Smoking Trends on Twitter

Topic and research question

It is common sense that smoking is a serious public health problem worldwide. It is considered to be the largest single cause of preventable death and disease in the world (WHO Report on the Global Tobacco Epidemic, 2008). The more research is conducted, the more is known about the risks of smoking, hence we are certain in these days that smoking causes serious harms to people’s health and can even lead to death. Research has been conducted as long as scientist and public health experts have known about the risks of smoking. Moreover it seems that the interest in researching smoking risks (Catsburg et al., 2014; Pimhanam et al., 2013) and/or people’s smoking behavior (Noorzurani et al., 2013) is important for today’s society. It comes as no surprise that scientists and researchers started engaging themselves with the questions as to why people start smoking in the first place. Thus many studies are concerned with the perception of smoking and tobacco in society and how the image of smoking is being shaped and influenced.

In this context the role of the media has been questioned many times. Especially when it comes to the problem of smoking among teenagers the film industry and mass media in general are under high scrutiny. Since the 1990s research provides a better understanding of the proliferation of the image of smoking in popular media. Young people indeed associated certain meanings and values to the representation of smoking in media (McCool et. al, 2005). Especially with the rise of the movie star in the 1930s and 1940s, showing images of leading male as well as female actors smoking in films, the image of tobacco has been glamorized (McCool et. al, 2003).  With a growing number of studies examining the image of tobacco use in film and the media, more evidence has been found that these images endorse the motivation, especially among younger people, to start smoking. The media not only plays a significant role in influencing the behavior of teenagers and generally speaking the youth culture, different kinds of mass media (film, magazine, tv) are attributing favorable characteristics to the usage of tobacco (Watson et. al, 2003). Thus there is a growing concern of the media influencing young people’s attitude toward smoking.

With the rise of social media this concern becomes even more crucial, as many people depict themselves or others on pictures while smoking and disseminating them via the various social networks. It is argued whether those pictures have potential to influence others who see these pictures. A recent study conducted earlier this year investigates how friendship with peers who smoke and showing their substance use online could influence the teens’ own attitude towards tobacco (bitscience.com, 2014).

           However research on the representation of tobacco use and smoking in social media networks has been few and clearly shows a research gap within this field of study. Therefore this study investigates the social media platform Twitter and how smoking and the sentiment towards smoking can be identified here. The research project hereby follows the overall research question: What is the image of smoking on Twitter? We developed subquestions for the logical analysis:

What devices are used to write tweets contained #smoking?

What hashtags are connected with #smoking  on Twitter?

What is the sentiment of the tweets and retweets with the hashtag #smoking and hashtags related to it?

Methodology

In order to answer our research question, certain DRM methods were applied. First, to show the real amount of tweets connected with #smoking several online tools were used. Those allowed this research to explore findings in real time and get a more complete picture of the image of smoking. Towards the end, Gephi was used for analyzing the connections between hashtags and tweets.

Data

To have a better understanding of how smoking is being perceived on Twitter, two different datasets were used. Accordingly, the first dataset is analysed through the following tools:

For the online real time analysis made with Tweet Binder, we used 1981 tweets from October 2014, retrieved on 28 October.

For the online real time analysis made with Sentiment Viz, 419 tweets from October 2014 were retrieved on 28 October 2014.

The second dataset is analysed from the perspective of Gephi.

Hashtag_edges file, which consisted of 3676 edges, was used.

The file hashtag_edges as well as the file retweet_edges (242 edges) and smokingEnLIWC file were used for the LIWC analysis.  

Methods and results

Tweet Binder

The report for #smoking shows significant results. Overall, 1,981 tweets that contain this hashtag have been retrieved. It is interesting to notice that 6,43% of them are original tweets (120), while 35,99% are retweets (717). Thus the number of retweets is larger and there is a certain interaction between users, since they are willing to retweet the others’ tweets. In addition, the analysis shows that 32 of all tweets were replies. Most tweets contains additional links and pics (55,97% of all tweets, 1115 tweets). As these additional pictures and links are another type of content than text, it testifies the interest to the topic among users.

