Analyzing Empowerment Processes Among Cancer Patients in an Online Community: A Text Mining Approach

Abstract Purpose This study aimed to explore the psychosocial impacts of the coronavirus disease (COVID-19) pandemic on cancer patients, survivors, and carers in Australia. Methods Using real-time insights from two Cancer Council NSW services—131120 Information and Support Line and Online Community (CCOC) forums—we assessed service demand trends, distress levels (using the distress thermometer), and content from 131120 calls and online posts between 01 December 2019 and 31 May 2020. Emergent themes were identified through an inductive conventional content analysis with 131120 call notes, followed by a deductive directed content analysis on CCOC posts. Results In total, 688 COVID-19-related 131120 calls (n = 496) and online posts (n = 192) were analysed. Service demand peaked in March 2020 and self-reported distress peaked in May 2020 at an average of 8/10 [Mean = 7.5; SD = 0.9]. Five themes emerged from the qualitative analysis: psychological distress and fear of virus susceptibility, practical issues, cancer service disruptions, information needs, and carer Issues. Conclusions The psychosocial impacts of COVID-19 on people affected by cancer are multifaceted and likely to have long-lasting consequences. Our findings drove the development of six recommendations across three domains of support, information, and access. Cancer patients, survivors, and carers already face stressful challenges dealing with a cancer diagnosis or survivorship. The added complexity of restrictions and uncertainty associated with the pandemic may compound this. It is important that healthcare providers are equipped to provide patient-centred care during and after this crisis. Our recommendations provide points of consideration to ensure care is tailored and patient oriented.

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Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

Healthcare ◽

10.3390/healthcare9091133 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1133

Author(s):

Jingfang Liu ◽

Jun Kong

Keyword(s):

Mental Health ◽

Text Mining ◽

Mental Disorders ◽

Empirical Analysis ◽

Online Community ◽

Information Quality ◽

Social Experience ◽

Adoption Model ◽

Health Communities ◽

Information Adoption

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

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Assessing the true nature of unplanned cancer care.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.30_suppl.183 ◽

2014 ◽

Vol 32 (30_suppl) ◽

pp. 183-183

Author(s):

Suzanne Tamang ◽

Manali I. Patel ◽

Sam Finlayson ◽

Xuemei Chen ◽

Julie Lawrence Kuznetsov ◽

...

Keyword(s):

Text Mining ◽

Cancer Patients ◽

Cancer Care ◽

Clinical Effectiveness ◽

Free Text ◽

Breast Cancer Patients ◽

True Nature ◽

Clinical Text ◽

Additional Information ◽

One Year

183 Background: Unplanned care can result in poor outcomes that are potentially preventable. The design of effective interventions to improve outcomes for cancer patients requires a better understanding of the true nature of unplanned care. Although cancer care teams document each patient’s care trajectory in detailed free-text notes, care outcomes are typically measured from structured patient record data and do not contain key information necessary for quality improvement efforts, such as the etiology of emergent events, or events that occur at outside facilities. To inform clinical effectiveness work at Stanford’s Cancer Institute, we describe our application of text-mining to improve the assessment of post-diagnosis morbidity outcomes. Methods: We conducted a retrospective study of unplanned care among 3,318 patients with a new diagnosis of breast, gastrointestinal, or thoracic cancer during 2010-13. Using a validated framework for clinical text-mining, we analyzed 308,000 notes for two tasks. First, we extract information on external unplanned events that are documented by providers. Second, we profile symptom mentions in Emergency Department (ED) notes. Results: For all cancer patients, text-mining detected over 400 unplanned events (93% PPV) at outside facilities, resulting in patient rates of 5% in the first 30 days, and 11% up to one year post-diagnosis. Among breast cancer patients, the top three symptoms reported in ED notes are pain (89%), nausea (37%) and fever (18%). Pain is consistently the most prevalent symptom up to one year after diagnosis, other symptoms exhibit more dynamic trends; wound related disorders and nausea are more prevalent among ED admissions in the first three months, whereas fever, cognitive impairment and mental health issues become more prevalent among admissions after the first three months of cancer care. Conclusions: The application of text-mining methods can improve the quantification of morbidity outcomes by improving the estimation of unplanned care rates and by providing continued learning for symptom-driven interventions to mitigate preventable emergent care. Although additional information gaps in care trajectories may continue to exist, text-mining can aid in assessing the true nature of unplanned care.

