Dataset Information

Classification of Twitter Users Who Tweet About E-Cigarettes.

ABSTRACT: Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, little is known about the diversity of the types of users responsible for generating e‑cigarette-related content on Twitter.The aim of this study was to demonstrate a novel methodology for automatically classifying Twitter users who tweet about e‑cigarette-related topics into distinct categories.We collected approximately 11.5 million e‑cigarette-related tweets posted between November 2014 and October 2016 and obtained a random sample of Twitter users who tweeted about e‑cigarettes. Trained human coders examined the handles' profiles and manually categorized each as one of the following user types: individual (n=2168), vaper enthusiast (n=334), informed agency (n=622), marketer (n=752), and spammer (n=1021). Next, the Twitter metadata as well as a sample of tweets for each labeled user were gathered, and features that reflect users' metadata and tweeting behavior were analyzed. Finally, multiple machine learning algorithms were tested to identify a model with the best performance in classifying user types.Using a classification model that included metadata and features associated with tweeting behavior, we were able to predict with relatively high accuracy five different types of Twitter users that tweet about e‑cigarettes (average F1 score=83.3%). Accuracy varied by user type, with F1 scores of individuals, informed agencies, marketers, spammers, and vaper enthusiasts being 91.1%, 84.4%, 81.2%, 79.5%, and 47.1%, respectively. Vaper enthusiasts were the most challenging user type to predict accurately and were commonly misclassified as marketers. The inclusion of additional tweet-derived features that capture tweeting behavior was found to significantly improve the model performance-an overall F1 score gain of 10.6%-beyond metadata features alone.This study provides a method for classifying five different types of users who tweet about e‑cigarettes. Our model achieved high levels of classification performance for most groups, and examining the tweeting behavior was critical in improving the model performance. Results can help identify groups engaged in conversations about e‑cigarettes online to help inform public health surveillance, education, and regulatory efforts.

SUBMITTER: Kim A

PROVIDER: S-EPMC5635233 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Classification of Twitter Users Who Tweet About E-Cigarettes.

Kim Annice A Miano Thomas T Chew Robert R Eggers Matthew M Nonnemaker James J

JMIR public health and surveillance 20170926 3

<h4>Background</h4>Despite concerns about their health risks, e‑cigarettes have gained popularity in recent years. Concurrent with the recent increase in e‑cigarette use, social media sites such as Twitter have become a common platform for sharing information about e-cigarettes and to promote marketing of e‑cigarettes. Monitoring the trends in e‑cigarette-related social media activity requires timely assessment of the content of posts and the types of users generating the content. However, littl ...[more]

PMID: 28951381

Similar Datasets

Project description:BackgroundThe amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.ObjectiveThis study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP.MethodsIn summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss.ResultsThe best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively.ConclusionsThis paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.

Project description:BackgroundMenthol cigarettes are used disproportionately by African American, female, and adolescent smokers. Twitter is also used disproportionately by minority and younger populations, providing a unique window into conversations reflecting social norms, behavioral intentions, and sentiment toward menthol cigarettes.ObjectiveOur purpose was to identify the content and frequency of conversations about menthol cigarettes, including themes, populations, user smoking status, other tobacco or substances, tweet characteristics, and sentiment. We also examined differences in menthol cigarette sentiment by prevalent categories, which allowed us to assess potential perceptions, misperceptions, and social norms about menthol cigarettes on Twitter. This approach can inform communication about these products, particularly to subgroups who are at risk for menthol cigarette use.MethodsThrough a combination of human and machine classification, we identified 94,627 menthol cigarette-relevant tweets from February 1, 2012 to January 31, 2013 (1 year) from over 47 million tobacco-related messages gathered prospectively from the Twitter Firehose of all public tweets and metadata. Then, 4 human coders evaluated a random sample of 7000 tweets for categories, including sentiment toward menthol cigarettes.ResultsWe found that 47.98% (3194/6657) of tweets expressed positive sentiment, while 40.26% (2680/6657) were negative toward menthol cigarettes. The majority of tweets by likely smokers (2653/4038, 65.70%) expressed positive sentiment, while 91.2% (320/351) of nonsmokers and 71.7% (91/127) of former smokers indicated negative views. Positive views toward menthol cigarettes were predominant in tweets that discussed addiction or craving, marijuana, smoking, taste or sensation, song lyrics, and tobacco industry or marketing or tweets that were commercial in nature. Negative views toward menthol were more common in tweets about smoking cessation, health, African Americans, women, and children and adolescents-largely due to expression of negative stereotypes associated with these groups' use of menthol cigarettes.ConclusionsExaminations of public opinions toward menthol cigarettes through social media can help to inform the framing of public communication about menthol cigarettes, particularly in light of potential regulation by the European Union, US Food and Drug Administration, other jurisdictions, and localities.

Project description:ImportanceUse of electronic cigarettes (e-cigarettes) is increasing. Measures of exposure to known tobacco-related toxicants among e-cigarette users will inform potential health risks to individual product users.ObjectivesTo estimate concentrations of tobacco-related toxicants among e-cigarette users and compare these biomarker concentrations with those observed in combustible cigarette users, dual users, and never tobacco users.Design, setting, and participantsA population-based, longitudinal cohort study was conducted in the United States in 2013-2014. Cross-sectional analysis was performed between November 4, 2016, and October 5, 2017, of biomarkers of exposure to tobacco-related toxicants collected by the Population Assessment of Tobacco and Health Study. Participants included adults who provided a urine sample and data on tobacco use (N = 5105).ExposuresThe primary exposure was tobacco use, including current exclusive e-cigarette users (n = 247), current exclusive cigarette smokers (n = 2411), and users of both products (dual users) (n = 792) compared with never tobacco users (n = 1655).Main outcomes and measuresGeometric mean concentrations of 50 individual biomarkers from 5 major classes of tobacco product constituents were measured: nicotine, tobacco-specific nitrosamines (TSNAs), metals, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs).ResultsOf the 5105 participants, most were aged 35 to 54 years (weighted percentage, 38%; 95% CI, 35%-40%), women (60%; 95% CI, 59%-62%), and non-Hispanic white (61%; 95% CI, 58%-64%). Compared with exclusive e-cigarette users, never users had 19% to 81% significantly lower concentrations of biomarkers of exposure to nicotine, TSNAs, some metals (eg, cadmium and lead), and some VOCs (including acrylonitrile). Exclusive e-cigarette users showed 10% to 98% significantly lower concentrations of biomarkers of exposure, including TSNAs, PAHs, most VOCs, and nicotine, compared with exclusive cigarette smokers; concentrations were comparable for metals and 3 VOCs. Exclusive cigarette users showed 10% to 36% lower concentrations of several biomarkers than dual users. Frequency of cigarette use among dual users was positively correlated with nicotine and toxicant exposure.Conclusions and relevanceExclusive use of e-cigarettes appears to result in measurable exposure to known tobacco-related toxicants, generally at lower levels than cigarette smoking. Toxicant exposure is greatest among dual users, and frequency of combustible cigarette use is positively correlated with tobacco toxicant concentration. These findings provide evidence that using combusted tobacco cigarettes alone or in combination with e-cigarettes is associated with higher concentrations of potentially harmful tobacco constituents in comparison with using e-cigarettes alone.

Dataset Information

Classification of Twitter Users Who Tweet About E-Cigarettes.

Publications

Classification of Twitter Users Who Tweet About E-Cigarettes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets