Smokers' Beliefs and Attitudes about Purchasing Cigarettes on the Internet

OBJECTIVES: Our objectives were to explore qualitatively how smokers find out about Internet cigarette sales and what factors motivate them to purchase cigarettes on-line, and to quantitatively describe the Internet cigarette purchasing behaviors and attitudes of Internet cigarette buyers. METHODS: Qualitative in-depth telephone interviews were conducted with 21 adult smokers who had purchased or contemplated purchasing cigarettes on-line. Findings from the qualitative study were used to develop a survey module on Internet cigarette purchasing behavior that was administered to 187 New Jersey adult smokers. RESULTS: Smokers who purchased cigarettes on-line were primarily motivated by lower prices, which occur because Internet vendors generally sell cigarettes without paying excise taxes for the destination state. Most Internet cigarette buyers first learned about on-line cigarette sales from interpersonal sources who had purchased on-line. New Jersey adult smokers who purchased cheaper cigarettes from the Internet and other lower-taxed sources significantly increased their consumption over time, compared to smokers who reported paying full-price at traditional bricks-and-mortar retail stores. CONCLUSIONS: Policies that have the effect of equalizing Internet cigarette prices with those at retail stores will likely deter smokers from purchasing cigarettes on-line. Internet cigarette vendors should be required to comply with the same provisions that apply to bricks-and-mortar retail vendors and charge appropriate state and local cigarette excise taxes. In the absence of such policies, the sales of cheaper, tax-free cigarettes on-line will undermine the public health benefit of raising cigarette prices

Number of unique Twitter users identified from birthday tweets by age group.

<p>Number of unique Twitter users identified from birthday tweets by age group.</p

Predicting age groups of Twitter users based on language and metadata features

<div><p>Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.</p></div

Precision and recall results from validation of multiple age classification models.

<p>Precision and recall results from validation of multiple age classification models.</p

Top predictive features for each age group in tweet language use and Twitter handle metadata models.

<p>Top predictive features for each age group in tweet language use and Twitter handle metadata models.</p

Confusion matrix.

<p>Confusion matrix.</p