Understanding Melanoma Talk on Twitter: The Lessons Learned and Missed Opportunities

The New Media Landscape and Its Effects on Skin Cancer Diagnostics, Prognostics, and Prevention: Scoping Review

BACKGROUND

The wide availability of web-based sources, including social media (SM), has supported rapid, widespread dissemination of health information. This dissemination can be an asset during public health emergencies; however, it can also present challenges when the information is inaccurate or ill-informed. Of interest, many SM sources discuss cancer, specifically cutaneous melanoma and keratinocyte cancers (basal cell and squamous cell carcinoma).

OBJECTIVE

Through a comprehensive and scoping review of the literature, this study aims to gain an actionable perspective of the state of SM information regarding skin cancer diagnostics, prognostics, and prevention.

METHODS

We performed a scoping literature review to establish the relationship between SM and skin cancer. A literature search was conducted across MEDLINE, Embase, Cochrane Library, Web of Science, and Scopus from January 2000 to June 2023. The included studies discussed SM and its relationship to and effect on skin cancer.

RESULTS

Through the search, 1009 abstracts were initially identified, 188 received full-text review, and 112 met inclusion criteria. The included studies were divided into 7 groupings based on a publication's primary objective: misinformation (n=40, 36%), prevention campaign (n=19, 17%), engagement (n=16, 14%), research (n=12, 11%), education (n=11, 10%), demographics (n=10, 9%), and patient support (n=4, 3%), which were the most common identified themes.

CONCLUSIONS

Through this review, we gained a better understanding of the SM environment addressing skin cancer information, and we gained insight into the best practices by which SM could be used to positively influence the health care information ecosystem.

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis

Background

Asthma is a condition in which the airways become inflamed and constricted, causing breathing difficulties, wheezing, coughing, and chest tightness. Social networks can have a substantial effect on asthma management and results. However, no studies of social networks addressing asthma have been undertaken.

Objective

The aim of this research was to identify the significant social network structures, key influencers, top topics, and sentiments of asthma-related Twitter conversations.

Methods

All the tweets collected for this study included the keyword "asthma" or were mentioned in or in replies to tweets that were performed. For this study, a random sample of Twitter data was collected using NodeXL Pro software between December 1, 2022, and January 29, 2023. The data collected includes the user's display name, Twitter handle, tweet text, and the tweet's publishing date and time. After being imported into the Gephi application, the NodeXL data were then shown using the Fruchterman-Reingold layout method. In our study, SNA (Social Network Analysis) metrics were utilized to identify the most popular subject using hashtags, sentiment-related phrases (positive, negative, or neutral), and top influencer by centrality measures (degree, betweenness).

Results

The study collected 48,122 tweets containing the keyword "asthma" or mentioned in replies. News reporters and journalists emerged as top influencers based on centrality measures in Twitter conversations about asthma, followed by government and healthcare institutions. Education, trigger factors (e.g., cat exposure, diet), and associated conditions were highly discussed topics on asthma-related social media posts (e.g., sarscov2, copd). Our study's sentiment analysis revealed that there were 8427 phrases associated neutral comments (18%), 12,582 words reflecting positive viewpoints (26%), and 27,111 words reflecting negative opinions (56%).

Conclusion

This study investigates the relevance of social media influencers, news reporters, health experts, health organizations, and the government in the dissemination and promotion of asthma-related education and awareness during public health information.

The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis

Background

XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant.

Objectives

This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA).

Methods

This experiment involved the collection of Twitter data through the keywords, "XBB.1.5″, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral.

Results

A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments.

Conclusion

Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.

Current landscape of social media use pertaining to glioblastoma by various stakeholders

Background

Given the potential for social media to allow widespread public engagement, its role in healthcare, including in cancer care as a support network, is garnering interest. To date, the use of social media in neuro-oncology has not been systematically explored. In the current manuscript, we sought to review Twitter use on glioblastoma among patients, caregivers, providers, researchers, and other stakeholders.

Methods

The Twitter application programming interface (API) database was surveyed from inception to May 2022 to identify tweets about glioblastoma. Number of tweet likes, retweets, quotes, and total engagement were noted for each tweet. Geographic location, number of followers, and number of Tweets were noted for users. We also categorized Tweets based on their underlying themes. A natural language processing (NLP) algorithm was used to assign a polarity score, subjectivity score, and analysis label to each Tweet for sentiment analysis.

Results

A total of 1690 unique tweets from 1000 accounts were included in our analyses. The frequency of tweets increased from 2013 and peaked in 2018. The most common category among users was MD/researchers (21.6%, n = 216), followed by Media/News (20%, n = 200) and Business (10.7%); patients or caregivers accounted for only 4.7% (n = 47) while medical centers, journals, and foundations accounted for 5.4%, 3.7%, and 2.1%. The most common subjects that Tweets covered included research (54%), followed by personal experience (18.2%) and raising awareness (14%). In terms of sentiment, 43.6% of Tweets were classified as positive, 41.6% as neutral, and 14.9% as negative; a subset analysis of "personal experience" tweets revealed a higher proportion of negative Tweets (31.5%) and less neutral tweets (25%). Only media (β = 8.4; 95% CI [4.4, 12.4]) and follower count (minimally) predicted higher levels of Tweet engagement.

Conclusion

This comprehensive analysis of tweets on glioblastoma found that the academic community are the most common user group on Twitter. Sentiment analysis revealed that most negative tweets are related to personal experience. These analyses provide the basis for further work into supporting and developing the care of patients with glioblastoma.