Examining hurricane-related social media topics longitudinally and at scale: A transformer-based approach.

Instead of turning to emergency phone systems, social media platforms, such as Twitter, have emerged as alternative and sometimes preferred venues for members of the public in the US to communicate during hurricanes and other natural disasters. However, relevant posts are likely to be missed by responders given the volume of content on platforms. Previous work successfully identified relevant posts through machine-learned methods, but depended on human annotators.

Problematic social media use and vaping among Mexican-American college students.

Introduction: Social media use and vaping nicotine are highly prevalent in the daily lives of young adults, especially among Mexican-American college students. The excessive and compulsive use of social media platforms, coupled with the urge to stay continuously connected, can lead to problematic social media use. To date, no studies have explored the impact of problematic social media use on the daily patterns of vaping among this vulnerable population.

The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study.

Background: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences.

Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach.

Introduction: The use of hashtags is a common way to promote e-cigarette content on social media. Analysis of hashtags may provide insight into e-cigarette promotion on social media. However, the examination of text data is complicated by the voluminous amount of social media data.