{"id":153472,"date":"2024-05-03T09:30:27","date_gmt":"2024-05-03T13:30:27","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=153472"},"modified":"2025-05-02T15:00:45","modified_gmt":"2025-05-02T19:00:45","slug":"urmc-ctsi-engage-vapor-strategies-from-e-cigarette-social-analytics","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/urmc-ctsi-engage-vapor-strategies-from-e-cigarette-social-analytics\/","title":{"rendered":"URMC-CTSI, Engage Vapor: Strategies from E-cigarette Social Analytics"},"content":{"rendered":"\r\n<h2 class=\"wp-block-heading\">Author<\/h2>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Weihong Qi<\/li>\r\n\r\n\r\n\r\n<li>Zhaochen(Jason) Wang<\/li>\r\n\r\n\r\n\r\n<li>Jiaqi Zhu<\/li>\r\n\r\n\r\n\r\n<li>Christy Kim<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\">Sponsor<\/h2>\r\n<p><span style=\"font-weight: 400;\">Dr. Zidian Xie<\/span><\/p>\r\n<h2 class=\"wp-block-heading\"><b>Instructor<\/b><\/h2>\r\n\r\n\r\n\r\n<p><span style=\"font-weight: 400;\">Professor Ajay Anand<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Professor Cantay Caliskan<\/span><\/p>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\r\n\r\n\r\n\r\n<p><span dir=\"ltr\" role=\"presentation\"> In the digital age, Twitter has emerged as an<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">central arena for public health issues, particularly those involv-<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">ing e-cigarette use. This study examines the dynamics of user<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">involvement with e-cigarette-related tweets in order to identify<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">patterns in how information regarding vaping is consumed and<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">shared. Using a dataset spanning May 2021 to March 2023,<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">we evaluated millions of tweets using advanced data mining<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">and deep learning models like RoBERTa for text classification<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">and BERTopic for topic modeling. We discovered key tweet<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">characteristics\u2014such as tweet length, hashtag usage, and the<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">presence<\/span> <span dir=\"ltr\" role=\"presentation\">of<\/span> <span dir=\"ltr\" role=\"presentation\">health-related<\/span> <span dir=\"ltr\" role=\"presentation\">content\u2014that<\/span> <span dir=\"ltr\" role=\"presentation\">have<\/span> <span dir=\"ltr\" role=\"presentation\">a<\/span> <span dir=\"ltr\" role=\"presentation\">significant<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">impact on user interactions, as evaluated by replies, retweets,<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">and likes. Our findings show that while some aspects, such<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">as<\/span> <span dir=\"ltr\" role=\"presentation\">tweet<\/span> <span dir=\"ltr\" role=\"presentation\">length<\/span> <span dir=\"ltr\" role=\"presentation\">and<\/span> <span dir=\"ltr\" role=\"presentation\">links,<\/span> <span dir=\"ltr\" role=\"presentation\">increase<\/span> <span dir=\"ltr\" role=\"presentation\">engagement,<\/span> <span dir=\"ltr\" role=\"presentation\">the<\/span> <span dir=\"ltr\" role=\"presentation\">usage<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">of slang and specific emotional tones can decrease it. These<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">insights are vital for public health activists and politicians<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">to optimize social media strategies for more effective vaping<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">prevention<\/span> <span dir=\"ltr\" role=\"presentation\">and<\/span> <span dir=\"ltr\" role=\"presentation\">education<\/span> <span dir=\"ltr\" role=\"presentation\">campaigns,<\/span> <span dir=\"ltr\" role=\"presentation\">since<\/span> <span dir=\"ltr\" role=\"presentation\">they<\/span> <span dir=\"ltr\" role=\"presentation\">provide<\/span> <span dir=\"ltr\" role=\"presentation\">a<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">sophisticated understanding of digital communication in public<\/span><br role=\"presentation\" \/><span dir=\"ltr\" role=\"presentation\">health contexts.<\/span><\/p>\r\n<h2 class=\"wp-block-heading\">Introduction<\/h2>\r\n<p>The growth of social media has drastically changed public debate on health-related issues, with Twitter appearing as a popular place for conversations regarding e-cigarettes. These platforms not only allow the rapid broadcast of information, but they also serve important places for monitoring public perceptions and behaviors about health issues. E-cigarettes, which are especially popular among teenagers, present substantial public health risks due to aggressive marketing and conflicting messages regarding their safety. This study examines Twitter data to see how different qualities of e-cigarette-related tweets influence user engagement, giving useful insights for improving public health campaigns and policymakers. Using advanced data mining and deep learning techniques, our study identifies tweet features associated with increased interaction, with the goal of providing public health advocates with practical suggestions for improving anti-vaping efforts on social media.