2 studies: 1 in patients with head and neck cancers and another in patients with esophageal cancer. Continuous refinement of AI-enabled algorithms was essential for collecting accurate and valuable results. This approach and workflow contribute to the establishment of well-defined standards of SML studies and advance the methodologic quality and rigor of researchers contributing to, conducting, and evaluating SML studies in a PFDD context.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Exploring the Perspectives of Patients Living With Lupus: Retrospective Social Listening Study

BACKGROUND

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease affecting various organs with a wide range of clinical manifestations. Cutaneous lupus erythematosus (CLE) can manifest as a feature of SLE or an independent skin ailment. Health-related quality of life (HRQoL) is frequently compromised in individuals living with lupus. Understanding patients' perspectives when living with a disease is crucial for effectively meeting their unmet needs. Social listening is a promising new method that can provide insights into the experiences of patients living with their disease (lupus) and leverage these insights to inform drug development strategies for addressing their unmet needs.

OBJECTIVE

The objective of this study is to explore the experience of patients living with SLE and CLE, including their disease and treatment experiences, HRQoL, and unmet needs, as discussed in web-based social media platforms such as blogs and forums.

METHODS

A retrospective exploratory social listening study was conducted across 13 publicly available English-language social media platforms from October 2019 to January 2022. Data were processed using natural language processing and knowledge graph tagging technology to clean, format, anonymize, and annotate them algorithmically before feeding them to Pharos, a Semalytix proprietary data visualization and analysis platform, for further analysis. Pharos was used to generate descriptive data statistics, providing insights into the magnitude of individual patient experience variables, their differences in the magnitude of variables, and the associations between algorithmically tagged variables.

RESULTS

A total of 45,554 posts from 3834 individuals who were algorithmically identified as patients with lupus were included in this study. Among them, 1925 (authoring 5636 posts) and 106 (authoring 243 posts) patients were identified as having SLE and CLE, respectively. Patients frequently mentioned various symptoms in relation to SLE and CLE including pain, fatigue, and rashes; pain and fatigue were identified as the main drivers of HRQoL impairment. The most affected aspects of HRQoL included "mobility," "cognitive capabilities," "recreation and leisure," and "sleep and rest." Existing pharmacological interventions poorly managed the most burdensome symptoms of lupus. Conversely, nonpharmacological treatments, such as exercise and meditation, were frequently associated with HRQoL improvement.

CONCLUSIONS

Patients with lupus reported a complex interplay of symptoms and HRQoL aspects that negatively influenced one another. This study demonstrates that social listening is an effective method to gather insights into patients' experiences, preferences, and unmet needs, which can be considered during the drug development process to develop effective therapies and improve disease management.

Artificial intelligence-driven approach for patient-focused drug development

Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.