Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

Bian, Jiang; Zhao, Yunpeng; Salloum, Ramzi G.; Guo, Yi; Wang, Mo; Prosperi, Mattia; Zhang, Hansi; Du, Xinsong; Ramirez-Diaz, Laura J; He, Zhe; Sun, Yuan

doi:10.2196/jmir.9266

Cited by 44 publications

(27 citation statements)

References 35 publications

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“…For RQ2, we found that 87 consumer topics (out of 122) are correlated with promotional information, suggesting that promotional health information on Twitter certainly has an impact on consumers' discussions, which is consistent with our previous study on Lynch syndrome [22].…”

Section: Discussionsupporting

confidence: 90%

“…We then categorized these tweets into either: (1) promotional information, or (2) consumers' discussions. Consistent with our previous findings [22], tweets that contain URLs are more likely to be promotional information, where the URLs are links to HPV-related news, research findings, and health promotion activities. We randomly annotated 100 tweets with URLs and found 95% are promotioinal information.…”

Section: Step 2: Rule-based Categorization Of the Tweetssupporting

confidence: 89%

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Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

Zhang

Wheldon

Dunn

et al. 2019

Journal of the American Medical Informatics Association

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ObjectivesTo test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MethodsWe used three Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers' discussions. We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). ResultsWe collected 2,846,495 tweets and analyzed 335,681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is "cervical cancer screening"; while in promotional tweets, the most popular topic is to increase awareness of "HPV causes cancer". 87 out of the 122 topics are correlated between promotional information and consumers' discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. 35 topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. ConclusionNot only mining Twitter to assess consumers' health behaviors can obtain results comparable to surveys but can yield additional insights via a theory-driven approach. Limitations exist; nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape. BACKGROUND and SIGNIFICANCEHuman papillomavirus (HPV) is the most common sexually transmitted disease (STD) in the United States (US) [1]. Although HPV infections are transient, persistent infection can lead to cancer. An estimated 33,700 new patients are diagnosed with HPV-associated cancers (e.g., anal, penile, cervical, and oral cancers) each year [2] in US. HPV vaccine is effective in preventing most of these HPV-related cancers for individuals in early age [3]. Nevertheless, in 2017, only 48.6% of US adolescents received recommended HPV vaccination series, and 65.5% received ≥1dose of the series [4]. HPV vaccination coverage also varies greatly by state. Only three states (i.e., District of Columbia: 91.9%, Rhode Island: 88.6%, and Massachusetts: 81.9%) have more than 80% coverage for the first dose, while the bottom three states (i.e., Kentucky: 49.6%, and Mississippi: 49.6%, Wyoming: 46.9%) have coverage rates less than 50% [4]. There is a huge public health needs to increase the awareness of HPV-related issues to promote HPV vaccination.To increase HPV vaccination initiation and coverage, we first need to understand factors that affect people's health behavior towards vaccination uptake. Recognized by the Integrated

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Section: Discussionsupporting

confidence: 90%

Section: Step 2: Rule-based Categorization Of the Tweetssupporting

confidence: 89%

Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

Zhang

Wheldon

Dunn

et al. 2019

Journal of the American Medical Informatics Association

Self Cite

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“…Preliminary results show that the best filters are based on the number of related posts a user sends. The profiling of Twitter's users to enhance tweet classification and relevance was already proposed as an open issue in [19].…”

Section: Using Social Network For Phsmentioning

confidence: 99%

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Aramburu

Berlanga²,

Lanza³

2020

IJERPH

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Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

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“…Text classification is a common task in natural language processing (NLP) and a building block for many complex NLP tasks. Text classification is the task of classifying an entire text by assigning it one or more predefined labels [1] with broad applications in the biomedical domain, including biomedical literature indexing [2,3], automatic diagnosis codes assignment [4,5], tweets classification for public health topics [6][7][8], patient safety reports classification [9], etc.…”

Section: Introductionmentioning

confidence: 99%

ML-Net: multi-label classification of biomedical texts with deep neural networks

Chen

Peng

et al. 2019

Journal of the American Medical Informatics Association

103

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Background:In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems. As an important task with broad applications in biomedicine such as assigning diagnosis codes, a number of different computational methods (e.g. training and combining binary classifiers for each label) have been proposed in recent years. However, many suffered from modest accuracy and efficiency, with only limited success in practical use. Methods:We propose ML-Net, a novel deep learning framework, for multi-label classification of biomedical texts. As an end-to-end system, ML-Net combines a label prediction network with an automated label count prediction mechanism to output an optimal set of labels by leveraging both predicted confidence score of each label and the contextual information in the target document. We evaluate ML-Net on three independent, publicly-available corpora in two kinds of text genres: biomedical literature and clinical notes. For evaluation, example-based measures such as precision, recall and f-measure are used. ML-Net is compared with several competitive machine learning baseline models.

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Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

Cited by 44 publications

References 35 publications

Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States

Social Media Multidimensional Analysis for Intelligent Health Surveillance

ML-Net: multi-label classification of biomedical texts with deep neural networks

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