Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke: A Topic Modeling Approach

Readability and topics of the German Health Web: Exploratory study and text analysis

BACKGROUND

The internet has become an increasingly important resource for health information, especially for lay people. However, the information found does not necessarily comply with the user's health literacy level. Therefore, it is vital to (1) identify prominent information providers, (2) quantify the readability of written health information, and (3) to analyze how different types of information sources are suited for people with differing health literacy levels.

OBJECTIVE

In previous work, we showed the use of a focused crawler to "capture" and describe a large sample of the "German Health Web", which we call the "Sampled German Health Web" (sGHW). It includes health-related web content of the three mostly German speaking countries Germany, Austria, and Switzerland, i.e. country-code top-level domains (ccTLDs) ".de", ".at" and ".ch". Based on the crawled data, we now provide a fully automated readability and vocabulary analysis of a subsample of the sGHW, an analysis of the sGHW's graph structure covering its size, its content providers and a ratio of public to private stakeholders. In addition, we apply Latent Dirichlet Allocation (LDA) to identify topics and themes within the sGHW.

METHODS

Important web sites were identified by applying PageRank on the sGHW's graph representation. LDA was used to discover topics within the top-ranked web sites. Next, a computer-based readability and vocabulary analysis was performed on each health-related web page. Flesch Reading Ease (FRE) and the 4th Vienna formula (WSTF) were used to assess the readability. Vocabulary was assessed by a specifically trained Support Vector Machine classifier.

RESULTS

In total, n = 14,193,743 health-related web pages were collected during the study period of 370 days. The resulting host-aggregated web graph comprises 231,733 nodes connected via 429,530 edges (network diameter = 25; average path length = 6.804; average degree = 1.854; modularity = 0.723). Among 3000 top-ranked pages (1000 per ccTLD according to PageRank), 18.50%(555/3000) belong to web sites from governmental or public institutions, 18.03% (541/3000) from nonprofit organizations, 54.03% (1621/3000) from private organizations, 4.07% (122/3000) from news agencies, 3.87% (116/3000) from pharmaceutical companies, 0.90% (27/3000) from private bloggers, and 0.60% (18/3000) are from others. LDA identified 50 topics, which we grouped into 11 themes: "Research & Science", "Illness & Injury", "The State", "Healthcare structures", "Diet & Food", "Medical Specialities", "Economy", "Food production", "Health communication", "Family" and "Other". The most prevalent themes were "Research & Science" and "Illness & Injury" accounting for 21.04% and 17.92% of all topics across all ccTLDs and provider types, respectively. Our readability analysis reveals that the majority of the collected web sites is structurally difficult or very difficult to read: 84.63% (2539/3000) scored a WSTF ≥ 12, 89.70% (2691/3000) scored a FRE ≤ 49. Moreover, our vocabulary analysis shows that 44.00% (1320/3000) web sites use vocabulary that is well suited for a lay audience.

CONCLUSIONS

We were able to identify major information hubs as well as topics and themes within the sGHW. Results indicate that the readability within the sGHW is low. As a consequence, patients may face barriers, even though the vocabulary used seems appropriate from a medical perspective. In future work, the authors intend to extend their analyses to identify trustworthy health information web sites.

The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust

The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.

Health Communication through Chinese Media on E-Cigarette: A Topic Modeling Approach

Background: Electronic cigarettes (e-cigarettes) have been a newsworthy topic in China. E-cigarettes are receiving greater consumer attention due to the rise of the Chinese e-cigarettes industry. In the past decade, e-cigarettes have been widely debated across the media, particularly their identity and their health effects. Objective: this study aims to (1) find the key topics in e-cigarette news and (2) provide suggestions for future media strategies to improve health communication. Method: We collected Chinese e-cigarettes news from 1 November 2015 to 31 October 2020, in the Huike (WiseSearch) database, using “e-cigarettes” (Chinese: “电子烟”) as the keyword. We used the Jieba package in python to perform the data cleaning process and the Dirichlet allocation (LDA) topic modeling method to generate major themes of the health communication through news content. Main finding: through an analysis of 1584 news articles on e-cigarettes, this paper finds 26 topics covered with 4 themes as regulations and control (n = 475, 30%), minor protection (n = 436, 27.5%), industry activities (n = 404, 25.5%), and health effects (n = 269, 17%). The peak and decline of the number of news articles are affected by time and related regulations. Conclusion: the main themes of Chinese news content on e-cigarettes are regulations and control, and minor protection. Newspapers should shoulder the responsibilities and play an important role in health communication with balanced coverage.

Categorising patient concerns using natural language processing techniques

OBJECTIVES

Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.

METHODS

Patient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.

RESULTS

The LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).

DISCUSSION

LDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.

CONCLUSION

Our findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

Who says what? Content and participation characteristics in an online depression community

BACKGROUND

An increasingly important source of informal help for people with depression are online depression communities. This study investigates the prevailing topics in an online depression community and how they are related to participation styles.

METHODS

A topic model with 26 topics of N = 16,291 posts and N = 71,543 comments of N = 20,037 users in a depression forum on Reddit was created using Latent Dirichlet allocation (LDA). The topics' proportions in the corpus were correlated with five participation measures, i.e. sum of scores, number of comments, posts to comments ratio, posting frequency, and word count.

RESULTS

The most common topics were Feelings, Motivation, The Community on Reddit, and Time. There were many significant, small to moderate correlations between topic proportions and participation style measures. The topics Feelings, Offering Support, and Small Talk generated a bigger response in the form of scores and comments. Talking about the past and relationships was more common in longer posts, whereas small talk, offering emotional support, and employing cognitive strategies was more readily found in short comments. Lower posting frequency was related to talking about feelings and romantic relationships.

LIMITATIONS

No information on users' demographics or mental health status was available. Topic modeling cannot capture elements of style and tone of text.

CONCLUSIONS

A wide spectrum of topics was uncovered in the topic modeling. Patterns in the correlations point to users with different participation styles preferring different topics. Results of this study can aid the development of online interventions for depression.

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

Background

Artificial intelligence (AI)–based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner.

Objective

The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research.

Methods

An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics.

Results

From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices.

Conclusions

The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.