Abstract
Medical social-media data are written for other purposes than clinical texts and biomedical literature, even though authors can be healthcare professionals, clinical researchers as well as non-health professionals. Thus, the literary style of medical social media data is markedly different. Whereas the linguistic characteristics of clinical and biomedical texts have been analysed in painstaking detail by other researchers [54, 57, >58] (see Sect. 5.2), the literary composition of medical social media data has not yet been analysed with the same degree of precision. Being aware of the linguistic peculiarities of that particular data is crucial when developing tools for language processing and data analysis. In this section, we will have a closer look at the characteristics of the language of medical social media data as well as to the content in comparison to clinical narratives.
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References
Kevin Pho. Kevinmd. http://www.kevinmd.com/blog/, last accessed: 14.03.2015.
Diane J. Standiford. A stellarlife. http://dj-astellarlife.blogspot.de, last accessed: 14.03.2015.
Randy Pausch. Randy pausch’s update page. http://www.cs.cmu.edu/~pausch/news/index.html, last accessed: 14.03.2015.
WebMD. Webmd website. http://www.webmd.com, last accessed: 14.03.2015.
Carol Friedman, Pauline Kra, and Andrey Rzhetsky A. Two biomedical sublanguages: a description based on the theories of zellig harris. Journal of Biomedical Informatics, 35:222–235, 2002.
Ivor Kovic, Ileana Lulic, and Gordana Brumini. Examining the Medical Blogosphere: An Online Survey of Medical Bloggers. Journal of Medical Internet Research, 10(3), 2008.
S.M. Meystre, G.K. Savova, K.C. Kipper-Schuler, and J.F: Hurdle. Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook Med Informat, pages 128–44, 2008.
Marcus Mitchell. Penn tree pos tagger. http://www.cis.upenn.edu/~treebank/, last accessed: 14.03.2015.
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pages 347–354, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.
Anthony Fader, Stephen Soderland, and Oren Etzioni. Identifying relations for open information extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’11, pages 1535–1545, Stroudsburg, PA, USA, 2011. Association for Computational Linguistics.
X. Zhou, X. Zhang, and X. Hu. Dragon toolkit: Incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), October 29–31, 2007, Patras, Greece, 2007.
Kerstin Denecke and Yihan Deng. Sentiment analysis in medical settings - new opportunities and challenges. Artificial Intelligence in Medicine, 2015.
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Denecke, K. (2015). Content and Language in Medical Social Media. In: Health Web Science. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-20582-3_6
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DOI: https://doi.org/10.1007/978-3-319-20582-3_6
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