Most of the contributors (1006) tweeted once. The number of contributors who tweeted three times was smaller than the number that tweeted six times or more. It can be assumed that there may be a person or organization interested in #smoking, who posted more than 6 tweets as it can be considered as a daily (or with a certain purpose) rather than random contribution.

Among most active contributors, there are some twitter accounts connected with porn and videos for adults, for instance, @C4SUpdates, which is “#1 Downloadable Clip Site on the Net” (https://twitter.com/@C4SUpdates, 2014). This user is also among those who has the greatest impact.

As for the top 10 sources that were used to write and retweet tweets contained #smoking, the most popular one was Twitter for Web Client. The second one was Twitter for iPhone, the third one - IFTTT. The difference between the number of tweets from each client was not significant, though: 287 for Twitter for Web Client, 235 for Twitter for iPhone and 221 for IFTTT. Interestingly we noticed Instagram among the clients (5th place with 168 tweets).

Sentiment Viz

As for the hashtags connected with #smoking, those connected to porn (#porn, #latex, #xxx) are marked as positive, while #tobacco, #cancer, #nicotine are marked as negative. Interestingly, #quit was among the positive ones. This can be interpreted as the positive emotions people feel while quitting smoking, which they share with their followers.

Hashtags connected with porn (#fetish) and pot (#420) are also connected with #smoking.

As for the sentiment of the tweets, they are mostly positive. It could be assumed that it is either because of the positive emotions people feel about smoking or because the tweets are connected with porn (and users are positive about it). 

Gephi

Gephi was used for further examination. When examining the hashtag_edges data file in Gephi and conducting indegree and Force Atlas upon the twitter data, the following Gephi visualisation appeared.

The biggest cluster (the second one) in the Gephi visualisation represents #smoking. It can be observed that the smaller clusters are connected to the #smoking. Moreover, a lot of terms are related to this tweet. Some of the most popular ones are #nicotine, #longcancer, #LetsSmokeLoud and #ShakeTheHabit. However, most interesting of all, it is the cluster of tweets forming at the right of the cluster of #smoking. These are tweets that are related to #Smoking (with capital letter S). The findings of these tweet related messages are interesting since half of the messages are also related to #Nicotine, #Addiction and #KidsHealth, #Electroniccigs topics, which are smoking-related topics. However, the other cluster that is related to #Smoking represents porn related messages.

Examples of related tweets are #porn, #Sextweets, #Nudescene and #Slut. Therefore, an interesting perception and maybe a noticeable trend is that #Smoking is not only related to subjects concerned with smoking, but also to Porn and Sex.

We sorted the data in Excel and selected all tweets with the hashtags ‘sex’,’porn’ etc. and then marked them as “porn”. As it was done manually, it could be considered as a difficulty. Manual sorting of hashtags can lead to the error in identification, for example,#sex can appear in the non spam porn related tweet, although the probability of this error is quite small and it could be neglected; nevertheless, this can be considered a limitation. While red lines are for tweets connected with porn, green lines are for tweets which do not contain any kind of porn. It is interesting to notice, that majority of the hashtags related to the pornographic content are linked together, which means that they are used together frequently. Also #smoking with all letters in lowercase has less connection to such hashtags, meaning that porn related tweets mostly use all caps or first letter caps hashtags. It can be the case that some of the hashtag which were tagged as porn hashtags were used in regular tweets, but chances of that are rather small, and we see that such hashtags are rarely connected to the normal ones. When we looked at the tweets manually, we noticed that there could be some hashtags which were not necessarily related to porn, like #naked.

LIWC data (smokingEnLIWC) was added to hastag_edges file. Blue is for none, green is for positive, and red is for negative. It is clear, that there were a lot of tweets marked as ‘none’.

It is interesting that tweets contained hashtags connected with porn are marked either ‘none’ or ‘positive’ in terms of emotions.

Hashtag ‘#420’ related to pot is also noticeable and is marked as ‘positive’.

As for analysing the retweets, Fruchterman-Reingold algorithm was applied to retweets_edges file. Red colour is for tweets connected with porn while green is for tweets about everything else. There is only one big cluster of retweets connected with porn.

