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    Area of Science:

    • Health Informatics
    • Social Computing
    • Information Science

    Background:

    • Online health communities are vital but risk spreading misinformation.
    • Patients and caregivers increasingly rely on these platforms for health information.
    • Inaccurate health advice can lead to serious patient harm.

    Purpose of the Study:

    • To propose an innovative approach for recommending reliable health information to online community users.
    • To accurately predict users' future information needs based on their disease progression and online interactions.
    • To tailor information suggestions to users as they navigate different stages of their disease or treatment.

    Main Methods:

    • Developed a collaborative filtering approach tailored for online health communities.
    • Utilized user profiles and interaction traces (posts, replies) to predict information needs.
    • Compared the proposed method against baseline approaches using two unique datasets.

    Main Results:

    • The proposed approach demonstrated superior performance in predicting topic tags.
    • Accurate and timely prediction of user information needs was achieved.
    • Effectively identified relevant information sources for users in online health communities.

    Conclusions:

    • The developed system effectively addresses the challenge of health misinformation in online communities.
    • Predicting information needs based on user history and community interactions is a viable strategy.
    • This approach offers a promising solution for connecting users with reliable, tailored health information.