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Evaluating Marijuana-Related Tweets on Twitter1 Evaluating Marijuana-Related Tweets On Twitter1 Anh Nguyen2y, Quang Hoang∗, Hung Nguyen∗, Dong Nguyen3∗, and Tuan Tranz ∗Saolasoft Inc., Centennial, CO 880211, USA Email: fqhoang, hnguyen, [email protected] yUniversity of Denver, Denver, CO 80208, USA Email: [email protected] zSullivan University, Louisville, KY 40205, USA Email: [email protected] Abstract—This paper studies marijuana-related tweets in social for monitoring and tracking the health-related issues and health network Twitter. We collected more than 300,000 marijuana risk behaviors towards addictive substances like alcohol or related tweets during November 2016 in our study. Our text- tobacco of the public community in short time. mining based algorithms and data analysis unveil some inter- esting patterns including: (i) users’ attitudes (e.g., positive or Marijuana has been legalized to be used for the medical and negative) can be characterized by the existence of outer links recreational purpose in 8 states Colorado, Washington, Alaska, in a tweet; (ii) 67% users use their mobile phones to post their Oregon, California, Nevada, Maine, and Massachusetts, and messages while many users publish their messages using third- Washington, D.C. Despite some medicinal benefits, it is an party automatic posting services; and (3) the number of tweets indisputable fact that marijuana usage has exerted a myriad during weekends is much higher than during weekdays. Our data also showed the impact of the political events such as the U.S. of detrimental impacts on the public health. For example, ac- presidential election or state marijuana legalization votes on the cording to data provided by the U.S. National Survey on Drug marijuana-related tweeting frequencies. User and Health [6], youth with poor academic outcomes were more than four times as likely to have consumed marijuana I. INTRODUCTION in the past year than youth with an average of higher grades. As a microblogging website allowing users to post messages Additionally, the study in [7] also reveals a strong correla- of 140 characters or less called “tweets”, Twitter has become tion between marijuana usage and the decline in intelligence the biggest daily source of news, public opinions, and per- quotient (IQ). Cannabis possession and consumption are still sonal discussions. For example, on the day of the 2016 U.S. illegal under the federal law because of its significant health presidential election, Twitter had nearly 40 million messages and safety risks to people, especially young individuals [8]. sent by midnight that day [1]. Twitter has 310 million monthly Thus, the threats that the marijuana usage poses to the public active users, posting hundreds of millions of tweets per day health and our society should be taken into consideration [2]. People on Twitter share, exchange, and discuss any events seriously. Consequently, surveillance of actual marijuana use of their life including various issues relating to their health and concerns would be necessary and useful for legislators to conditions or health-related behaviors. This phenomenal leads impose appropriate public health laws. to a research area among public health scientists to achieve Our research leverages Twitter’s public data of marijuana- public health analytic outcomes by harnessing the vast amount related tweets exchanged among Twitter users to reveal hidden of publicly available health-related data. patterns of marijuana related aspects. Particularly, we collected That said, there is a growing interest in exploring the wide more than 300,000 marijuana-related tweets during Novem- range of topics over social media Twitter for epidemiology ber 2016 in our study. We use the unigram and bigram of and surveillance. The study of Culotta et al. [3] uses the marijuana related hashtags to compute the word frequencies. Twitter corpus to estimate influenza rates and the sale volume Furthermore, we apply some text-mining sentiment techniques arXiv:1612.08913v1 [cs.SI] 28 Dec 2016 of alcohol with high accuracy. The researchers in [4] reveals to analyze the users’ attitudes based on their tweets. Our data a content and sentiment analysis of tobacco-related Twitter indicates a strong correlation between tweets with outer links messages and categorizes tobacco-relevant posts with the focus and positive attitudes, and between the number of marijuana on emerging products like hookah and electronic cigarettes tweets with political events in November 2016. among users. Xu et al. [5] observe the usage patterns of the terms “cancer”, “breast cancer”, “prostate cancer”, and “lung II. RELATED WORK cancer” between Caucasian and African American groups on Together with the rapid expansion of social media including Twitter to understand their knowledge and awareness about Facebook, Instagram, or Snapchat, microblogging websites specific topics in real-time. Apparently, the social media- such as Twitter have evolved to become an enormous source based surveillance highlight the potential that online user- of various kind of information. Many studies have consid- generated