<\/p>\r\n<h2 class=\"wp-block-heading\">Method<\/h2>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Combine advanced data mining techniques with deep learning models<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Leverage transformer-based model &#8211; RoBERTa for classification<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Utilize topic modeling through LDA and BERTopic<\/span><\/li>\r\n<\/ul>\r\n<h2 class=\"wp-block-heading\">Data<\/h2>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-177312\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/data-process.png\" alt=\"\" width=\"1048\" height=\"495\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/data-process.png 1048w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/data-process-300x142.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/data-process-1024x484.png 1024w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/data-process-768x363.png 768w\" sizes=\"auto, (max-width: 1048px) 100vw, 1048px\" \/><\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-177012 size-full\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/overall.png\" alt=\"\" width=\"1133\" height=\"686\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/overall.png 1133w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/overall-300x182.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/overall-1024x620.png 1024w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/overall-768x465.png 768w\" sizes=\"auto, (max-width: 1133px) 100vw, 1133px\" \/><\/p>\r\n<p><b style=\"color: inherit; font-size: 2.5rem; letter-spacing: -0.046875rem;\">Features<\/b><\/p>\r\n<p><b>Manually labeled + RoBERTa fine-tuned Features<\/b><\/p>\r\n<ol>\r\n<li><span style=\"font-weight: 400;\"> question\u00a0<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> slang\/abbr\u00a0<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> first-person language\u00a0<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> health related<\/span><\/li>\r\n<\/ol>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-173202\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/3-300x157.png\" alt=\"\" width=\"403\" height=\"211\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/3-300x157.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/3.png 512w\" sizes=\"auto, (max-width: 403px) 100vw, 403px\" \/><\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-173222\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/2-300x254.jpg\" alt=\"\" width=\"300\" height=\"254\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/2-300x254.jpg 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/2.jpg 409w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\r\n<p><b>Algorithm-labeled Features<\/b><\/p>\r\n<ol>\r\n<li><span style=\"font-weight: 400;\">text length<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> hashtag<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> @mentions<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> contains link or not (most links are image or video)\u00a0 https\/t.xxxxx<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> sentiment of text\uff08sentiment score)<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> contains emoji or not<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> when it post (morning\/night =&gt; time of day, weekday or weekend =&gt; day of week)<\/span><\/li>\r\n<li><span style=\"font-weight: 400;\"> how many stopwords in text<\/span><\/li>\r\n<\/ol>\r\n<h2 class=\"wp-block-heading\"><b>Model <\/b><\/h2>\r\n<div class=\"flex-1 overflow-hidden\">\r\n<div class=\"react-scroll-to-bottom--css-wpnbb-79elbk h-full\">\r\n<div class=\"react-scroll-to-bottom--css-wpnbb-1n7m0yu\">\r\n<div class=\"flex flex-col text-sm pb-9\">\r\n<div class=\"w-full text-token-text-primary\" dir=\"auto\" data-testid=\"conversation-turn-11\" data-scroll-anchor=\"true\">\r\n<div class=\"px-4 py-2 justify-center text-base md:gap-6 m-auto\">\r\n<div class=\"flex flex-1 text-base mx-auto gap-3 juice:gap-4 juice:md:gap-6 md:px-5 lg:px-1 xl:px-5 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]\">\r\n<div class=\"relative flex w-full min-w-0 flex-col agent-turn\">\r\n<div class=\"flex-col gap-1 md:gap-3\">\r\n<div class=\"flex flex-grow flex-col max-w-full\">\r\n<div class=\"min-h-[20px] text-message flex flex-col items-start gap-3 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"86c9905d-c1ff-4b27-803c-734199364249\">\r\n<div class=\"markdown prose w-full break-words dark:prose-invert dark\">\r\n<p>To prepare our training dataset, we manually labeled 2200 tweets after conducting an initial assessment of 200 tweets, which yielded a satisfactory kappa score. This initial labeling assured us of the reliability of our annotations. Using this labeled data, we trained and fine-tuned four different RoBERTa models to apply to our entire dataset. Each of the four models displayed unique performance characteristics, tailored to handle specific aspects of the data.<\/p>\r\n<p><strong>Question:<\/strong><\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175122\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/4-300x250.png\" alt=\"\" width=\"432\" height=\"360\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/4-300x250.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/4.png 564w\" sizes=\"auto, (max-width: 432px) 100vw, 432px\" \/><\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<div class=\"mt-1 flex gap-3 empty:hidden\">\r\n<div class=\"text-gray-400 flex self-end lg:self-center items-center justify-center lg:justify-start mt-0 -ml-1 h-7 gap-[2px] visible\">\r\n<div class=\"flex\">\u00a0<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<div class=\"absolute\">\r\n<div class=\"flex w-full gap-2 items-center justify-center\">\r\n<p><span style=\"font-weight: 400;\">F1 score = 0.98<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><strong>Slang\/abbr: <img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175182\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/5-300x241.png\" alt=\"\" width=\"452\" height=\"363\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/5-300x241.