Electronic Cigarettes and Twitter: Sentiments ...eclark/VCBH_EcigPoster_eclark.pdf · Infomercial...
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Electronic Cigarettes and Twitter: Sentiments, Categorization, and Hedonometrics Eric M. Clark 1,2,3,4,5 , Chris Jones 5,8,9 , Diann Gaalema 6,7,8 , Ryan Redner 6,8 , Thomas J. White 6,8 , Allison Kurti 6,8 , Andrew Schneider 8 , Marion Couch 5 , Peter Dodds 1,2,3,4 , and Chris Danforth 1,2,3,4 Computational Story Lab 1 , Department of Mathematics & Statistics 2 , Vermont Complex Systems Center 3 , Vermont Advanced Computing Core 4 , Department of Surgery 5 , Department of Psychiatry 6 , Department of Psychology 7 , Vermont Center on Behavior and Health 8 , & Global Health Economics Unit of the Vermont Center for Clinical and Translational Science 9 Abstract Electronic cigarettes, or e-cigs for short, have become a popular alternative to traditional tobacco products. The vaporization technology present in e-cigarettes allows consumers to simulate tobacco smoking without igniting the carcinogens found in tobacco. The health risks, marketing regulations, and the potential of these devices as a form of nicotine replacement therapy are hotly debated both politically and clinically. Twitter, a mainstream social media outlet, provides a means to survey the popularity and sentiment of consumer opinions regarding e-cigarettes. Approximately 700,000 tweets containing mentions of e-cigarettes were collected from a 10% sample of Twitter spanning from January 2012 to July 2014. All tweets mentioning e-cigarettes were categorized as Commercial, Infomercial, or Organic. Tweets in the commercial category (≈ 70%) contained atleast 3 marketing key words (e.g. ‘free trial’, ‘buy’, ‘coupon’, ‘starter kit’,... ) , a key word along with a URL, or are from SPAM accounts. The Infomercial category (≈ 15%) contains all tweets with URLs that omit these key-words. The Organic category (≈ 15%) contains the remaining tweets. The emotionally charged words that contribute to the positivity of various subsets of tweets from each category are quantitatively measured, a hedonometrics. Outliers in both the positivity and frequency time-series distributions correspond to political debates regarding the regulation of e-cigarettes. Time-series analysis techniques are implemented to determine the effect promotional tweets have on organic sentiments. Due to the high youth presence on twitter as well as the clinical uncertainty regarding the risks associated with e- cigarettes, understanding the effect of promotionally marketing vaporization products across social media is relevant to public health agendas. Hedonometrics: Measuring the Happiness of a Text LabMT is a happiness distribution of the most frequently occurring 10,000 English words that were compiled through frequency distributions from literature,(Google Books), websites (Google Web Crawl), and Twitter. Surveys were created mimicking the self affective mannequin method, a sample of which is given above. Fifty participants were recruited using the online survey tool, Amazon Mechanical Turk, to identify the face that best matched the emotional response elicited by each word, which were then converted to a 9 point scale. On the numeric scale, 1 corresponded to the face with the largest frown and 9 to the face with the largest smile. The average happiness score, h avg , for each word was then calculated via the arithmetic mean of 50 user reported ratings per word. Using the average happiness scores of each word, the average positivity of a subset of tweets can be quantified and used to compare different tweet distributions. To increase the emotional signal, neutral words (4 ≤ h avg ≤ 6) are removed from the analysis. The standard approach to perform a hedonometric analysis on twitter is to create a happiness time-series. Outliers on the time series correspond to time-periods containing an overabundance of emotionally charged words. These outliers can then be investigated with word-shift graphs to help illuminate what is driving the emotional shift. US Geo-tagged E-cig Tweets: March-August 2014 Approximately 1% of all tweets report the geo-location to within ten meters of accuracy of the user. This Geo-tagged data-set allows for regional comparisons of Electronic Cigarette mentions across the United States. All tweets that mention E-cigarette keywords between March and August 2014 were collected and binned by their U.S. state. Below we present a heat map of the counts of these tweets per each state. A substantial number of tweets is required to perform a meaningful hedonometric analysis of a region. This heat map shows us that California, Texas, and New York are most prevalently mentioning E-cigarettes, which in part is due to their larger populations relative to the rest of the US. Using word shift graphs (see right pane for a more thorough explanation), the different types of words influencing the average happiness of several regions are displayed below. The leftmost shift compares the tweets from New York relative