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International Conference on Applications of Natural Language to Information Systems

NLDB 2018: Natural Language Processing and Information Systems pp 324–332Cite as

On the Semantic Similarity of Disease Mentions in \(\textsc {medline}^{\circledR } \) and Twitter

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10859)

Abstract

Social media mining is becoming an important technique to track the spread of infectious diseases and to understand specific needs of people affected by a medical condition. A common approach is to select a variety of synonyms for a disease derived from scientific literature to then retrieve social media posts for subsequent analysis. With this paper, we question the underlying assumption that user-generated text always makes use of such names, or assigns them the same meaning as in scientific literature. We analyze the most frequently used concepts in \(\textsc {medline}^{\circledR } \) for semantic similarity to Twitter use and compare their normalized entropy and cosine similarities based on a simple distributional model. We find that diseases are referred to in semantically different ways in both corpora, a difference that increases in inverse proportion to the frequency of the synonym, and of the commonness of the disease or condition. These results imply that, when sampling social media for disease-related micro-blogs, query expressions must be carefully chosen, and even more so for rarily mentioned diseases or conditions.

Keywords

  • Social media mining
  • Twitter
  • \(\textsc {medline}^{\circledR } \)
  • Disease names

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Notes

  1. 1.

    https://www.nlm.nih.gov/bsd/pmresources.html.

  2. 2.

    https://www.nlm.nih.gov/mesh/intro_trees.html.

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Acknowledgments

This work was supported by a grant from the Ministry of Science, Research and Arts of Baden-Württemberg to Roman Klinger.

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Authors and Affiliations

  1. Institut für Maschinelle Sprachverarbeitung (IMS), University of Stuttgart, Stuttgart, Germany

    Camilo Thorne & Roman Klinger

Authors
  1. Camilo Thorne
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  2. Roman Klinger
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Corresponding author

Correspondence to Camilo Thorne .

Editor information

Editors and Affiliations

  1. Université de Franche-Comté, Besançon, France

    Max Silberztein

  2. Conservatoire National des Arts et Métiers, Paris, France

    Faten Atigui

  3. Conservatoire National des Arts et Métiers, Paris, France

    Elena Kornyshova

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  5. University of Salford, Manchester, United Kingdom

    Farid Meziane

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Thorne, C., Klinger, R. (2018). On the Semantic Similarity of Disease Mentions in \(\textsc {medline}^{\circledR } \) and Twitter. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-91947-8_34

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