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/5.png 592w\" sizes=\"auto, (max-width: 452px) 100vw, 452px\" \/><\/strong><\/p>\r\n<p><span style=\"font-weight: 400;\">F1 score = <\/span><span style=\"font-weight: 400;\">0.81<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><span style=\"font-weight: 400;\"><strong>First-person language:<\/strong><\/span><\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175192\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/6-300x271.png\" alt=\"\" width=\"482\" height=\"435\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/6-300x271.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/6.png 539w\" sizes=\"auto, (max-width: 482px) 100vw, 482px\" \/><\/p>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<p><span style=\"font-weight: 400;\">F1 score = \u00a00.92<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><strong>Health related:<\/strong><\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175292\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/7-300x270.png\" alt=\"\" width=\"470\" height=\"423\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/7-300x270.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/7.png 592w\" sizes=\"auto, (max-width: 470px) 100vw, 470px\" \/><\/p>\r\n<p><span style=\"font-weight: 400;\">F1 score = 0.86<\/span><\/p>\r\n<h2 class=\"wp-block-heading\"><b>Results<\/b><\/h2>\r\n<p><b>Number of Replies<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">The multivariate regression results show that the number of replies is negatively correlated with the number of slangs, whether the tweet is related to health-related issue, the sentiment score and whether contains an emoji. But the length of the tweet positively correlated with the number of replies.<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175452\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/8-1-300x144.png\" alt=\"\" width=\"621\" height=\"298\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/8-1-300x144.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/8-1.png 682w\" sizes=\"auto, (max-width: 621px) 100vw, 621px\" \/><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><b>Number of Retweets<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">The results show that the number of retweets is negatively correlated with the slangs, question, tweet length, hashtags, mentions, sentiment score and emoji. But it is positively correlated with whether it contains links.<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175472\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/9-300x148.png\" alt=\"\" width=\"578\" height=\"285\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/9-300x148.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/9.png 673w\" sizes=\"auto, (max-width: 578px) 100vw, 578px\" \/><\/p>\r\n<p><b>Number of Favorites<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">The number of favorites is negatively correlated with all the features.<\/span><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-175482\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/\u56fe\u72471-300x150.png\" alt=\"\" width=\"560\" height=\"280\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/\u56fe\u72471-300x150.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2024\/05\/\u56fe\u72471.png 682w\" sizes=\"auto, (max-width: 560px) 100vw, 560px\" \/><\/p>\r\n<p>&nbsp;<\/p>\r\n<p><b>Other Results<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">User engagement is also correlated with the time of day and day of the week. Further analysis indicates that evenings and weekends are associated with increased user engagement.<\/span><\/p>\r\n<h2 class=\"wp-block-heading\"><b>Acknowledgement<\/b><\/h2>\r\n<p><span style=\"font-weight: 400;\">We express our gratitude to Dr. Zidian Xie and the team at the Clinical and Translational Science Institute for their sponsorship and guidance throughout this project. We also extend our thanks to Prof. Ajay Anand and Prof. Cantay Caliskan for their valuable instructions and suggestions during the project&#8217;s development.<\/span><\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>Author Sponsor Dr. Zidian Xie Instructor Professor Ajay Anand Professor Cantay Caliskan Abstract In the digital age, Twitter has emerged as ancentral arena for public health issues, particularly those involv-ing&hellip;<\/p>\n","protected":false},"author":6242,"featured_media":178242,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[4442,2936,106,3006],"tags":[16522,19592,15832,16532,19582,6072],"coauthors":[8612],"class_list":["post-153472","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-archive","category-keywords-archive","category-dsc-archive","category-machine-learning-archive","tag-data-science","tag-gids","tag-health","tag-machine-learning","tag-nlp","tag-urmc"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>URMC-CTSI, Engage Vapor: Strategies from E-cigarette Social Analytics - Senior Design Day<\/title>\n<meta name=\"description\" content=\"Twitter has emerged as ancentral arena for public health issues, particularly those involv-ing e-cigarette use. 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