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

Using Large Pre-Trained Language Models to Track Emotions of Cancer Patients on Twitter

Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in these emotions and sentiment values can be clearly linked to popular cancer awareness events, the beginning of stay-at-home mandates related to COVID-19, and the relative mortality rates of different cancer diagnoses. This thesis demonstrates the accuracy and viability of using state-of-the-art NLP techniques to advance the field of public health communications analysis

Data mining Twitter for cancer, diabetes, and asthma insights

Twitter may be a data resource to support healthcare research. Literature is still limited related to the potential of Twitter data as it relates to healthcare. The purpose of this study was to contrast the processes by which a large collection of unstructured disease-related tweets could be converted into structured data to be further analyzed. This was done with the objective of gaining insights into the content and behavioral patterns associated with disease-specific communications on Twitter. Twelve months of Twitter data related to cancer, diabetes, and asthma were collected to form a baseline dataset containing over 34 million tweets. As Twitter data in its raw form would have been difficult to manage, three separate data reduction methods were contrasted to identify a method to generate analysis files, maximizing classification precision and data retention. Each of the disease files were then run through a CHAID (chi-square automatic interaction detector) analysis to demonstrate how user behavior insights vary by disease. Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. This study followed the standard CRISP-DM data mining approach and demonstrates how the practice of mining Twitter data fits into this six-stage iterative framework. The study produced insights that provide a new lens into the potential Twitter data has as a valuable healthcare data source as well as the nuances involved in working with the data

Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to equip non-technical domain experts with a structured approach that will enable them to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses

Organizational Twitter Use: A Qualitative Analysis of Tweets During Breast Cancer Awareness Month

One in eight women will develop breast cancer in her lifetime. The best-known awareness event to fight the health issue is Breast Cancer Awareness Month (BCAM). Twitter is a growing source of health information amongst users; however, little research exists into understanding how various organizations use their Twitter accounts to communicate about breast cancer during BCAM, as well as implications of this use for the health information consumers. In this context, there is also a dearth of research about if, and how organizations use behavioral change theories to tailor their social media content or not. The paper explored through qualitative content analysis how four different health related organizations- Susan G. Komen, US News Health, Woman’s Hospital and Breast Cancer Social Media use their Twitter accounts to talk about breast cancer during the Breast Cancer Awareness Month (BCAM). In this study, all the tweets by these organizations were analyzed through the framework of behavioral change theory- Health Belief Model (HBM). The main purpose of this research study was to examine the tweets of the varied organizations for the presence or absence of theoretical constructs of Health Belief Model such as perceived threat, perceived benefits, perceived barriers and cues to action, which inform about the potential for users to take protective action against breast cancer. A content analysis based on theoretical lens of Health Belief Model (HBM) of 2916 tweets revealed that majority of the tweets posted by these organizations did not reflect the theoretical constructs of Health Belief Model. Out of all the tweets that represented the theoretical constructs, it was observed that “perceived barrier” (n= 781, 26.37%), was in the maximum number. This was followed by “cues to action” (n= 711, 24.01%), “perceived benefits” (n=397, 13.40%) and “perceived threat” (n=230, 7.76%). Overall the study demonstrated that different organizations shared valuable breast cancer related content on Twitter and each Twitter outlet took a different approach to its use of Twitter, evident through focus on different types of breast cancer related content, use of elements like hashtags and videos etc

User Dynamics in Mental Health Forums – A Sentiment Analysis Perspective

Individuals around the world in need of mental healthcare do not find adequate treatment because of lacking resources. Since the necessary support can often not be provided directly, many turn to the Internet for assistance, whereby mental health forums have evolved into an important medium for millions of users to share experiences. Information Systems research lacks empirical evidence to analyze how health forums influence users’ moods. This paper addresses the research gap by conducting sentiment analysis on a large dataset of user posts from three leading English-language forums. The goal of this study is to shed light on the mood effects of mental health forum participation, as well as to better understand user roles. The results of our exploratory study show that sentiment scores develop either positively or negatively depending on the condition. We additionally investigate and report on user forum roles

Content Analysis of Hospital Reviews From Differing Sources: Does Review Source Matter?

