Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

• URL: http://arxiv.org/abs/2211.02789v1
• Date: Sat, 5 Nov 2022 00:09:45 GMT
• Title: Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
• Authors: Amogh Subbakrishna Adishesha, Lily Jakielaszek, Fariha Azhar, Peixuan Zhang, Vasant Honavar, Fenglong Ma, Chandra Belani, Prasenjit Mitra, Sharon Xiaolei Huang
• Abstract summary: This paper proposes an innovative approach to suggesting reliable information to participants in online communities. We pose the problem of predicting topic tags that describe the future information needs of users based on their profiles. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities.
• Score: 16.088586964818703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This paper proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on an expert curated data set which demonstrate the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).

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