The emotions for retweets were analyzed using the same algorithm: red for negative emotions, green for positive emotions and blue for neutral or none. As it can be seen, the blue retweets dominate.

Conclusion

In order to answer the main question of this research (What is the image of smoking on Twitter?), these findings show that the used database has two main points of focus when using a hashtag related to smoking: (a) health concerns and smoking in general and (b) pornographic content. Moreover, an extended range of smoking-related hashtags were used. As previously seen visualizations show, it can be assumed that #smoking is primarily related to actual smoking and health issues concerning smoking. It is interesting to note that #Smoking is not only related to actual smoking but also to porn and sexual themes. Another interesting finding concerns #SMOKING, which proves to be mostly spam or related to pornographic content. Furthermore, this research shows that when users use a smoking-related hashtag, they do not necessarily refer to cigarettes but also to cannabis and pot consumption. Tweets connected with cannabis and pot are positive, tweets connected with porn are marked either as positive, or as none in terms of emotions. Most of retweets and tweets were marked as ‘none’ or ‘positive’ in terms of emotions. These findings can be useful for future research about the image of smoking on social networks.

P.s. Visualizations can be found here.

References

Catsburg C., Kirsh V.A., Soskolne C.L., Kreiger N., & Rohan T.E. (2014). Active cigarette smoking and the risk of breast cancer: a cohort study. Cancer Epidemiol, 38(4), 376-381.  doi: 10.1016/j.canep.2014.05.007.

McCool, J. P., Cameron, L.D. & Petrie, K. J. (2005). The influence of smoking imagery on the smoking intentions of young people: Testing a media interpretation model. Journal of Adolescence Health. 36, 475-485.

McCool, J. P., Cameron, L.D. & Petrie, K. J. (2003). Interpretations of smoking in film by older teenagers. Social Science and Medicine. 56. 1023-1032.

Noorzurani, R., Bond, A., Wolff, K. (2013). A comparison of smoking behaviour characteristics between Caucasian smokers in United Kingdom and Malay smokers in Malaysia, Preventive Medicine 2013, doi: 10.1016/j.ypmed.2013.04.010.

No author. (2014) How Party Pics on Social Media Stimulate Teens Smokinng and Drinking Behaviour, Retrieved October 24th 2014 from: http://www.bitescience.com/Knowledgedatabase/Article/tabid/81/ArticleID/251/ArticleName/HowPartyPicsonSocialMediaStimulateTeensSmokingandDrinkingBehavior/Default.aspx

Pimhanam, C., Sangrajrang, S., Ekpanyaskul, C. (2014). Tobacco Smoke Exposure and Breast Cancer Risk in Thai Urban Females. Asian Pacific Journal of Cancer Prevention, 15 (17), 7407-7411.

Sentiment Viz. Retrieved from: http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

Tweet Binder. Retrieved from: http://www.tweetbinder.com/

Watson, N.A., Clarkson, J.P., Donovan, R.J. & Glies-Corti, B.(2003). Filthy or fashionable? Young people’s perception of smoking in the media. Health Education Research. Vol. 18, no. 5, 554-567.

Tutorial group 1

Petrica Mogos - 415170

Ekaterina Sytova - 414041

Barbara Wilton - 334057

Valentina von Lutterotti - 408517

Group Project Update 

Topic and research question

It is common sense that smoking is a serious public health problem worldwide. It is considered to be the largest single cause of preventable death and disease in the world (WHO Report on the Global Tobacco Epidemic, 2008). The more research is conducted, the more is known about the risks of smoking, hence we are certain in these days that smoking causes serious harms to people’s health and can even lead to death. Research has been conducted as long as scientist and public health experts have known about the risks of smoking, and the interest in researching smoking risks (Catsburg et al., 2014; Pimhanam et al., 2013), or people’s smoking behavior (Noorzurani et al., 2013) seems to be important for today’s society. The problem of quitting smoking has been seen as a crucial one as well, considering the harm smoking does to the health of the people.

