Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media (2021)

Abstract: Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore sentiment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products’ sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.

## introduction smoking tobacco causes more than 8 million deaths each year 1 . while cigarette smoking has fallen globally, e-cigarettes remain the commonly used tobacco product among the us youth 2 . so far, 2, 807 cases and 68 deaths have been recorded due to e-cigarette use-associated lung injury in the us 2 alone. these developments prompted the us government to enforce a countrywide ban on addictive flavors and sales to minors. as smokers are more likely to develop severe covid-19 symptoms, who has introduced the world's first digital health worker florence based on ai to fill the gap between overburdened medical system users who are trying to quit 3 . health-related discourses on social media have risen substantially over the years @xcite since social media provides an opportunity to informally express opinions freely with like-minded individuals across geographical and societal barriers. twitter boasts of 330 million active monthly users who send out half a billion tweets daily 4 while reddit has 430 million monthly users and has 100k+ communities foot_1 . reddit provides expendable and pseudo-anonymous accounts that are well suited for controversial discussions, including the perception of electronic cigarettes and marijuana, which might be inappropriate to discuss on non-anonymous forums @xcite . in @xcite , we see that information flows more often between users who share the same sentiments. studying trends of firstperson accounts of experience and sentiment towards different tobacco products in these forums becomes imperative in the ongoing bio-surveillance and regulatory efforts @xcite . real-time monitoring of public sentiment and informativeness gives us an opportunity for bottom-up discovery of emergent patterns, especially in vulnerable and ethnically diverse populations that may not be readily detectable by traditional methods @xcite . although yanamandra et al. (2020) identified tobacco products on twitter, they leave a gap in identifying the products' sentiment. this work explores tobacco products' identification and their sentiments in texts extracted from two commonly-used social media platforms. we release two datasets for multiclass classification annotated with tobacco products and their sentiment: sentismoke-twitter and sentismoke-reddit. 6 we ## dataset we use the smokpro (yanamandra et al., 2020) dataset, which consists of 2, 116 tobacco-related tweets classified into the following five distinct product classes: traditional tobacco product mention, modern tobacco product mention, general mention of smoking, narcotics & other drug mentions, and ambivalent mentions. the authors consider cigarette, hookah, pipe, cigar, bidis, cigarillo, shisha, and baccy as traditional tobacco products. moreover, they consider e-cigarette, e-juice, e-hookahs, e-liquid, mods, vape pens, vapes, tank systems, and electronic nicotine delivery systems (ends) as modern tobacco products. using praw foot_3 , we scrape a sample of reddit posts made in the last year. we use reddit's top filter and only consider posts with more than 10 upvotes to reduce spam and fewer interacted-with posts. while twitter's character limit is 280, reddit's maximum character limits for the title and the body are 300 and 40, 000 characters, respectively. to avoid cross-platform discrepancies, we only consider the post's title for this task due to its positive negative neutral general usage past usage before quitting sarcastic or unclear usage positive experience of the product negative experience of the product cravings cravings experienced during current/past journey of quitting depicts the usage by a second-person or third-person shows antipathy towards the product and users advertisements praising the product advertisements showcasing negative aspects or rehabilitation products general advertisements product reviews on various brands and flavours studies and statistics showcasing negative aspects and addictive nature the tobacco product is not the focus of the text. campaigns supporting usage and condemning regulatory steps quitting and cessation campaigns and movements usage of smoking-related words in other contexts. for example, "smoking gun", "smoking hot". ## methodology this section briefly describes the methodology used for our benchmarking experiments and details the semi-supervised text classification for the redditbased dataset. ## experiments in this section, we describe the text classification experiments performed using both supervised and semi-supervised learning. we also highlight the experimental settings along with the experimental results for each of the three experiments. we conduct three experiments with the aforementioned datasets: sentiment identification in twitter is a supervised experiment that entails predicting the text's sentiment in the manually annotated sentismoke-twitter dataset. product identification in reddit is a semisupervised experiment that entails predicting the tobacco product of the text in the sentismoke reddit dataset. we use the cased variant of bert large , which had the highest predictive performance in the twitter dataset @xcite , to pseudo-label the dataset as described in subsection 4.2. sentiment identification in reddit is another semi-supervised experiment that entails predicting the sentiment of the text in the sentismoke-reddit dataset. we use robert a large to pseudo-label the dataset since it performed the best on twitter. we evaluate all the models on the following metrics: f1, precision, recall, and accuracy. moreover, for fasttext, we use its automatic hyperparameter optimization functionality and validate it for 100 validation trials. for all bert-based models and their distilled variants, we use a learning rate of 1 * 10 -5 with a weight decay of 0.01, and an adam epsilon value of 1 * 10 -8 while fine-tuning the models. we use a maximum sequence length of 100 and fine-tune the models for 2 epochs. ## discussion in the year 2016 alone, an estimated 10.5 million us youth were exposed to e-cigarette advertisefigure 3 : reddit word cloud. moreover, extracting data from moderated topic subreddits directly instead of general keyword search used in previous studies @xcite helps us access targeted tobaccorelated discourse while weeding out spam and un-9 http://bit.ly/ecig_marketing 10 http://bit.ly/ecig_onlinesale 11 https://bit.ly/reddit_policy related information. we also perform a part-of-speech-based analysis for comparing between the sentismoke-twitter and sentismoke-reddit, illustrated in ## conclusion and future works this work explored sentiment and product identification on tobacco-related text from two social media platforms: twitter and reddit. we released two datasets for multiclass classification annotated across two axes: tobacco product and sentiment. we utilized semi-supervised learning on reddit text using manually annotated text for twitter. we then perform benchmarking experiments for sentiment and product identification in reddit and twitter using commonly used text classification models like fasttext, bert, roberta, and distilbert. we obtain f1 scores as high as 0.72 for supervised sentiment identification in twitter text, using manually annotated data. our semi-supervised experiments on product and sentiment identification in reddit text, using features learned from the twitter text, obtain the f1 scores of 0.57 and 0.46, respectively. future work may involve using the predicted information in recommender systems, expanding the tasks for other social media platforms, and exploring the use of metadata-derived information and comments for the task. our study can also be extended to real-time monitoring and bio-surveillance tools for social media, which takes continuous inflow of unseen data.