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Lessons on humanity: The voice of cancer patients through a literary contest.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e24189 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e24189-e24189

Author(s):

Darío Niewiadomski ◽

Ernesto Gil Deza ◽

Daniela Gercovich ◽

Lourdes Gil Deza ◽

Claudia Lorena Acuna ◽

...

Keyword(s):

Medical Education ◽

Text Mining ◽

Coping Strategies ◽

Head And Neck ◽

Cancer Patients ◽

Latin American ◽

Country Of Origin ◽

Literary Works ◽

The Voice

e24189 Background: Most cancer narratives are provided by skilled American and British writers (Broyard, Hitchens, Jobs, Kalanithi, Pausch, Sacks, Sontag or Wishart). This paper highlights the voice of ordinary Latin American patients who wrote about their experience. Methods: Between February 2, 2019 and March 3, 2019, the first author encouraged cancer patients to narrate their experience with the disease for an online literary contest (“ www.oncologiaesperanzadora.com ”). After studying all submissions, we grouped attitudes, feelings, metaphors and descriptions used in different domains related to the course of the disease. A text-mining study was conducted to identify the context of keywords in each of the domains. Results: In total, 224 literary works were submitted to the contest. Characteristics of the population: sex F/M: 184/40; age at time of contest: 53 yo (18-82); time between diagnosis and submission to contest: 8 y (1-73); country of origin: Argentina (201), other countries (23); diagnosis: breast (130), hematological (15), colorectal (13), melanoma (6), lung (5), head and neck (5), kidney (3), other (47). The table shows keywords found in each of the domains. Conclusions: 1) Cancer patients are willing to share their story if they are given the opportunity. 2) Through the narratives of the disease, it is possible to study its impact on patients’ lives and the different coping strategies. 3) Reading the experience of patients with the disease in their own voice is a valuable instrument for medical education. [Table: see text]

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BIOBOARD

Asia-Pacific Biotech News ◽

10.1142/s0219030315000671 ◽

2015 ◽

Vol 19 (10) ◽

pp. 53-63

Keyword(s):

United States ◽

Cancer Patients ◽

Online Community ◽

Genomic Analysis ◽

Disease Diagnosis ◽

Healthcare Systems ◽

Cancer Society ◽

International Research Team ◽

Tract Infections ◽

New Treatments

SINGAPORE – A*STAR Scientists Reveal How Stem Cells Defend Against Viruses: New Insights to the Mechanisms bring Broad Implications to Stem Cell Therapy and Disease Diagnosis SINGAPORE – Singtel - Singapore Cancer Society Race against Cancer 2015 SINGAPORE – NCCS and IMCB to Collaborate on Research for New Treatments to Benefit Cancer Patients SINGAPORE – MerLion’s Finafloxacin Shown to be More Efficacious than Ciprofloxacin in the Treatment of Complicated Urinary Tract Infections TAIWAN AND UNITED STATES – Professor Yuk-ling Yung Receives Gerard P. Kuiper Prize SWEDEN – New Data Confirm Tresiba® U200 Delivers Significantly Lower Rates of Confirmed Hypoglycaemia versus Insulin Glargine U100 UNITED KINGDOM – Using Ultrasound to Clean Medical Instruments THE NETHERLANDS & UNITED STATES – Philips and Dutch Radboud University Medical Centre Introduce First Diabetes Prototype App with Integrated Online Community to Empower Patients and Enhance Continuity of Care UNITED STATES – New Clinical Architecture Content Cloud Establishes Reliable Single Source for Terminology Updates, Makes It Easier to Keep Healthcare Systems Up-to-Date UNITED STATES – Genomic Analysis for All Cancer Patients UNITED STATES – Birds That Eat at Feeders Are More Likely to Get Sick, Spread Disease, International Research Team Says

