Audio delivery of health information: An NLP study of information difficulty and bias in listeners

Arif Ahmed, Gondy Leroy, Han Yu Lu, David Kauchak, Jeff Stone, Philip I Harber, Stephen A. Rains, Prashant Mishra, Bhumi Chitroda

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Health literacy is the ability to understand, process, and obtain health information and make suitable decisions about health care [3]. Traditionally, text has been the main medium for delivering health information. However, virtual assistants are gaining popularity in this digital era; and people increasingly rely on audio and smart speakers for health information. We aim to identify audio/text features that contribute to the difficulty of the information delivered over audio. We are creating a health-related audio corpus. We selected text snippets and calculated seven text features. Then, we converted the text snippets to audio snippets. In a pilot study with Amazon Mechanical Turk (AMT) workers, we measured the perceived and actual difficulty of the audio using the response of multiple choice and free recall questions. We collected demographic information as well as bias about doctors' gender, task preference, and health information preference. Thirteen workers completed thirty audio snippets and related questions. We found a strong correlation between text features lexical chain, and the dependent variables, and multiple choice response, percentage of matching word, percentage of similar word, cosine similarity, and time taken (in seconds). In addition, doctors were generally perceived to be more competent than warm. How warm workers perceive male doctors correlated significantly with perceived difficulty.

Keywords

  • Audio information delivery
  • Health information
  • Health literacy
  • Natural Language Processing
  • NLP
  • Text features

ASJC Scopus subject areas

  • General Computer Science

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