Social media has had an impact on how patients find and evaluate medical professionals and their experiences of modern healthcare. Qualitative research in healthcare has increased its focus on social media. The present study examined 497 reviews of hospitals in the Pittsburgh area across three websites: Google, Yelp, and Healthgrades. Using computerized content analysis tools (CATA), we analyzed positive and negative comments to identify key themes. Key themes and words included “doctor,” “hospital,” “staff,” and “time.” These findings highlight the importance of medical staff to patient experience. Results indicated that Yelp had the lowest average rating. CATA also revealed that the central term for Google reviews was “hospital,” for Healthgrades reviews it was “doctor,” and the central term for Yelp reviews was “patient.” These central terms reflect the focus of each website. The present study highlights the importance of healthcare professionals understanding the source of reviews and being cautious about how social media comments are used in decision-making about the practice. Future research should try to expand this approach to other cities and countries to evaluate cross-cultural effects on social media comments

A web-based platform promoting family communication and cascade genetic testing for families with hereditary breast and ovarian cancer (DIALOGUE study)

The overall aim of this dissertation is to develop an eHealth intervention to promote family communication and cascade genetic testing among families concerned with Hereditary Breast and Ovarian Cancer (HBOC) syndrome. Within this context an international, multi-centre scientific project entitled "DIALOGUE" was designed that aims to develop (Phase A), and test the feasibility (Phase B) of an intervention within various genetic clinics across Switzerland and South Korea. This dissertation describes only the Phase A, the adaptation of an intervention, a web-based platform designed for families with HBOC to share genetic test results, including usability testing in a sample from Switzerland. Chapter 1 provides a general introduction to the current field of hereditary cancer and cascade genetic testing, including the current state of eHealth technologies in science. The chapter also includes a short introduction to the prototype developed in the U.S.—as well as a description of the DIALOGUE study. In addition, the chapter summarises the main conceptual models, i.e. the Ottawa Decision Support Framework (ODSF) and the Medical Research Council (MRC) framework. These models are commonly implemented in the development and evaluation of complex interventions. The rational of this dissertation is guided by all of these elements. Chapter 2 provides a detailed description of the dissertation’s specific aims, including the three studies conducted. The articles presented in Chapter 3 describe the methodology and findings of the dissertation. Study I comprises a systematic literature review of previous studies, with a particular focus on HBOC and Lynch syndromes. The literature review identified and synthesised evidence from psychoeducational interventions designed to facilitate family communication of genetic testing results and/or cancer predisposition and to promote cascade genetic testing. A meta-analysis was also conducted to assess intervention efficacy in relation to these two research aims. Our findings highlight the need to develop new interventions and approaches to family communication and cascade testing for cancer susceptibility. Study II describes the state-of-the-art text mining techniques used to detect and classify valuable information from interviews with study participants concerning determinants of open intrafamilial communication regarding genetic cancer risk. This study had two major aims: 1) to quantify openness of communication about HBOC cancer risk, and 2) to examine the role of sentiment in predicting openness of communication. Our findings showed that the overall expressed sentiment was associated with the communication of genetic risk among HBOC families. This analysis identified additional factors that affect openness to communicate genetic risk. These were defined as “high-risk” factors and integrated into the design and development of the intervention. Study III describes the development of the intervention, a web-based platform designed for families with HBOC to share genetic test results. The platform was developed in line with the quality criteria set by the MRC framework. Being web-based, the platform could be accessed via a laptop, smartphone or tablet. Usability testing was applied to evaluate the prototype intervention which received high ratings on a satisfaction scale. Chapter 4 synthesises and discusses the key findings of all the studies presented in the previous chapter, and addresses study limitations and implications for future research