Examining the value of online health checks for medical info

Research in the International Journal of Electronic Marketing and Retailing introduces a new model aimed at assessing the credibility and relevance of online health care information. With the proliferation of online health advice, the challenge of distinguishing trustworthy sources from false information has become increasingly important for patients and their caregivers.

S. Sri Hari of the Illinois Institute of Technology in Chicago, U.S., S. Porkodi and R. Saranya of the University of Technology and Applied Sciences, and N. Vijayakumar of the Technical Administrative Training Institute in Muscat, Oman, have developed a model that uses sentiment analysis on reader comments to gauge the reliability of digital health care content.

Using content relevance analysis, word scoring using a lexicon analyzer, and classification via a maximum entropy model, the model generates what the team refers to as a veracity score, which can help users make a better-informed decision about the information they find online.

The researchers tested their model using health care content and found it to work effectively in evaluating the veracity of information. The new model could have significant implications for content marketing efforts within the health care sector, providing users with tailored recommendations while enhancing the credibility of digital health care information. The model's ability to identify and highlight trustworthy content benefits patients and caregivers as consumers.

The model's impact could affect all age groups, youngsters, the middle-aged, and an aging population. The identification of reliable health care information among the vast number of online medical and health resources is critical. The model could allow better-informed decision-making and mitigate the problems that might arise through the spread of misinformation.

Future work will expand the model's capabilities by developing tools to analyze multimedia content and incorporate additional mechanisms to identify misinformation and disinformation.