to California. In New York there are less occurrences of the negative words ‘ban’, ’restrictions’, a higher occurrence of the negative words ‘poison’, ‘died’, ‘worst’, ‘stupid’. On the right, tweets from Texas are compared to tweets from New York. In Texas there are less occurrences of ‘banned’, ‘poison’, ‘protest’, and ‘nasty’, and more occurrences of ‘juice’, ‘flavor’, ‘candy’, and ‘quit’. Acknowledgments The authors wish to acknowledge the Vermont Advanced Computing Core, which is supported by NASA (NNX-08AO96G) at the University of Vermont which provided High Performance Computing resources that contributed to the research results reported within this poster. EMC was supported by the UVM Complex Systems Center, PSD was supported by NSF Career Award # 0846668. CMD and PSD were also supported by a grant from the MITRE Corporation. CJ, DG, RR, TJW, AK, and AS are supported in part by the National Institute of Health (NIH) Research wards R01DA014028 & R01HD075669, and by the Center of Biomedical ResearchExcellence Award P20GM103644 from the National Institute of General Medical Sciences. Ecig Categorical Tweet Happiness Distributions Using the happiness scores from LabMT, the average emotional rating of a corpus is calculated by tallying the appearance of words found in the intersection of the wordlist and a given corpus, in this case subsets of tweets. A weighted arithmetic mean of each word’s frequency, f word , and corresponding happiness score, h word for each of the N words in a text yields the average happiness score for the corpus, ¯ h text : ¯ h text = N ∑ w =1 f w · h w N ∑ w =1 f w All E-cigarette mentions spanning January 2012 to July 2014 from the Twitter firehose, a 10% sample of all tweets, were collected and plotted as a function of time (upper left). The tweets were categorized into three classes: Organic, Commercialized, and Infomercial. Tweets with an abundance of marketing keywords were classified as Commercial. Tweets without these commercial keywords but containing a URL were classified as Infomercial. The remaining tweets make up the Organic Category. Categorizing and analyzing these categories separately is important to isolate true user sentiments pertaining to E-cigarettes. Marketing tweets use many overly positive words to advertise the product. There are also orders of magnitude more commercialized tweets than Organic and Infomercial Tweets. Since the use of Social Media as a marketing outlet for E-cigarettes is currently a hot political issue, it’s important to isolate each of these categories and analyze each seperately. The number of E-cigarette tweets from each user in this study is displayed on logarithmic axes to the right. The Commercial distribution is quite different from the Organic and Infomercial in terms of its size and max number of individual user tweets. These marketing (SPAMers) tweet high volumes of E-cigarette related advertising, some of which are directed to Organic twitter patrons. Categorical Word-Shifts: Sentiments over Time Word-shift graphs illustrate two separate word frequency distributions. A reference period (T ref ), creates a basis of the emotional words being used to compare with another period, (T comp ). The top 50 words responsible for a happiness shift between the two periods are displayed, along with their contribution to shifting the average happiness of the tweet-set. The arrows (↑, ↓) next to a word indicate an increase or decrease, respectively, of the word’s frequency during the comparison period with respect to the reference period. The addition and subtraction signs indicate if the word contributes positively or negatively, respectively, to the average happiness score. Here we can identify the words contributing to the change in happiness between each category and over time. (Right) Here the change in sentiments of organic tweets over time (in yearly bins) is visualized with word shift graphs. The average positivity of Organic Tweets has decreased over time in both cases. On the left, 2012 is used as reference for 2013. An increase in the negative words ‘die’,‘ban’, ‘hate’, ‘against’, ‘stop’, and a decrease in positive words like ‘haha’, ‘love’, ‘good’, and ‘hope’. The word shift on the right compares 2014 to 2012, with a similar theme. Since E-cigarettes were discussed as a means of ‘quitting tobacco’ it is of note that the relative use of ‘quit’ has continued to decrease over time. (Right) Here, word shift graphs compare the Commercial (left) and Infomercial (right) categories in reference to the Organic category over 2012. There is a copious amount of both positive and advertisement related words in the Commercial Tweet set including ‘free’, ‘trial’, ‘sale’, ‘new’, ‘save’, etc. There is a similar theme from the Infomercial Category from which many tweets describe an E-cigarette brand and provide a URL. It is also notable that the word ‘quit’ has a higher relative appearance in both of these categories in comparison to Organic Tweets. Categorical Time Series Correlations: Sentiments and Counts Spearman Monthly