Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter

Topic: Amyloid, tau and blood-test diagnosis

Objectives Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Method Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset. Results Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability". Discussion This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed.

Key findings

• Results Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability".

Why it matters

This study analyzed Twitter posts and found that about 21% of Alzheimer's-related tweets used the disease keywords in ways that perpetuate public stigma. It addresses social attitudes and stigma, not the treatment, cure or prevention of Alzheimer's disease.

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Study details

• Effect size21.13%
• Evidence strengthLow

Read the original

• DOI: 10.1093/geronb/gbx014
• PubMed: 28329835
• Original source →
• Free full text: The Journals of Gerontology Series B →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{oscar2017-28329835,
  author  = {Oscar, N. and Fox, P. A. and Croucher, R. and Wernick, R. and Keune, J. and Hooker, K.},
  title   = {{Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter}},
  journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
  year    = {2017},
  doi     = {10.1093/geronb/gbx014},
  pmid    = {28329835},
  url     = {https://doi.org/10.1093/geronb/gbx014}
}

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