#COVID19 and #Breastcancer: A Qualitative Analysis of Tweets

Naganathan, Gayathri; Bilgen, Idil; Cleland, Jordan; Reel, Emma; Cil, Tulin

doi:10.3390/curroncol29110669

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…A. T. collected an additional 368 Tweets via Tweet‐Chasing between 20 July and 2 August 2021. The final sample was 415 Tweets, comparable to the amount used in similar studies 8,30–32 …”

Section: Methodsmentioning

confidence: 99%

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult‐to‐treat depression

Talbot

Ford

Ryan

et al. 2023

Health Expectations

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IntroductionTreatment‐resistant depression (TRD) is depression unresponsive to antidepressants and affects 55% of British primary care users with depression. Current evidence is from secondary care, but long referral times mean general practitioners (GPs) manage TRD. Studies show that people with depression use Twitter to form community and document symptoms. However, Twitter remains a largely unexplored space of documented patient experience. Twitter data could provide valuable insights into learning about primary care experiences of TRD. In this study, we explored Twitter comments and conversations about TRD and produced patient‐driven recommendations.MethodsTweets from UK‐based users were collected manually and using a browser extension in June 2021. Conventional content analysis was used to provide an overview of the Tweets, followed by interpretation to understand why Twitter may be important to people with TRD.ResultsA total of 415 Tweets were organised into five clusters: self‐diagnosis, symptoms, support, small wins and condition experts. These Tweets were interpreted as showing Twitter as a community for people with TRD. People had a collective sense of illness identity and were united in their experiences of TRD. However, users in the community also highlighted the absence of effective GP care, leading users to position themselves as condition experts. Users shared advice from a place of lived experience with the community but also shared potentially harmful information, including recommendations about nonevidence‐based medications.ConclusionsFindings illuminate the benefits of the TRD Twitter community and also highlight that the perception of a lack of knowledge and support from GPs may lead community members to advise nonevidenced‐based medications.Patient and Public ContributionThis study was led by a person with lived experience of TRD and bipolar. Two public contributors with mental health conditions gave feedback on our study protocol and results.

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Section: Methodsmentioning

confidence: 99%

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult‐to‐treat depression

Talbot

Ford

Ryan

et al. 2023

Health Expectations

View full text Add to dashboard Cite

show abstract

“…We used qualitative content analysis to analyze tweets from 20 maternal health advocacy organizations on Twitter. We used qualitative content analysis instead of textual machine-learning analysis because the focus was not on emotional, sentimental analysis, or word count but on categorizing, interpreting, and analyzing themes generated from the data and their meanings [34]. The traditional qualitative content analysis allowed us to detect quotes and attributions suitable to support the themes.…”

Section: Methodsmentioning

confidence: 99%

Tweets That Matter: Exploring the Solutions to Maternal Mortality in the United States Discussed by Advocacy Organizations on Twitter

Aruah

Henshaw

Walsh-Childers

2023

IJERPH

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This study investigated maternal mortality solutions mentioned on Twitter by maternal health advocacy organizations in the United States. Using qualitative content analysis, we examined tweets from 20 advocacy organizations and found that the majority of the tweets focused on policy, healthcare, community, and individual solutions. The most tweeted policy solutions include tweets advocating signing birth equity, paid family leave, Medicaid expansion, and reproductive justice bills, whereas the most tweeted community solutions were funding community organizations, hiring community doulas, and building community health centers. The most tweeted individual solutions were storytelling, self-advocacy, and self-care. These findings provide insights into the perspectives and priorities of advocacy organizations working to address maternal mortality in the United States and can inform future efforts to combat this critical public health issue.

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“…Naganathan et al [ 10 ] performed a qualitative analysis of tweets related to breast cancer and COVID19. Their findings from the samplsize of 403 tweets showed that advocacy organizations tweeted the most about the topic and a majority of the tweets came from the USA.…”

Section: Related Literaturementioning

confidence: 99%

Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning

Rao

Singh

Prakashini

et al. 2023

Indian J Otolaryngol Head Neck Surg

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This study aims to investigate public sentiment on laryngeal cancer via tweets in 2022 using machine learning. We aimed to analyze the public sentiment about laryngeal cancer on Twitter last year. A novel dataset was created for the purpose of this study by scraping all tweets from 1st Jan 2022 that included the hashtags #throatcancer, #laryngealcancer, #supraglotticcancer, #glotticcancer, and #subglotticcancer in their text. After all tweets underwent a fourfold data cleaning process, they were analyzed using natural language processing and sentiment analysis techniques to classify tweets into positive, negative, or neutral categories and to identify common themes and topics related to laryngeal cancer. The study analyzed a corpus of 733 tweets related to laryngeal cancer. The sentiment analysis revealed that 53% of the tweets were neutral, 34% were positive, and 13% were negative. The most common themes identified in the tweets were treatment and therapy, risk factors, symptoms and diagnosis, prevention and awareness, and emotional impact. This study highlights the potential of social media platforms like Twitter as a valuable source of real-time, patient-generated data that can inform healthcare research and practice. Our findings suggest that while Twitter is a popular platform, the limited number of tweets related to laryngeal cancer indicates that a better strategy could be developed for online communication among netizens regarding the awareness of laryngeal cancer.

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#COVID19 and #Breastcancer: A Qualitative Analysis of Tweets

Cited by 12 publications

References 33 publications

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult‐to‐treat depression

#TreatmentResistantDepression: A qualitative content analysis of Tweets about difficult‐to‐treat depression

Tweets That Matter: Exploring the Solutions to Maternal Mortality in the United States Discussed by Advocacy Organizations on Twitter

Investigating Public Sentiment on Laryngeal Cancer in 2022 Using Machine Learning

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