Frequency Correlations Commercial Infomercial Organic 0.587 0.879 Infomercial 0.468 (p < 0.05) Spearman Monthly Happiness Correlations Commercial Infomercial Organic 0.434 0.762 Infomercial 0.406 (p < 0.05) Here, the relationship between each of these categorical tweets is explored. Some possible evidence that the Commercial or Infomercial categories are having an effect on Organic sentiments and frequencies is quanitfied. A nontrivial number of Commercial and Infomercial tweets are directed at Organic users. On the left, all Commercial and Organic tweets spanning January 2012 to July 2014 are binned into their hourly distributions and correlated as a function of an hourly lag. Each of these correlations are significant for the first 10 hours (p < 0.01). The correlation is maximized with a lag of one hour. The subgraph presents the correlations for a lag of up to 400 hours. The cyclic nature is due to the daily cycle of twitter activity, and although the correlation cyclically returns to above 0.40 it is maximized within the first hour. On the right, tweets from each distribution are binned by month and correlated against each other. Both the frequency and happiness distributions exhibit a strong positive Spearman correlation. 2013 Daily Resolution: Political Responses to Ecig Regulation Each categorical time-series exhibits a severe negative trend occurring in January of 2013. Observing tweets at the daily resolution for Organic users, a spike in the frequency distribution occurred in December of 2013. There is an inverse relationship with the average happiness daily scores during this time period. This was during the time that the EU was debating a possible e-cigarette ban. Many tweets in this time frame were tagged with #EUcigban. The sentiments of Organic users as well as those from Commercialized accounts are visualized from this time period with word shift graphs. (Right) On the leftmost word shift Organic Tweets from December 2013 (during the debate) are compared against tweets from January 2013 for reference. There is a plethora of negative words including ‘ban’, ‘stop’, ‘against’, ‘disaster’, ‘deaths’, and ‘corruption’ among others. The word shift on the right depicts the same time period, but are taken from the Commercial tweet category. Here there is an increase of the negative words ‘die’, ‘stop’, ‘no’, ‘not’, ‘ban’, and a decrease in positive words (related to marketing) ‘free’, ‘happy’, ’win’, and ‘thanks’. http://www.uvm.edu/storylab @compstorylab
Transcript of Electronic Cigarettes and Twitter: Sentiments ...eclark/VCBH_EcigPoster_eclark.pdf · Infomercial...
Electronic Cigarettes and Twitter: Sentiments, Categorization, and HedonometricsEric M. Clark1,2,3,4,5, Chris Jones5,8,9, Diann Gaalema6,7,8, Ryan Redner6,8, Thomas J. White6,8, Allison Kurti6,8, Andrew Schneider8,
Marion Couch5, Peter Dodds1,2,3,4 , and Chris Danforth1,2,3,4
Computational Story Lab1, Department of Mathematics & Statistics2, Vermont Complex Systems Center3, Vermont Advanced Computing Core4,Department of Surgery5, Department of Psychiatry6, Department of Psychology7, Vermont Center on Behavior and Health8,
& Global Health Economics Unit of the Vermont Center for Clinical and Translational Science9
AbstractElectronic cigarettes, or e-cigs for short, have become a popular alternative to traditional tobacco products. The vaporization
technology present in e-cigarettes allows consumers to simulate tobacco smoking without igniting the carcinogens found intobacco. The health risks, marketing regulations, and the potential of these devices as a form of nicotine replacement therapy arehotly debated both politically and clinically. Twitter, a mainstream social media outlet, provides a means to survey the popularityand sentiment of consumer opinions regarding e-cigarettes. Approximately 700,000 tweets containing mentions of e-cigaretteswere collected from a 10% sample of Twitter spanning from January 2012 to July 2014.All tweets mentioning e-cigarettes were categorized as Commercial, Infomercial, or Organic. Tweets in the commercial category(≈ 70%) contained atleast 3 marketing key words (e.g. ‘free trial’, ‘buy’, ‘coupon’, ‘starter kit’,... ) , a key word along with a URL, orare from SPAM accounts. The Infomercial category (≈ 15%) contains all tweets with URLs that omit these key-words. The Organiccategory (≈ 15%) contains the remaining tweets.
The emotionally charged words that contribute to the positivity of various subsets of tweets from each category arequantitatively measured, a hedonometrics. Outliers in both the positivity and frequency time-series distributions correspond topolitical debates regarding the regulation of e-cigarettes. Time-series analysis techniques are implemented to determine the effectpromotional tweets have on organic sentiments. Due to the high youth presence on twitter as well as the clinical uncertaintyregarding the risks associated with e- cigarettes, understanding the effect of promotionally marketing vaporization products acrosssocial media is relevant to public health agendas.
