Abstract
Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil.Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results.Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts.Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.
Highlights
Increasing numbers of people use social media and other online spaces as either a first or second line health information [1] and exchange resource [2, 3] with estimates suggesting the volume of online health related data will have grown by 300% between 2017 and 2020 [4]
Natural language processing (NLP) refers to the use of computational techniques and algorithms that aim to interpret the semantic meaning from large volumes of unstructured text [26]
From the sites we identified posts made between 1st Jan 2002 and 17th Jan 2017, and searched for individual posts about Modafinil using the site search engine
Summary
Increasing numbers of people use social media and other online spaces as either a first or second line health information [1] and exchange resource [2, 3] with estimates suggesting the volume of online health related data will have grown by 300% between 2017 and 2020 [4]. This unstructured freeform textual data contains a mass of contextually grounded detail about the perceptions and health concerns of those who post online. A rapidly developing area [27], it is being used to explore health related social media usage [28,29,30,31,32], detecting drug or device related adverse events from user generated content [33, 34], generating new understanding about treatment switching and adherence behavior [35, 36] and as a surveillance tool for infectious disease outbreaks [37, 38] and suicide risk [12] little work has been carried out into its use for assessing effectiveness [35]
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