Social listening: Applying natural language processing methods to social media data to yield actionable analytics for health and care services

It is vital to understand public opinion and preferences towards the use of information technology and health data as part of designing future models of healthcare and research. Social media platforms (e.g. Twitter), blogs and online discussion forums provide a rich resource of naturally occurring conversations for examining public attitudes and preferences towards (a) information and digital technologies as part of the delivery of healthcare and (b) the secondary use of health data for purposes beyond direct healthcare, such as research.

Analysing public comments and conversations can be analysed manually using established qualitative research techniques. Whilst such methods provide depth and rigour, they are typically labour intensive and can neither be applied rapidly nor on a large scale basis without significant effort and resource. I propose to investigate promising new techniques from the field of natural language processing (NLP) to rapidly and automatically analyse textual data about public attitudes and preferences towards health and care from publicly available social media data. I will compare the performance of NLP methods against established, qualitative approaches and assess how the two approaches can complement each other to gather insights into public opinion for the purposes of ongoing monitoring, research, evaluation and informing public policy.

I will test advanced methods of data visualisation to report my findings. Leveraging my networks, I will explore how to translate my work into wider applications, within Health Data Research UK, healthcare services and internationally. Throughout the project I will adhere to ethical guidelines for using social media data and will involve citizens from relevant communities (online and offline) in shaping the design and delivery of the research.

Future healthcare delivery and research relies on public acceptance of information technology and health data uses as part of service delivery. Social media platforms (e.g. Twitter), blogs and online discussion forums provide an increasingly ubiquitous, yet under exploited, source of unstructured textual data for examining public attitudes and preferences relevant to health, information technology, data uses and healthcare delivery. Yet, analysing such data manually is labour intensive and can neither be done rapidly nor at scale.
This project will investigate the accuracy of newer natural language processing (NLP) techniques for the rapid, automated extraction of public attitudes and preferences from large-scale, social media data. Exemplar datasets relevant to public attitudes towards the commercial use of health data and wearable technologies will be extracted from social media data, cleaned and prepared for analysis. NLP techniques (e.g. sentiment analysis, text mining, machine learning and/or rule-based methods) and qualitative analysis (e.g. framework analysis, discursive psychology) will then be: (a) applied to unstructured textual data in parallel; (b) benchmarked against each other; and (c) tested within an integrated mixed methods approach.
Online open source decision support tools will be developed to guide the selection and application of NLP techniques (alone and/or as part of a mixed methods approach) to unstructured social media data, addressing distinct purposes, such as longitudinal monitoring of public opinion and informing policy development. Advanced data visualisations will be developed, evaluated and optimised for data exploration, presentation and informing decision making. This will include sentiment analysis, clustering, frequency analysis, and high-dimensional representations of big text data. Findings, tools and methods will be disseminated widely, with the aim of enabling public opinion data to inform future policy development.