Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

Vaporous marketing: Uncovering pervasive electronic cigarette advertisements on twitter

Background Twitter has become the wild-west of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, kid-friendly flavors, algorithmically generated false testimonials, and free samples. Methods All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. Results The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words \u27ban\u27, \u27tobacco\u27, \u27doesn\u27t\u27, \u27drug\u27, \u27against\u27, \u27poison\u27, \u27tax\u27 and a relative decrease in the positive words like \u27haha\u27, \u27good\u27, \u27cool\u27. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like \u27best\u27, \u27win\u27, \u27buy\u27, \u27sale\u27, \u27health\u27, \u27discount\u27 and a relative decrease in negative words like \u27bad\u27, \u27hate\u27, \u27stupid\u27, \u27don\u27t\u27. Conclusions Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing

Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media

Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. We mined data from several public Twitter endpoints to identify content relevant to healthcare providers and public health regulatory professionals. We began by compiling content related to electronic nicotine delivery systems (or e-cigarettes) as these had become popular alternatives to tobacco products. There was an apparent need to remove high frequency tweeting entities, called bots, that would spam messages, advertisements, and fabricate testimonials. Algorithms were constructed using natural language processing and machine learning to sift human responses from automated accounts with high degrees of accuracy. We found the average hyperlink per tweet, the average character dissimilarity between each individual\u27s content, as well as the rate of introduction of unique words were valuable attributes in identifying automated accounts. We performed a 10-fold Cross Validation and measured performance of each set of tweet features, at various bin sizes, the best of which performed with 97% accuracy. These methods were used to isolate automated content related to the advertising of electronic cigarettes. A rich taxonomy of automated entities, including robots, cyborgs, and spammers, each with different measurable linguistic features were categorized. Electronic cigarette related posts were classified as automated or organic and content was investigated with a hedonometric sentiment analysis. The overwhelming majority (≈ 80%) were automated, many of which were commercial in nature. Others used false testimonials that were sent directly to individuals as a personalized form of targeted marketing. Many tweets advertised nicotine vaporizer fluid (or e-liquid) in various “kid-friendly” flavors including \u27Fudge Brownie\u27, \u27Hot Chocolate\u27, \u27Circus Cotton Candy\u27 along with every imaginable flavor of fruit, which were long ago banned for traditional tobacco products. Others offered free trials, as well as incentives to retweet and spread the post among their own network. Free prize giveaways were also hosted whose raffle tickets were issued for sharing their tweet. Due to the large youth presence on the public social media platform, this was evidence that the marketing of electronic cigarettes needed considerable regulation. Twitter has since officially banned all electronic cigarette advertising on their platform. Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indicators both during and post treatment. We have studied several active cancer patient populations, discussing their experiences with the disease as well as survivor-ship. We experimented with a Convolutional Neural Network (CNN) as well as logistic regression to classify tweets as patient related. This led to a sample of 845 breast cancer survivor accounts to study, over 16 months. We found positive sentiments regarding patient treatment, raising support, and spreading awareness. A large portion of negative sentiments were shared regarding political legislation that could result in loss of coverage of their healthcare. We refer to these online public testimonies as “Invisible Patient Reported Outcomes” (iPROs), because they carry relevant indicators, yet are difficult to capture by conventional means of self-reporting. Our methods can be readily applied interdisciplinary to obtain insights into a particular group of public opinions. Capturing iPROs and public sentiments from online communication can help inform healthcare professionals and regulators, leading to more connected and personalized treatment regimens. Social listening can provide valuable insights into public health surveillance strategies

Health Risks of e-cigarettes: Analysis of Twitter Data Using Topic Mining

The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These issues include lung diseases, coughing and breathing issues, heart related issues, throat burn, respiratory related risks, dizziness, addiction, bronchitis, and cancer

Reactions to FDA e-cigarette regulation : how vape shops in different socioeconomic areas of Kentucky use Facebook.

