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Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis

Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis

Additionally, the Institute of Medicine suggests the federal law could save nearly a quarter of a million lives by preventing premature death associated with tobacco use [7]. Public support may have contributed to the rapid enactment of local and state Tobacco 21 policies.

Page D Dobbs, Allison Ames Boykin, Nnamdi Ezike, Aaron J Myers, Jason B Colditz, Brian A Primack

JMIR Form Res 2023;7:e50346

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

For example, the following tweet was classified as a commercial tweet: “COCO THC CBD Oil # Vape System New pod Style THC # CBD Oil System 4 empty tanks that are easy to fill and a 220ohm slim battery. Share !” Two coders were provided with online versions of the 2401 tweets for annotation using a qualitative content analysis approach. Coders were also provided with retweets, which are tweets that are in response to other users’ tweets.

Nnamdi C Ezike, Allison Ames Boykin, Page D Dobbs, Huy Mai, Brian A Primack

JMIR Infodemiology 2022;2(2):e37412

Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study

Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study

An LSTM network is a special kind of RNN capable of learning long-term dependencies [5]. Unlike standard feedforward networks, such as CNNs, LSTMs have a feedback connection. This feedback connection allows the network to not only process a single data point (ie, a word), but also entire sequences of data (ie, sentence or phrase), which make them extremely powerful in classifying sentiment of a message.

William Baker, Jason B Colditz, Page D Dobbs, Huy Mai, Shyam Visweswaran, Justin Zhan, Brian A Primack

JMIR Med Inform 2022;10(7):e33678

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets

Got a free puff bar at the gas station :) I wish I could explain to you guys how flabbergasted I am to have just met a 5 year old child 2/ a puff bar…he talked about disposable vapes for 5 minutes & rated various flavors This little boy really asked if I could buy him a puff bar and when I did, he went goat and then says I can’t get it anymore I’m sorry Why do I gotta be 21 to buy myself a puff bar FDA calls for removal of fruity and disposable Puff Bar vapes devices Anyways does anyone wanna paypal me 16 dollar

Kar-Hai Chu, Tina B Hershey, Beth L Hoffman, Riley Wolynn, Jason B Colditz, Jaime E Sidani, Brian A Primack

J Med Internet Res 2022;24(3):e27894

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

As a step toward the development of a Twitter-based vaping surveillance system, we derived machine learning classifiers to automatically identify tweets that are vaping-related, are noncommercial, and express provape sentiments. Using a data set of manually annotated tweets and a larger data set of unannotated tweets, we derived and evaluated traditional machine learning and deep learning classifiers.

Shyam Visweswaran, Jason B Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B Taneja, Joel Welling, Kar-Hai Chu, Jaime E Sidani, Brian A Primack

J Med Internet Res 2020;22(8):e17478

For Better or for Worse? A Systematic Review of the Evidence on Social Media Use and Depression Among Lesbian, Gay, and Bisexual Minorities

For Better or for Worse? A Systematic Review of the Evidence on Social Media Use and Depression Among Lesbian, Gay, and Bisexual Minorities

However, studies in which gender minorities were a subpopulation included in the study LGB sample were included. Literature searches were developed and executed by a health sciences librarian (CBW) in Pub Med or MEDLINE (1946-Present), Psyc INFO, Ovid (1806-present), and Soc INDEX, EBSCOhost (1895-present).

César G G Escobar-Viera, Darren L Whitfield, Charles B Wessel, Ariel Shensa, Jaime E Sidani, Andre L Brown, Cristian J Chandler, Beth L Hoffman, Michael P Marshal, Brian A Primack

JMIR Ment Health 2018;5(3):e10496