Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals

Abstract

Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.

Assessment of a Crowdsourcing Open Call for Approaches to University Community Engagement and Strategic Planning During COVID-19

“…This finding is consistent with a study on crowdsourced narratives providing an opportunity for social listening. 30 The findings of the current study provided an early warning signal about faculty, staff, and student concerns related to reopening. These findings demonstrate how crowdsourcing can provide novel insights into community concerns that thus far have been absent from institutional responses to the pandemic.…”

Underrecognized Sleep Disorders Across Rare Diseases: Real-world Insights From a Patient and Caregiver Summit

“…Mechanisms to amplify patient voices are becoming more common (35,36,(38)(39)(40). Surveys have a longstanding history for gathering information but are often limited in scope and structure.…”

“…Social listening is another strategy for collecting RWD regarding disease burdens in an unbiased manner. Social listening is an analytics tool that similarly probes conversations by analyzing social media content for counts of speci c words and phrases (39). This approach can be especially valuable for rare disease communities who connect through online support groups from around the world.…”

“…This approach can be especially valuable for rare disease communities who connect through online support groups from around the world. Social listening con rmed that a PWS support group was discussing sleep issues more frequently than hyperphagia, a primary PWS symptom (39), leading to further inquiries into the problem in PWS. Patient voices can be critical for drug development for rare diseases, particularly in developing endpoints for clinical trials (40).…”

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

“…The researches also investigate social networks, focusing on analyzing public opinion and emotions on the COVID pandemics in Twitter posts [ 56 , 57 ], monitoring illicit sales of COVID medication on Twitter and Instagram [ 58 ], and observing COVID symptoms and disease histories collected from a large population in Reddit, which may provide more reliable insights [ 59 ]. Another important issue is that, in the current situation, the surveillance of emerging epidemiological events becomes again very important, as around 60% of all outbreaks are detected using informal sources, which motivated online epidemiological surveillance [ 32 ].…”