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Depression detection in social media text: leveraging machine learning for effective screening

Shin et al. | Mar 25, 2025

Depression detection in social media text: leveraging machine learning for effective screening

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

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High school students show some reluctance to COVID-19 guidelines

Basit Abid et al. | Jun 25, 2024

High school students show some reluctance to COVID-19 guidelines
Image credit: The authors

COVID-19 has officially been downgraded from the status of a global health emergency, but have COVID-19 safety practices become a new way of life for students? The authors collected survey data on COVID-19-related knowledge and behaviors of high-school students in Punjab, Pakistan and Santa Clara County, California, USA, so see where high-schoolers stand on pandemic safety today.

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The presence of Wolbachia in Brood X cicadas

Hasan et al. | Oct 15, 2022

The presence of <em>Wolbachia</em> in Brood X cicadas

Here, seeking to understand a possible cause of the declining popluations of Brood X cicadas in Ohio and Indiana, the authors investigated the presence of Wolbachia, an inherited bacterial symbiont that lives in the reproductive cells of approximately 60% of insect species in these cicadas. Following their screening of one-hundred 17-year periodical cicadas, they only identified the presence of Wolbachia infection in less than 2%, suggesting that while Wolbachia can infect cicadas it appears uncommon in the Brood X cicadas they surveyed.

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