In May 2016 the FDA deemed e-cigarettes a tobacco product. The “deeming rule” gave regulation to e-cigarettes where none had existed prior. Some of the regulations included: prohibiting the sale of e-cigarettes to minors, prohibiting giving away free e-cigarette products, requiring FDA-approved warning labels on e-cigarette packaging, and requiring pre-market authorization of all new e-cigarette products. The consequences of these regulations primarily affected e-cigarette manufacturers and vape shops, where most e-cigarette sales take place. In July 2017 the FDA announced an extended timeline for some e-cigarette regulations, citing the reason as having limited research on if, and how, e-cigarettes can be used as a smoking cessation tool. This research focuses on how vape shops use Facebook to communicate about FDA e-cigarette regulations, and whether socioeconomic factors influenced their communication. Facebook posts from six vape shops in two socioeconomic areas of Kentucky were evaluated using an inductive thematic analysis to determine the emerging themes and sub-themes. Four main themes related to the FDA regulations were found: FDA Regulations; Health, Smoking Cessation, and Education; Discounts and Giveaways; and E-liquids. Each theme was characterized by distinct sub-themes, which shared similarities and differences between the two socioeconomic areas

Is exposure to social media advertising and promotion associated with e-cigarette use? evidence from Indonesia.

BACKGROUND: E-cigarette use is a growing public health concern globally, including in Indonesia. OBJECTIVE: Our study aimed to assess whether exposure to advertising and promotion in social media is associated with e-cigarette use in Indonesia, an upper-middle-income country. METHODS: We conducted mixed-method research including quantitative and qualitative methods. For the quantitative methods, we did an online survey in the five largest cities in the country with 1,239 participants during September-October 2020. For the qualitative method, we conducted an in-person focused group discussion during November 2020 (while complying to the COVID-19 protocols). RESULTS: We found high rates of e-cigarette ever use (29%) and current use (13%) among study participants. There was high exposure to e-cigarette advertising and promotion in social media, with a majority of participants (84%) reported ever seeing e-cigarette adverts or promotions on Facebook, Instagram, YouTube, and others. Both high rates of e-cigarette use and exposure to social media advertising are associated. Participants reported ever seeing adverts and promotions were 2.91 times and 2.82 times more likely ever to use and currently use e-cigarettes, respectively, after controlling for socioeconomic factors, region, and cigarette smoking status. CONCLUSION: Exposure to social media advertising and promotion is associated with e-cigarette use in Indonesia

Internet All Nation Breath of life (I-ANBL) a Tribal College Student Engaged Development of an Internet-based Smoking Cessation Intervention

Background: Compared to non-Hispanic white college students, American Indian (AI) tribal college students have the highest smoking prevalence in the U.S. (~34%). Culturally-tailored smoking cessation programs have proven to be successful in reducing smoking rates but may require new methods to reach college students. Currently, there is little documentation on the development and success of Internet-based smoking interventions for AI tribal college students. Objectives: To develop an Internet-based smoking cessation program (Internet-All Nations Breath of Life or I-ANBL) with tribal college students. Methods: We conducted six focus groups (n=41) at a tribal college. Focus groups included tribal college students who smoked and groups were stratified by sex. Transcripts were analyzed using insider and outsider perspectives. After analysis, an Internet-based smoking cessation program was developed, based on insight gained. Results: Numerous suggestions for creating the program were offered. There was consensus on the need for a variety of visuals including cultural images, videos, and interactive content. The students also suggested the integration of familiar platforms such as FacebookTM. Conclusion: When culturally tailoring a web-based smoking cessation program for tribal college students, it is important to incorporate cultural aspects and recognize gender differences. One important aspect is to recognize that for many AI, tobacco is a sacred plant and images of tobacco should be respectful. Now that this intervention has been developed, next we will test it for efficacy in a randomized controlled trial. Keywords: American Indians, tribal college, tobacco, program development, smoking cessation, community-based participatory researc

World Vaping Day: Contextualizing Vaping Culture in Online Social Media Using a Mixed Methods Approach

Few studies have demonstrated the use of mixed methods research to contextualize health topics using primary data from social media. To address this gap in the methodological literature, we present research about electronic nicotine delivery systems, using Twitter data from “World Vaping Day.” To engage with the quantitative breadth and qualitative depth of 5,149 collected tweets, we utilized a convergent parallel mixed methods framework, integrating thematic prevalence estimates with phenomenological contextualization. Sentiment was more positive than negative across all categories except policy related. A total of 23% of tweets were promotional and relatively few tweets related to tobacco use (4.9%) or health concerns (4.2%). Salient themes included modifying or upgrading electronic nicotine delivery systems devices, and general mistrust of public health advocates and tobacco companies. </jats:p

Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks