Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis

Stens, Oleg; Weisman, Michael H.; Reuter, Katja

doi:10.2196/15623

Cited by 11 publications

(10 citation statements)

References 47 publications

(39 reference statements)

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“…Twitter has been highly popular amongst healthcare researchers, epidemiologists, medical practitioners, data scientists, and computer science researchers for studying, analyzing, modeling, and interpreting social media communications related to pandemics, epidemics, viruses, and diseases such as Ebola [27], E-Coli [28], Dengue [29], Human papillomavirus (HPV) [30], Middle East Respiratory Syndrome (MERS) [31], Measles [32], Zika virus [33], H1N1 [34], influenza-like illness [35], swine flu [36], flu [37], Cholera [38], Listeriosis [39], cancer [40], Liver Disease [41], Inflammatory Bowel Disease [42], kidney disease [43], lupus [44], Parkinson's [45], Diphtheria [46], and West Nile virus [47].…”

Section: Introductionmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a ‘neutral’ emotion. The other emotions—‘bad’, ‘good’, ‘terrible’, and ‘great’—were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021.

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Section: Introductionmentioning

confidence: 99%

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur

Han

2022

COVID

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“…There are additional studies underway for which the content analysis protocol has been published and final analyses are to soon follow [43]. A large study involving a social media content analysis of a web-based forum for a variety of chronic diseases, including SLE, indicated success in assessing patients' desire to participate in studies and insights regarding their needs and priorities [44]. Additional studies have been published on the use of social listening on a publicly accessible, web-based forum-PatientsLikeMe [20]-to access and quantify both the needs of patients with SLE and their responses to various treatments [45][46][47][48].…”

Section: Digital Health Interventions and Social Listeningmentioning

confidence: 99%

Listening to Patients With Lupus: Why Not Proactively Integrate the Internet as a Resource to Drive Improved Care?

Blackie¹,

Gualtieri²,

Kasturi³

2023

J Med Internet Res

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Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. It is characterized by a broad spectrum of manifestations, depending on the affected organs and the severity of the inflammation at the time of presentation. Despite improvements in management, treatments are required on a chronic, cyclical basis; have high potential for unpleasant side effects; and deliver variable efficacy. Patients require care from multiple specialists, which can be delivered simultaneously and sporadically. Our fragmented health care system further exacerbates the disconnect between intermittent medical care and the lived experiences of patients with SLE. The goals of this research are to (1) assess the current standard of care for patients with SLE through the review of medical literature, including clinical consensus guidelines and systematic reviews; (2) assess the lived experiences of patients with lupus through the review of peer-reviewed literature on social listening, structured interviews, and data available from the open-access digital health platform PatientsLikeMe; and (3) present the perspective that the medical community has an opportunity to acknowledge and review the use of digital health interventions (DHIs) with their patients. The results of this research indicate that patients are incorporating DHIs, such as the internet and social media platforms, as critical components of their care for even the most basic of support. Although patients with SLE are depending on this support to shape their care, it is not considered a primary source of care by clinicians. Integrating the voices of patients brings valuable dimension to understanding the lived experiences of patients with SLE and the impacts of mutually dependent patient needs as patients navigate the disease in daily life. The medical community has a meaningful opportunity to leverage and recommend existing DHIs, such as web-based community platforms and web-based patient registries, at every stage of the patient journey to help patients better manage their condition. This has the potential to proactively build patient trust and well-being, reduce the underreporting of symptoms, increase shared decision-making, inform and shape clinical guidelines and future research, and improve patient outcomes.

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“…Twitter has been highly popular amongst healthcare researchers, epidemiologists, medical practitioners, data scientists, and computer science researchers for studying, analyzing, modeling, and interpreting social media communications related to pandemics, epidemics, viruses, and diseases such as Ebola [7], E-Coli [8], Dengue [9], Human papillomavirus (HPV) [10], Middle East Respiratory Syndrome (MERS) [11], Measles [12], Zika virus [13], H1N1 [14], influenza-like illness [15], swine flu [16], flu [17], Cholera [18], COVID 2022, 2 1028 Listeriosis [19], cancer [20], Liver Disease [21], Inflammatory Bowel Disease [22], kidney disease [23], lupus [24], Parkinson's [25], Diphtheria [26], and West Nile virus [27]. The recent outbreaks of COVID-19 and MPox have served as "catalysts," leading to the usage of Twitter for sharing and exchange of information on diverse topics related to these respective viruses leading to the generation of tremendous amounts of Big Data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

Thakur¹

2023

Preprint

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Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

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Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis

Cited by 11 publications

References 47 publications

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Listening to Patients With Lupus: Why Not Proactively Integrate the Internet as a Resource to Drive Improved Care?

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

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