data can be a very powerful and valuable medium ered utilizing these sites for event monitoring purposes and event-based surveillance system. For instance, using garnered 1This research will be presented in IEEE-CCWC 2017 2This research was done during the author’s intern at Saolasoft Inc. information from Twitter, the authors in [9] build a sentiment 3Corresponding author classifier based on n-gram model, that is able to determine of Online Slang Dictionary (http://onlineslangdictionary.com). Next, we developed a data collection tool that garnered all tweets containing one or more marijuana-related terms from cities and states in the United States. The workflow of the data collection mechanism is illustrated in Fig. 1. The server, written in Python, interacts with the Twitter Search API. While the Twitter Search API usually serves Tweets from the past week, our system allows us to bypass this limitation of time constraints by basically replicating the way the Twitter Search engine works on browsers. The server calls the API by com- mand: “https://twitter.com/i/search/timeline?f=realtime” with following parameters: Fig. 1. The workflow of tweet collecting server which aggregates the tweets • q: a query text in which searched tweets will contain. It according to the vocabulary of marijuana-related keywords. The server is is a word and phrase relating to marijuana, pre-stored in written in Python. our vocabulary. • since: the lower bound of the posting date of searched tweets. positive, negative and neutral emotions of Twitter users. With • until: the upper bound of the posting date of searched the same purpose, [10] conducts their research with three types tweets. of models: Unigram model, feature-based model, and tree • lang: the language of searched tweets. kernel-based model. Besides, Wang et al. [11] using Twitter corpus unravel a high correlation of some hashtags and tweets This API retrieves a list of matched tweets in the form of posted by users living in the locations affected by the 2012 an HTML string. The server then extracts useful data from Hurricane Sandy with its movement. Researchers have also HTML string and save them to our tweet database server. begun to investigate various ways of automatically collecting B. Data Description and Processing training Twitter data. Consider [12] as an example, they take The resulting tweets are stored in a NoSQL tweet database advantages of Twitter hashtags as the training data to train which includes 316; 191 documents relating to marijuana. their three-way sentiment classifiers. Each document represents a tweet with 15 different fields Studies on Twitter data are also noticeable in health care extracted from the JSON object resulted from our data col- sector. For example, the work of [13], for the first time, seeks lection process, including username, URL, external links, text, to recommend relevant Twitter hashtags for health-related the number of retweets and favorites, keyword, state, posted keywords based on distributed language representations. An- time, types of devices, etc. Also, those records are linked into alyzing data from Twitter, blogs, and forums, the authors in a different table of N-gram sequences processed from tweet [14] make an attempt to detect hints to public health threats text. We use unigrams (1-gram) and bigrams (2-gram) for as well as monitor population’s health status. In 2012, the generating text mining clouds. In term of sentiment analysis, researchers in [15] introduced a new approach to discovering we utilized machine learning techniques from a third party to a large number of meaningful ailment description by using code the Tweets for different types: positive sentiment about machine learning and natural language processing techniques. marijuana, negative sentiment about marijuana and neutral In [16], the authors make an effort to examine the sentiment sentiment about marijuana. In addition, since hashtags (symbol and themes of marijuana-related chatters on Twitter sent by #) are likely to be used before prime keywords or phrases in a influential Twitter users and to describe the demographics of tweet, we extract hashtags from Tweets as well. We also count these Twitter users. Another worth noting work is by [17] in the total number of external links of each user and analyze the which the authors estimate user demographic characteristics users who post most tweets which contain external links. based the content of tweets of a popular pro-marijuana Twitter handle and its followers. However, the studies do not consider IV. RESULTS AND DISCUSSION other meaningful properties of tweets’ metadata, such as A. Marijuana Unigram and Bigram Clouds geographical features, external links, user’s device types, etc., Unigrams and bigrams allow generating cloud tags for and their correlation with each other and with other social illustration of popular terms. Fig. 2(a) shows the most frequent phenomena. terms among unigrams (after removing some most common III. DATA COLLECTION AND DATASET terms in English). Generally, “dope”,“weed”, “pot”, and “mar- ijuana” are some highlight words.
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