Automatic detection of health misinformation: a systematic review.
J Ambient Intell Humaniz Comput
; : 1-13, 2023 May 27.
Article
in English
| MEDLINE | ID: covidwho-20242548
ABSTRACT
The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Randomized controlled trials
/
Reviews
/
Systematic review/Meta Analysis
Topics:
Vaccines
Language:
English
Journal:
J Ambient Intell Humaniz Comput
Year:
2023
Document Type:
Article
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