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Exploring brand associations: an innovative methodological approach

European Journal of Marketing ◽

10.1108/ejm-12-2011-0770 ◽

2014 ◽

Vol 48 (5/6) ◽

pp. 1092-1112 ◽

Cited By ~ 24

Author(s):

Belinda Crawford Camiciottoli ◽

Silvia Ranfagni ◽

Simone Guercini

Keyword(s):

Text Mining ◽

Online Community ◽

Brand Management ◽

Methodological Approach ◽

Interdisciplinary Approach ◽

Computer Assisted ◽

Brand Associations ◽

Content Type ◽

Brand Association ◽

Brand Attributes

Purpose – The purpose of this exploratory study is to propose a new methodological approach to investigate brand associations. More specifically, the study aims to show how brand associations can be identified and analysed in an online community of international consumers of fashion to determine the degree of matching with company-defined brand associations. Design/methodology/approach – The methodology is two-pronged, integrating qualitative market research techniques with quantitative text mining. It was applied to determine types and perceptions of brand associations among fashion bloggers with reference to three leading Italian fashion houses. These were then compared to brand associations found in company-generated texts to measure the degree of matching. Findings – The results showed consistent brand associations across the three brands, as well as substantial matching with company-defined brand associations. In addition, the analysis revealed the presence of distinctive brand association themes that shed further light on how brand attributes were perceived by blog participants. Practical implications – The methods described can be used by managers to identify and reinforce favourable brand associations among consumers. This knowledge can then be applied towards developing and implementing effective brand strategies. Originality/value – The authors propose an interdisciplinary approach to investigate brand associations in online communities. It incorporates text mining and computer-assisted textual analysis as techniques borrowed from the field of linguistics which have thus far seen little application in marketing studies, but can nonetheless provide important insights for strategic brand management.

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Analysis of Users' Health Knowledge Requirement and Health Perception in Senior Online Community Based on Web Text Mining

Proceedings of the 2nd International Conference on Social Science, Public Health and Education (SSPHE 2018) ◽

10.2991/ssphe-18.2019.49 ◽

2019 ◽

Author(s):

Yuxing Qian ◽

Huayang Zhou ◽

Hao Li ◽

Meiling Ren ◽

Wenxuan Gui ◽

...

Keyword(s):

Text Mining ◽

Online Community ◽

Health Knowledge ◽

Health Perception ◽

Community Based ◽

Web Text Mining

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Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach

JMIR Mental Health ◽

10.2196/mental.7797 ◽

2017 ◽

Vol 4 (4) ◽

pp. e48 ◽

Cited By ~ 3

Author(s):

Adeline Abbe ◽

Bruno Falissard

Keyword(s):

Text Mining ◽

Online Social Networks ◽

Online Community ◽

Self Esteem ◽

Face To Face ◽

Online Health Communities ◽

Self Help ◽

Patient Profiles ◽

Mining Methods ◽

Health Communities

Background Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. Objective The aim of this study was to use text mining on material from an online forum exploring patients’ concerns about treatment (antidepressants and anxiolytics). Methods Concerns about treatment were collected from discussion titles in patients’ online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. Results The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients’ concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Conclusions Patients’ expression on the Internet is a potential additional resource in addressing patients’ concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

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Financial Distress of Cancer Patients and the Families: Analysis of Individual Consultations Data through Text Mining

Palliative Care Research ◽

10.2512/jspm.14.139 ◽

2019 ◽

Vol 14 (2) ◽

pp. 139-144

Author(s):

Masamitsu Kobayashi ◽

Hironobu Ikehara ◽

Ai Tomotaki ◽

Takuya Kenmi

Keyword(s):

Text Mining ◽

Cancer Patients ◽

Financial Distress

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