Several studies related to the topic of quitting smoking aim to answer the question of how social media and communication means can encourage a person to quit smoking, if social engagement matters and how social networks can help stop smoking. Considering the internet as a meeting place for people from all over the world researchers moreover are interested in examining the potential social media could entail as a supporting force in the process of stop smoking.

A review of five articles published in Cochrane Summaries (Whittaker et. al, 2012) shows that text messages can increase chances for quitting up to 100%. Messages that encourage motivate and support, making it easier to break the destructive habit. Although it is shown that there is an increase in a long term rate of cessation, results seems to be not unambiguous, indicating that text messages may not be the best way to intervene people’s communication space.

There is where social networks and smartphone apps come in, since vast spread of smartphones and mobile internet made text messages somewhat irrelevant right now and most people learned how to ignore messages that come not directly from the people they know. In the study by Joe Phua (2014) groups that were focused on quitting smoking in the social networks were analyzed. Using online questionnaires to conduct ANOVA analysis showed that social interaction and engagement in such groups helps to establish proper social identification, and the more active a person is, the higher is the probability of smoking cessation for this individual.

The rise of social media give concepts such as community and support groups a whole new meaning, as the internet nowadays serves as a platform or rather a meeting point where people can exchange experiences with their cigarette consumption. The findings by Phua seem to be important, since more and more people are socially encouraged in the process of quitting smoking with the help of social networks, such as Twitter.  More than 30000 tweets appears with keywords “quit” and “smoking” together on a weekly basis (Aly, 2014). Regular smokers are now have a possibility to not rely only on their willpower to quit, now there are a network around them that allows them to communicate, share emotions, gain moral support from other people, and not just automated messages (Phua, 2014).

Another recent study that is conducted by the University of California Irvine and still in progress, is about to investigate how Twitter can help with quitting smoking (Pechmann et al., 2014, in progress). In fact people look for help or like-minded people, with whom they can share and exchange experiences and support each other.

Twitter support can be compared with the support from friends or relatives, but it allows a better social identification and self-motivation, since a smoker is communicating with people with a similar mindset and goals. It is also quite easy to start communicating and interacting: just post a tweet with a certain hashtag, reply and it is already enough to get a feeling of support and social activity. Sharing thoughts and experiences with a like-minded audience makes the person tweeting and looking for help feeling more comfortable as the like-minded community offers sympathy and the feeling not to be alone.

Seeking for professional help can be hard and cost a huge amount of time and money, same with a smoke-quitters meetups. Researches and data indicates that social network help you to be socially engaged in a process of quitting at almost no additional cost and gain additional encouragement and motivation.

This research project, however, is investigating the sentiment about smoking on Twitter, while focusing on negatively associated Tweets towards smoking. The study hereby tries to identify a trend about quitting smoking on Twitter, by analyzing hashtags used on Twitter. The overall research question hereby is: To what extent is a trend observable on Twitter that may encourage people to stop smoking? How can Twitter aid people in their aspiration to quit smoking and  what trends can be observed from analyzing hashtags that are correlated with smoking?

Data set and methods

The hashtags that are analyzed are #stopsmoking and #smokingkills with the help of data visualization tools, and #smoking with a positive/negative sentiment analysis. The method that is used here can be described as a quantitative Twitter-Data-Analysis within the realm of digital research methods. Hereto 3 Twitter datasets (one for each hashtag) have been retrieved on October 6th 2014. For the actual analysis data visualization tools are used, more precisely Tableau and Gephi, in order to make sense of the data, visualizing it and displaying the findings. For showing the real amount of tweets in real time in general, online services, such as Twit Binder (http://www.tweetbinder.com/), are used.

Process

Online real time visualisation

To begin with, hashtag #stopsmoking was analyzed. This hashtag seems to be important for our research since it contains the main idea of stop smoking. Let us focus on the on-line Twitter data available at Twit Binder to show the real amount of tweets with this hashtag being written in October 2014. The report (since 2014-10-02 till 2014-10-09) for this hashtag showed significant results. Overall, 621 tweet contained this hashtag were written. It is interesting to notice, that 16,05% of them were original tweets (100), while 23,76% were retweets (148). Thus, it can be claimed, that there is a certain interaction between users, since they are willing to retweet tweets of others. Furthermore, the largest number of tweets contained additional links and pics (52,65% of all tweets, 328 tweets). It can be stated that additional pictures and links, as being another type of content than text, can testify the interest to the topic among users, since they provide extra content.

