A pattern-matched Twitter analysis of US cancer-patient sentiments

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning-Based Content Analysis Study

BACKGROUND

Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking.

OBJECTIVE

Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions.

METHODS

We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling.

RESULTS

We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention.

CONCLUSIONS

The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study's findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Use of Social Media Data Mining to Examine Needs, Concerns, and Experiences of People With Traumatic Brain Injury

PURPOSE

Given the limited availability of topic-specific resources, many people turn to anonymous social media platforms such as Reddit to seek information and connect to others with similar experiences and needs. Mining of such data can therefore identify unmet needs within the community and allow speech-language pathologists to incorporate clients' real-life insights into clinical practices.

METHOD

A mixed-method analysis was performed on 3,648 traumatic brain injury (TBI) subreddit posts created between 2013 and 2021. Sentiment analysis was used to determine the sentiment expressed in each post; topic modeling and qualitative content analysis were used to uncover the main topics discussed across posts. Subgroup analyses were conducted based on injury severity, chronicity, and whether the post was authored by a person with TBI or a close other.

RESULTS

There was no significant difference between the number of posts with positive sentiment and the number of posts with negative sentiment. Comparisons between subgroups showed significantly higher positive sentiment in posts by or about people with moderate-to-severe TBI (compared to mild TBI) and who were more than 1 month postinjury (compared to less than 1 month). Posts by close others had significantly higher positive sentiment than posts by people with TBI. Topic modeling identified three meta-themes: Recovery, Symptoms, and Medical Care. Qualitative content analysis further revealed that returning to productivity and life as well as sharing recovery tips were the primary focus under the Recovery theme. Symptom-related posts often discussed symptom management and validation of experiences. The Medical Care theme encompassed concerns regarding diagnosis, medication, and treatment.

CONCLUSIONS

Concerns and needs shift over time following TBI, and they extend beyond health and functioning to participation in meaningful daily activities. The findings can inform the development of tailored educational resources and rehabilitative approaches, facilitating recovery and community building for individuals with TBI.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24881340.

Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data

BACKGROUND

Twitter has become a popular platform for individuals to broadcast their daily experiences and opinions on a wide range of topics and emotions. Tweets from patients with cancer could offer insights into their needs. However, limited research has been conducted using Twitter data to understand the needs of patients with cancer despite the substantial amount of health-related data posted on the platform daily.

OBJECTIVE

This study aimed to uncover the potential of using Twitter data to understand the perspectives and experiences of patients with thyroid cancer at a global level.

METHODS

 This retrospective descriptive study collected tweets relevant to thyroid cancer in 2020 using the Twitter scraping tool. Only English-language tweets were included, and data preprocessing was performed to remove irrelevant tweets, duplicates, and retweets. Both tweets and Twitter users were manually classified into various groups based on the content. Each tweet underwent sentiment analysis and was classified as either positive, neutral, or negative.

RESULTS

A total of 13,135 tweets related to thyroid cancer were analyzed. The authors of the tweets included patients with thyroid cancer (3225 tweets, 24.6%), patient's families and friends (2449 tweets, 18.6%), medical journals and media (1733 tweets, 13.2%), health care professionals (1093 tweets, 8.3%), and medical health organizations (940 tweets, 7.2%), respectively. The most discussed topics related to living with cancer (3650 tweets, 27.8%), treatment (2891 tweets, 22%), diagnosis (1613 tweets, 12.3%), risk factors and prevention (1137 tweets, 8.7%), and research (953 tweets, 7.3%). An average of 36 tweets pertaining to thyroid cancer were posted daily. Notably, the release of a film addressing thyroid cancer and the public disclosure of a news reporter's personal diagnosis of thyroid cancer resulted in a significant escalation in the volume of tweets. From the sentiment analysis, 53.5% (7025/13,135) of tweets were classified as neutral statements and 32.7% (4299/13,135) of tweets expressed negative emotions. Tweets from patients with thyroid cancer had the highest proportion of negative emotion (1385/3225 tweets, 42.9%), particularly when discussing symptoms.

CONCLUSIONS

 This study provides new insights on using Twitter data as a valuable data source to understand the experiences of patients with thyroid cancer. Twitter may provide an opportunity to improve patient and physician engagement or apply as a potential research data source.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.