Hedonometrics: Measuring the Happiness of a Text
LabMT is a happiness distribution of the most frequently occurring 10,000 English words that were compiled throughfrequency distributions from literature,(Google Books), websites (Google Web Crawl), and Twitter. Surveys were createdmimicking the self affective mannequin method, a sample of which is given above. Fifty participants were recruited using theonline survey tool, Amazon Mechanical Turk, to identify the face that best matched the emotional response elicited by each word,which were then converted to a 9 point scale. On the numeric scale, 1 corresponded to the face with the largest frown and 9 to theface with the largest smile. The average happiness score, havg, for each word was then calculated via the arithmetic mean of 50user reported ratings per word. Using the average happiness scores of each word, the average positivity of a subset of tweets canbe quantified and used to compare different tweet distributions. To increase the emotional signal, neutral words (4 ≤ havg ≤ 6) areremoved from the analysis. The standard approach to perform a hedonometric analysis on twitter is to create a happinesstime-series. Outliers on the time series correspond to time-periods containing an overabundance of emotionally charged words.These outliers can then be investigated with word-shift graphs to help illuminate what is driving the emotional shift.
US Geo-tagged E-cig Tweets: March-August 2014
Approximately 1% of all tweets report the geo-location to within ten meters of accuracy of the user. This Geo-tagged data-setallows for regional comparisons of Electronic Cigarette mentions across the United States. All tweets that mention E-cigarettekeywords between March and August 2014 were collected and binned by their U.S. state. Below we present a heat map of thecounts of these tweets per each state. A substantial number of tweets is required to perform a meaningful hedonometric analysisof a region. This heat map shows us that California, Texas, and New York are most prevalently mentioning E-cigarettes, which inpart is due to their larger populations relative to the rest of the US.
Using word shift graphs (see right pane for a more thorough explanation), the different types of words influencing the averagehappiness of several regions are displayed below. The leftmost shift compares the tweets from New York relative to California. In NewYork there are less occurrences of the negative words ‘ban’, ’restrictions’, a higher occurrence of the negative words ‘poison’, ‘died’,
‘worst’, ‘stupid’. On the right, tweets from Texas are compared to tweets from New York. In Texas there are less occurrences of‘banned’, ‘poison’, ‘protest’, and ‘nasty’, and more occurrences of ‘juice’, ‘flavor’, ‘candy’, and ‘quit’.
Acknowledgments
The authors wish to acknowledge the Vermont Advanced Computing Core, which is supported by NASA (NNX-08AO96G) at theUniversity of Vermont which provided High Performance Computing resources that contributed to the research results reportedwithin this poster. EMC was supported by the UVM Complex Systems Center, PSD was supported by NSF Career Award #0846668. CMD and PSD were also supported by a grant from the MITRE Corporation. CJ, DG, RR, TJW, AK, and AS aresupported in part by the National Institute of Health (NIH) Research wards R01DA014028 & R01HD075669, and by the Center ofBiomedical ResearchExcellence Award P20GM103644 from the National Institute of General Medical Sciences.
Ecig Categorical Tweet Happiness Distributions
Using the happiness scores from LabMT, the average emotional rating of a corpus is calculated by tallying the appearance of words found in the intersection of the wordlistand a given corpus, in this case subsets of tweets. A weighted arithmetic mean of each word’s frequency, fword , and corresponding happiness score, hword for each of the Nwords in a text yields the average happiness score for the corpus, h̄text :
h̄text =
N∑w=1
fw · hw
N∑w=1
fw
All E-cigarette mentions spanning January 2012 to July 2014 from the Twitter firehose, a 10% sample of all tweets, were collected and plotted as a function of time (upperleft). The tweets were categorized into three classes: Organic, Commercialized, and Infomercial. Tweets with an abundance of marketing keywords were classified asCommercial. Tweets without these commercial keywords but containing a URL were classified as Infomercial. The remaining tweets make up the Organic Category.Categorizing and analyzing these categories separately is important to isolate true user sentiments pertaining to E-cigarettes. Marketing tweets use many overly positivewords to advertise the product. There are also orders of magnitude more commercialized tweets than Organic and Infomercial Tweets. Since the use of Social Media as amarketing outlet for E-cigarettes is currently a hot political issue, it’s important to isolate each of these categories and analyze each seperately.