  Tableau visualization

Now we would like to concentrate on the data retrieved from Twitter on October 6th 2014. The downloaded dataset included a number of 810 tweets that contained the #StopSmoking tag. After cleaning the data (by filtering the files, the eventual database contained only #StopSmoking tweets that were further retweeted - 250 tweets out of 810) and importing it into Tableau, several interesting aspects can be noticed. Most importantly, two main trending projections can be found on Twitter when analysing the amount of tweets that encourage people to stop smoking: as #StopSmoking was the principal searching criteria of the first part of this research, several other hashtags were found when observing the tweets that already contained #StopSmoking. First of all, in addition to the engaging trend regarding #Stoptober (whose domination in this dataset can be observed in the graph below), #StopSmoking tweets’ rationale consisted in a wide range of motives: personal endeavours (#WorkingOut) individual objectives (#MoneySaver, #Stoptober, #Motivational, #Pride, #Challenge), and, most often than not, health issues (#HealthyLiving, #BetterHealth, #LungCancer, #WeightLoss, #HarmReduction) or, other more peculiar purposes such as #Hypnotherapy, #WorldHeartDay and #App.

  For further analysis, the tweets that only contain the #StopSmoking tag have been removed. Considering that this dataset has been collected on the 6th of October, the #Stoptober label becomes increasingly important for the purpose of this research. That is so mostly due to the fact that this particular campaign has and still plays a crucial role in encouraging Twitter users to stop smoking. Based on a research that argues that stopping from smoking for 28 days increases the chances of consumers by five times to stop for good (West, R. and Stapleton, J., 2008), this worldwide challenge can be seen as an actual generator of extra tweets that discourage smoking.

Another interesting aspect that can be noticed is the link between the #Stoptober campaign and the increasing usage of technology to discourage smoking which can be revealed through the heavy utilisation of multiple tags that include #App, #Apple and #StopSmoking. Under these circumstances, a link can be noticed between NHS’ official app to stop smoking which was released in 2014 and this year’s #Stoptober campaign, a social media developed by the same oraganisation.

Gephi visualization

However, will the above established trends in Tableau also be clearly visible when looking at the dataset with Gephi? For further exploration a closer examination in another visualisation programme is needed. The tweet data file consists of 2959 edges. Since edges refer to the relations between nodes, this means that there are several relations present between the individual twitters. When examining the hashtag edges data file of data file in Gephi the following visualisation is shaped.

As can be seen above two clear clusters are present. It is strikingly that one cluster is bigger in size, and has more relations within the cluster. The biggest cluster is the cluster with the #StopSmoking. The second cluster at the right represents the cluster with #Stoptober, #Stopsmoking. This cluster is notably smaller in size than the one to the left. However both clusters show a lot of internal relations and links. Therefore, overall it can be stated that there are a lot of users that retweet messages from other users. There are even several users that retweeted multiple stop smoking tweets. Thus it can be concluded that people are actively participating in the tweeting and retweeting process of stopping with smoking.

References

Aly, E. (2014, August 12). Who Tweets about Quitting Smoking? Health Media Collaboratory. Retrieved from: http://www.healthmediacollaboratory.org/who-tweets-about-quitting-smoking/.

Catsburg C., Kirsh V.A., Soskolne C.L., Kreiger N., & Rohan T.E. (2014). Active cigarette smoking and the risk of breast cancer: a cohort study. Cancer Epidemiol, 38(4), 376-381.  doi: 10.1016/j.canep.2014.05.007.

Noorzurani, R., Bond, A., Wolff, K. (2013). A comparison of smoking behaviour characteristics between Caucasian smokers in United Kingdom and Malay smokers in Malaysia, Preventive Medicine 2013, doi: 10.1016/j.ypmed.2013.04.010.

