ArticleJournal of medical Internet research2016
Web Conversations About Complementary and Alternative Medicines and Cancer: Content and Sentiment Analysis.
Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Defining Health Misinformation: Theoretical Concept Analysis.Journal of medical Internet research · 2026Review
- Internet health information-seeking behavior and the use of traditional and complementary medicine: the role of online engagement and perceived information usefulness and reliability.BMC complementary medicine and therapies · 2025Article
- Education about complementary and alternative medicine in cancer self-help groups by trained peers.BMC complementary medicine and therapies · 2024Observational
- Advertising Alternative Cancer Treatments and Approaches on Meta Social Media Platforms: Content Analysis.JMIR infodemiology · 2023Article
- The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms.Journal of medical Internet research · 2023Article
- What are the ways in which social media is used in the context of complementary and alternative medicine in the health and medical scholarly literature? a scoping review.BMC complementary medicine and therapies · 2023Article
- Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.BMC complementary medicine and therapies · 2022Article
- Assessing the Quality of Online Health Information About Breast Cancer from Chinese Language Websites: Quality Assessment Survey.JMIR cancer · 2021Article
- Biological, Chemical, and Nutritional Food Risks and Food Safety Issues From Italian Online Information Sources: Web Monitoring, Content Analysis, and Data Visualization.Journal of medical Internet research · 2020Article
- Web-Based Medical Information Searching by Chinese Patients With Breast Cancer and its Influence on Survival: Observational Study.Journal of medical Internet research · 2020Observational
- Reactions and countermeasures of medical oncologists towards the incoming COVID-19 pandemic: a WhatsApp messenger-based report from the Italian College of Chief Medical Oncologists.Ecancermedicalscience · 2020Article
- Diabetes on Twitter: A Sentiment Analysis.Journal of diabetes science and technology · 2019Article
- Characterizing Websites That Provide Information About Complementary and Integrative Health: Systematic Search and Evaluation of Five Domains.Interactive journal of medical research · 2018Article
- Non-conventional practice versus evidence-based medicine. A scientific and ethical analysis of the Italian regulation.Acta bio-medica : Atenei Parmensis · 2017Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
- Frequencies of Private Mentions and Sharing of Mammography and Breast Cancer Terms on Facebook: A Pilot Study.Journal of medical Internet research · 2017Article
- "Mommy Blogs" and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites.JMIR public health and surveillance · 2016Article
- Assessing the Viability of Social Media for Disseminating Evidence-Based Nutrition Practice Guideline Through Content Analysis of Twitter Messages and Health Professional Interviews: An Observational Study.Journal of medical Internet research · 2016Observational
- Counseling on Complementary Methods in the Treatment of Side Effects of Oncological Therapies: A Project of the Breast and Bowel Center Nahe at the Hospital Sankt Marienwoerth Bad Kreuznach.Integrative cancer therapiesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of complementary and alternative medicine (CAM) among cancer patients is widespread and mostly self-administrated. Today, one of the most relevant topics is the nondisclosure of CAM use to doctors. This general lack of communication exposes patients to dangerous behaviors and to less reliable information channels, such as the Web. The Italian context scarcely differs from this trend. Today, we are able to mine and analyze systematically the unstructured information available in the Web, to get an insight of people's opinions, beliefs, and rumors concerning health topics.
objectiveOur aim was to analyze Italian Web conversations about CAM, identifying the most relevant Web sources, therapies, and diseases and measure the related sentiment.
methodsData have been collected using the Web Intelligence tool ifMONITOR. The workflow consisted of 6 phases: (1) eligibility criteria definition for the ifMONITOR search profile; (2) creation of a CAM terminology database; (3) generic Web search and automatic filtering, the results have been manually revised to refine the search profile, and stored in the ifMONITOR database; (4) automatic classification using the CAM database terms; (5) selection of the final sample and manual sentiment analysis using a 1-5 score range; (6) manual indexing of the Web sources and CAM therapies type retrieved. Descriptive univariate statistics were computed for each item: absolute frequency, percentage, central tendency (mean sentiment score [MSS]), and variability (standard variation σ).
resultsOverall, 212 Web sources, 423 Web documents, and 868 opinions have been retrieved. The overall sentiment measured tends to a good score (3.6 of 5). Quite a high polarization in the opinions of the conversation partaking emerged from standard variation analysis (σ≥1). In total, 126 of 212 (59.4%) Web sources retrieved were nonhealth-related. Facebook (89; 21%) and Yahoo Answers (41; 9.7%) were the most relevant. In total, 94 CAM therapies have been retrieved. Most belong to the "biologically based therapies or nutrition" category: 339 of 868 opinions (39.1%), showing an MSS of 3.9 (σ=0.83). Within nutrition, "diets" collected 154 opinions (18.4%) with an MSS of 3.8 (σ=0.87); "food as CAM" overall collected 112 opinions (12.8%) with a MSS of 4 (σ=0.68). Excluding diets and food, the most discussed CAM therapy is the controversial Italian "Di Bella multitherapy" with 102 opinions (11.8%) with an MSS of 3.4 (σ=1.21). Breast cancer was the most mentioned disease: 81 opinions of 868.
conclusionsConversations about CAM and cancer are ubiquitous. There is a great concern about the biologically based therapies, perceived as harmless and useful, under-rating all risks related to dangerous interactions or malnutrition. Our results can be useful to doctors to be aware of the implications of these beliefs for the clinical practice. Web conversation exploitation could be a strategy to gain insights of people's perspective for other controversial topics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.