{"id":16472,"date":"2025-05-28T12:02:29","date_gmt":"2025-05-28T16:02:29","guid":{"rendered":"https:\/\/www.rochester.edu\/coe\/?p=16472"},"modified":"2025-06-04T11:44:58","modified_gmt":"2025-06-04T15:44:58","slug":"urmc-ctsi-exploring-e-cigarette-perception-among-spanish-and-english-speaking-users-on-social-media","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/coe\/urmc-ctsi-exploring-e-cigarette-perception-among-spanish-and-english-speaking-users-on-social-media\/","title":{"rendered":"URMC CTSI: Exploring E-Cigarette Perception among Spanish and English-Speaking Users on Social Media"},"content":{"rendered":"

\"Analyzing<\/p>\n

Team Members: Chen-Jui Chen, Anik De, Wonha Shin, Yadi Zhang, Chen Zhang<\/em><\/p>\n

The goal of the UR Clinical & Translational Science Institute(CTSI) project was to analyze public perceptions of e-cigarettes from English and Spanish language posts on social media, informing culturally appropriate public health interventions. Students employed natural language processing (NLP) techniques for classification tasks such as data cleaning, preprocessing, labeling, and fine-tuning transformer-based machine learning models (e.g., Bertweet and Twitter Twin BERT Large). They then applied Sentence Transformers and K-means clustering for topic modeling to analyze thematic content. The team\u2019s results revealed nuanced differences between linguistic groups. Specifically, English posts focused on public health and regulatory concerns, while Spanish posts highlighted the cultural and lifestyle aspects of vaping. Additionally, further attitude analysis revealed significant discrepancies between traditional sentiment metrics for English and Spanish speakers; translation evaluations revealed potential information losses but highlighted cultural nuances that could affect interpretations of the project\u2019s findings. The team\u2019s work underscores the need for culturally sensitive health messaging and policy approaches. It also emphasizes the value of robust multilingual analyses to inform public health strategies.<\/p>\n","protected":false},"excerpt":{"rendered":"

Team Members: Chen-Jui Chen, Anik De, Wonha Shin, Yadi Zhang, Chen Zhang The goal of the UR Clinical & Translational Science Institute(CTSI) project was to analyze public perceptions of e-cigarettes…<\/p>\n","protected":false},"author":162,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42],"tags":[52,182,172],"class_list":["post-16472","post","type-post","status-publish","format-standard","hentry","category-fall-2024-projects","tag-healthcare","tag-ur-ctsi","tag-urmc"],"yoast_head":"\nURMC CTSI: Exploring E-Cigarette Perception among Spanish and English-Speaking Users on Social Media - Center of Excellence in Data Science and Artificial Intelligence<\/title>\n<meta name=\"description\" content=\"The team analyzed English and Spanish social media posts to understand e-cigarette perceptions. They used BERT-based models and topic modeling techniques. 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