Pechmann, C., Lakon, C., Prochaska, J. (2014, in progress). Tweet for quit: Quit smoking in 6 months using the power of Twitter. Paul Merage School of Business, University of California, Irvine. Retrieved 09.10.2014 from: http://tweet2quit.merage.uci.edu/#.

Phua, J.J. (2014). Quitting Smoking using Health Issue-Specific Social Networking Sites (SNSs): What influences Participation, Social Identification, and Smoking Cessation Self-Efficacy?. Journal of Smoking Cessation, 9(1), 39-51.

Pimhanam, C., Sangrajrang, S., Ekpanyaskul, C. (2014). Tobacco Smoke Exposure and Breast Cancer Risk in Thai Urban Females. Asian Pacific Journal of Cancer Prevention, 15 (17), 7407-7411.

West, R., Stapleton, J. (2008). Clinical and public health significance of treatments to aid smoking cessation. Eur Respir Rev, 17(110), 199–204.

Whittaker R, McRobbie H, Bullen C, Borland R, Rodgers A, Gu Y. (2012). Mobile phone-based interventions for smoking cessation. Cochrane Database of Systematic Reviews, 11. DOI: 10.1002/14651858.CD006611.pub3.

Topic:

Electronic cigarettes (or simply e-cigarettes) are advertised as a safer substitute to conventional cigarettes. The way most of them function can be explained in simple terms: e-cigarettes aerosolize nicotine and produce a vapour that emulates that of cigarettes (Yamin, Bitton, & Bates, 2010). Cahn and Siegel (2010) assume that e-cigarettes can be considered as less harmful than traditional ones, as long as they deliver nicotine free of toxins, which tobacco contains. Brown, Beard, Kotz, Michie, & West (2014) point out that the mortality and morbidity, associated with cigarette smoking, arises from the inhalation of those toxins. Moreover, some of the studies (for instance, Bullen, McRobie, & Thornley, 2010) show that smoking e-cigarettes can help to quit smoking as withdrawal symptoms of inhalators and some of e-cigarettes do not differ from each other, while traditional cigarettes “reduce withdrawal ratings more on all items than the other products” (Bullen, McRobie, & Thornley, 2010). Thus, e-cigarettes have become very popular among smokers. It is worth mentioning, that some of the studies reveal the increase in number of e-cigarettes Internet searches. Ayers, Ribisl, & Brownstein (2011) claim that the number of e-cigarette searches increased in Canada, the United Kingdom (UK), and the USA from July 2008 through February 2010. Internet queries for e-cigarettes in the USA exceeded those for “other smoking alternatives, nicotine replacement, or smoking-cessation products” in 2011 (Siegel, 2011). 

However, usage of e-cigarettes might have its disadvantages. There are some brands of e-cigarettes which contain high levels of nicotine. Usage of this types of e-cigarettes is likely to cause nicotine addiction (Glatter, Forbes, 2012), that may compel, for instance, kids or teenagers to try other products, which contain tobacco, including conventional cigarettes (“FDA Warns of Health Risks Posed by E-Cigarettes”, 2009). That is why it seems to be important to find out if there is a correlation between e-cigarettes and traditional smoking.

Our research project is focused on the dependence of traditional cigarettes consumption from usage of e-cigarettes among teenagers in the USA.

Research question:

Does the usage of the electronic e-cigarettes encourage or discourage the usage of traditional tobacco among teenagers in the USA?

Data set: http://www.cdc.gov/tobacco/data_statistics/surveys/nyts/

References:

Are Electronic Cigarettes A Safe Alternative For Smokers? Forbes, 10/30/2012 (URL: http://www.forbes.com/sites/robertglatter/2012/10/30/are-electronic-cigarettes-a-safe-alternative-for-smokers/)

Ayers JW, Ribisl KM, Brownstein JS. Tracking the rise in popularity of Electronic Nicotine Delivery Systems (electronic cigarettes) using search query surveillance. Am J Prev Med 2011; 40: 448–53. URL: http://www.ajpmonline.org/article/S0749-3797(10)00793-2/abstract

