Abstract
Since the advent of Web 2.0 and social media, anyone with an Internet connection can create content online, even if it is uncertain or fake information, which has attracted significant attention recently. In this study, we address the challenge of uncertain online health information by automating systematic approaches borrowed from evidence-based medicine. Our proposed algorithm, MedFact, enables recommendation of trusted medical information within health-related social media discussions and empowers online users to make informed decisions about the credibility of online health information. MedFact automatically extracts relevant keywords from online discussions and queries trusted medical literature with the aim of embedding related factual information into the discussion. Our retrieval model takes into account layperson terminology and hierarchy of evidence. Consequently, MedFact is a departure from current consensus-based approaches for determining credibility using “wisdom of the crowd”, binary “Like” votes and ratings, popular in social media. Moving away from subjective metrics, MedFact introduces objective metrics. We also present preliminary work towards a granular veracity score by using supervised machine learning to compare statements within uncertain social media text and trusted medical text. We evaluate our proposed algorithm on various data sets from existing health social media involving both patient and medic discussions, with promising results and suggestions for ongoing improvements and future research.
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Notes
- 1.
The GenSim Python API includes the TextRank algorithm [21] implementation
https://radimrehurek.com/gensim/summarization/keywords.html.
- 2.
SNOMED CT data set available from U.S. National Library of Medicine (NLM)
- 3.
CHV data set available from the Consumer Health Vocabulary Initiative
- 4.
SEW historical data set available via PIKES home page
- 5.
The TRIP database is accessible programmatically via web services that were most kindly made available to the authors by Jon Brassey, the TRIP database creator
- 6.
POS tagging is done using the Penn Treebank tags set, all steps in this particular pipeline are programmed with the NLTK Python library http://nltk.org.
- 7.
Sentiment analysis is performed using the TextBlob Python library
- 8.
The spaCy Python library is used for generating dependency trees https://spacy.io.
- 9.
We implement a shallow CNN with the ConText tool
- 10.
Health Stack Exchange’s beta web site https://health.stackexchange.com.
- 11.
Data set curated from the Stack Exchange Data Dump from the Internet Archive
- 12.
QuackWatch web site http://quackwatch.org.
- 13.
DocCheck web site http://doccheck.com.
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We thank the Alberta Machine Intelligence Institute (Amii) for funding this research.
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Samuel, H., Zaïane, O. (2018). MedFact: Towards Improving Veracity of Medical Information in Social Media Using Applied Machine Learning. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_9
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