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Leveraging Social Media Data to Understand the Impact of COVID-19 on Residents' Dietary Behaviors: Observational Study

Li et al [19] proposed an approach that predicts state-level obesity rates using social media data. Users’ obesity-related behaviors in social media were found and could be classified into 4 levels of interaction: individual, interpersonal, web-based social environment, and connection to the real world [20]. Additionally, the use of social media platforms increased by 61% during the pandemic [21].

Chuqin Li, Alexis Jordan, Yaorong Ge, Albert Park

J Med Internet Res 2025;27:e51638

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Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study

Li et al [27] used a data set comprises 65 sentence pairs that were created using the dictionary definition of 65 word pairs used in the Rubenstein-Goodenough data set [28]. A similarity range of 0 to 4 (from the lowest to the highest) was provided voluntarily by 32 native English speakers. The mean of the scores given by all the volunteers was taken as the final score.

Nidhi Saraswat, Chuqin Li, Min Jiang

JMIR AI 2023;2:e43483

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