The number of E-cigarette tweets from each user in this study is displayed on logarithmic axes to the right. The Commercial distribution is quite different from the Organicand Infomercial in terms of its size and max number of individual user tweets. These marketing (SPAMers) tweet high volumes of E-cigarette related advertising, some ofwhich are directed to Organic twitter patrons.
Categorical Word-Shifts: Sentiments over Time
Word-shift graphs illustrate two separate word frequency distributions. A reference period (Tref ), creates a basis of the emotional words being used to compare with anotherperiod, (Tcomp). The top 50 words responsible for a happiness shift between the two periods are displayed, along with their contribution to shifting the average happiness ofthe tweet-set. The arrows (↑, ↓) next to a word indicate an increase or decrease, respectively, of the word’s frequency during the comparison period with respect to thereference period. The addition and subtraction signs indicate if the word contributes positively or negatively, respectively, to the average happiness score. Here we canidentify the words contributing to the change in happiness between each category and over time.
(Right) Here the change insentiments of organic tweets overtime (in yearly bins) is visualizedwith word shift graphs. Theaverage positivity of OrganicTweets has decreased over time inboth cases. On the left, 2012 isused as reference for 2013. Anincrease in the negative words‘die’,‘ban’, ‘hate’, ‘against’, ‘stop’,and a decrease in positive wordslike ‘haha’, ‘love’, ‘good’, and‘hope’. The word shift on the rightcompares 2014 to 2012, with asimilar theme. Since E-cigaretteswere discussed as a means of‘quitting tobacco’ it is of note thatthe relative use of ‘quit’ hascontinued to decrease over time.
(Right) Here, word shift graphscompare the Commercial (left) andInfomercial (right) categories inreference to the Organic categoryover 2012. There is a copiousamount of both positive andadvertisement related words in theCommercial Tweet set including‘free’, ‘trial’, ‘sale’, ‘new’, ‘save’,etc. There is a similar theme fromthe Infomercial Category fromwhich many tweets describe anE-cigarette brand and provide aURL. It is also notable that theword ‘quit’ has a higher relativeappearance in both of thesecategories in comparison toOrganic Tweets.
Categorical Time Series Correlations: Sentiments and Counts
Spearman Monthly Frequency CorrelationsCommercial Infomercial
Organic 0.587 0.879Infomercial 0.468 (p < 0.05)
Spearman Monthly Happiness CorrelationsCommercial Infomercial
Organic 0.434 0.762Infomercial 0.406 (p < 0.05)
Here, the relationship between each of these categorical tweets is explored. Some possible evidence that the Commercial or Infomercial categories are having an effect onOrganic sentiments and frequencies is quanitfied. A nontrivial number of Commercial and Infomercial tweets are directed at Organic users. On the left, all Commercial andOrganic tweets spanning January 2012 to July 2014 are binned into their hourly distributions and correlated as a function of an hourly lag. Each of these correlations aresignificant for the first 10 hours (p < 0.01). The correlation is maximized with a lag of one hour. The subgraph presents the correlations for a lag of up to 400 hours. Thecyclic nature is due to the daily cycle of twitter activity, and although the correlation cyclically returns to above 0.40 it is maximized within the first hour.On the right, tweets from each distribution are binned by month and correlated against each other. Both the frequency and happiness distributions exhibit a strong positiveSpearman correlation.
2013 Daily Resolution: Political Responses to Ecig Regulation
Each categorical time-series exhibits a severe negative trend occurring in January of 2013. Observing tweets at the daily resolution for Organic users, a spike in thefrequency distribution occurred in December of 2013. There is an inverse relationship with the average happiness daily scores during this time period. This was during thetime that the EU was debating a possible e-cigarette ban. Many tweets in this time frame were tagged with #EUcigban. The sentiments of Organic users as well as thosefrom Commercialized accounts are visualized from this time period with word shift graphs.
(Right) On the leftmost word shiftOrganic Tweets from December2013 (during the debate) arecompared against tweets fromJanuary 2013 for reference. Thereis a plethora of negative wordsincluding ‘ban’, ‘stop’, ‘against’,‘disaster’, ‘deaths’, and ‘corruption’among others. The word shift onthe right depicts the same timeperiod, but are taken from theCommercial tweet category. Herethere is an increase of the negativewords ‘die’, ‘stop’, ‘no’, ‘not’, ‘ban’,and a decrease in positive words(related to marketing) ‘free’,‘happy’, ’win’, and ‘thanks’.
http://www.uvm.edu/storylab @compstorylab