Brown J, Beard E, Kotz D, Michie S & West R. Real-world effectiveness of e-cigarettes when used to aid smoking cessation: a cross-sectional population study. Published online 20 May 2014. URL: http://onlinelibrary.wiley.com/doi/10.1111/add.12623/abstract

Bullen C, McRobie H, Thornley S, et al. Effect of an electronic cigarette on desire to smoke and withdrawal, user preferences and nicotine delivery: randomized cross-over trial. Tobacco Control 2010; 19: 98–103. URL: http://www.healthnz.co.nz/2010%20Bullen%20ECig.pdf

Electronic Cigarettes As a Smoking-Cessation Tool. Siegel, Michael B. et al. American Journal of Preventive Medicine, Volume 40, Issue 4, 2011, 472 – 475. URL: http://www.ajpmonline.org/article/S0749-3797%2810%2900792-0/fulltext

U.S. Food and Drug Admin., FDA Warns of Health Risks Posed by E-Cigarettes (2009). URL: http://www.fda.gov/downloads/ForConsumers/ConsumerUpdates/UCM173430.pdf [hereinafter FDA Consumer Update]

Yamin CK, Bitton A, & Bates DW. E-cigarettes: a rapidly growing Internet phenomenon. Ann Intern Med 2010; 153:607–9.URL: http://annals.org/article.aspx?articleid=746383

Zachary Cahn, Michael Siegel. Electronic cigarettes as a harm reduction strategy for tobacco control: A step forward or a repeat of past mistakes? Journal of Public Health Policy, 2010 (URL: http://superbeans.com/quitting/electronic_cigarettes_as_harm%20reduction.pdf)

Articles which could be useful for our study:

Westenberger BJ. US Food and Drug Administration: evaluation of e-cigarettes. St Louis, MO: US Food and Drug Administration, Center for Drug Evaluation and Research, Division of Pharmaceutical Analysis. 2009. URL: http://www.fda.gov/downloads/drugs/scienceresearch/ucm173250.pdf

Trtchounian A, Williams M, & Talbot P. Conventional and electronic cigarettes (e-cigarettes) have different smoking characteristics. Nic & Tob Research 2011; 12: 905–912. URL: http://ntr.oxfordjournals.org/content/12/9/905.full.pdf+html

Reddit link: 

Research Question: Does the usage of e-cigarettes discourage the usage of traditional tobacco among teenagers in the USA?

Data Set: http://www.cdc.gov/tobacco/data_statistics/surveys/nyts/

Academic literature: 

Ayers, J.W., Ribisl, K.M., Brownstein, J.S., (2011) Tracking the Rise in Popularity of Electronic Nicotine Delivery Systems (Electronic Cigarettes) Using Search Query Surveillance, American Journal of Preventive Medicine, Volume 40, Issue 4, Pages 448–453, April;

Brown, J., Beard, E., Kotz, D., (2014) Real-world effectiveness of e-cigarettes when used to aid smoking cessation: a cross-sectional population study, Addiction, Volume 109, Issue 9, pages 1531-1540, September;

Bullen, C., McRobbie, H., Thornley, S., et al. (2010) Effect of an electronic nicotine delivery device (e-cigarette) on desire to smoke and withdrawal, user preferences and nicotine delivery: randomised cross-over trial, Tobacco Control, BMJ: British Medical Journal, 8 April;

Cahn, Z., Siegel, M., (2010) Electronic Cigarettes as a harm reduction strategy for tobacco control: A step forward or a repeat of past mistakers?, Journal of Public Health Policy 1-16, Macmillan Publishers;

Glatter, R., (2012) Are Electronic Cigarettes A Safe Alternative For Smokers?, Forbes Magazine, Tech [accessed on: 18.09.14];

Levy, M.,  Westenberger, B.J., (2009) Evaluation of e-cigarettes, Center for Drug Evaluation and Research Division of Pharmaceutical Analysis, St. Louis;  

Siegel, M., et al (2011) Electronic Cigarettes as a Smoking-Cessation Tool, American Journal of Preventive Medicine Volume 40, Issue 4, Pages 472–475, April;