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. 2017 May 3;3(2):e24.
doi: 10.2196/publichealth.6396.

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

Nestor Alvaro  1   2 , Yusuke Miyao  1   2 , Nigel Collier  3
Affiliations

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

Nestor Alvaro et al. JMIR Public Health Surveill. .

Abstract

Background: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.

Objective: This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed.

Methods: We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines.

Results: The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes.

Conclusions: We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP.

Keywords: PubMed; Twitter; annotation; corpus; natural language processing; pharmacovigilance; text mining.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Annotation pipeline. The initial number of raw sentences differed between twitter (165,489 tweets) and PubMed (29,435 sentences).
Figure 2
Figure 2
Sample with the annotation of a drug, a disease and the relation between these concepts in a sentence from Twitter.
Figure 3
Figure 3
Sample of an annotation where “duration” and “exemplification” attributes are used.
See this image and copyright information in PMC

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