Toward an Extended Infodemiology Framework: Leveraging Social Media Data and Web Search Queries as Digital Pulse on Cancer Communication

This study aims to extend the infodemiology framework by postulating that effective use of digital data sources for cancer communication should consider four components: (a) content: key topics that people are concerned with, (b) congruence: how interest in cancer topics differ between public posts (i.e., tweets) and private web searches, (c) context: the influence of the information environment, and (d) information conduits. We compared tweets (n = 36, 968) and Google web searches on breast, lung, and prostate cancer between the National Cancer Prevention Month and a non-cancer awareness month in 2018. There are three key findings. First, reliance on public tweets alone may result in lost opportunities to identify potential cancer misinformation detected from private web searches. Second, lung cancer tweets were most sensitive to external information environment - tweets became substantially pessimistic after the end of cancer awareness month. Finally, the cancer communication landscape was largely democratized, with no prominent conduits dominating conversations on Twitter.

Twitter Conversations About Pancreatic Cancer by Health Care Providers and the General Public: Thematic Analysis

Background

 There is a growing interest in the pattern of consumption of health-related information on social media platforms.

Objective

We evaluated the content of discussions around pancreatic cancer on Twitter to identify subtopics of greatest interest to health care providers and the general public. 

Methods

 We used an online analytical tool (Creation Pinpoint) to quantify Twitter mentions (tweets and retweets) related to pancreatic cancer between January 2018 and December 2019. Keywords, hashtags, word combinations, and phrases were used to identify mentions. Health care provider profiles were identified using machine learning and then verified by a human analyst. Remaining user profiles were classified as belonging to the general public. Data from conversations were stratified qualitatively into 5 domains: (1) prevention, (2) survivorship, (3) treatment, (4) research, and (5) policy. We compared the themes of conversations initiated by health care providers and the general public and analyzed the impact of the Pancreatic Cancer Awareness Month and announcements by public figures of pancreatic cancer diagnoses on the overall volume of conversations. 

Results

 Out of 1,258,028 mentions of pancreatic cancer, 313,668 unique mentions were classified into the 5 domains. We found that health care providers most commonly discussed pancreatic cancer research (10,640/27,031 mentions, 39.4%), while the general public most commonly discussed treatment (154,484/307,449 mentions, 50.2%). Health care providers were found to be more likely to initiate conversations related to research (odds ratio [OR] 1.75, 95% CI 1.70-1.79, P<.001) and prevention (OR 1.49, 95% CI 1.41-1.57, P<.001) whereas the general public took the lead in the domains of treatment (OR 1.63, 95% CI 1.58-1.69, P<.001) and survivorship (OR 1.17, 95% CI 1.13-1.21, P<.001). Pancreatic Cancer Awareness Month did not increase the number of mentions by health care providers in any of the 5 domains, but general public mentions increased temporarily in all domains except prevention and policy. Health care provider mentions did not increase with announcements by public figures of pancreatic cancer diagnoses. After Alex Trebek, host of the television show Jeopardy, received his diagnosis, general public mentions of survivorship increased, while Justice Ruth Bader Ginsburg’s diagnosis increased conversations on treatment. 

Conclusions

Health care provider conversations on Twitter are not aligned with the general public. Pancreatic Cancer Awareness Month temporarily increased general public conversations about treatment, research, and survivorship, but not prevention or policy. Future studies are needed to understand how conversations on social media platforms can be leveraged to increase health care awareness among the general public.

Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia

The sustainability of human existence is in dire danger and this threat applies to our environment, societies, and economies. Smartization of cities and societies has the potential to unite individuals and nations towards sustainability as it requires engaging with our environments, analyzing them, and making sustainable decisions regulated by triple bottom line (TBL). Poor healthcare systems affect individuals, societies, the planet, and economies. This paper proposes a data-driven artificial intelligence (AI) based approach called Musawah to automatically discover healthcare services that can be developed or co-created by various stakeholders using social media analysis. The case study focuses on cancer disease in Saudi Arabia using Twitter data in the Arabic language. Specifically, we discover 17 services using machine learning from Twitter data using the Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-services, namely, Prevention, Treatment, Psychological Support, Socioeconomic Sustainability, and Information Availability. Subsequently, we show the possibility of finding additional services by employing a topical search over the dataset and have discovered 42 additional services. We developed a software tool from scratch for this work that implements a complete machine learning pipeline using a dataset containing over 1.35 million tweets we curated during September–November 2021. Open service and value healthcare systems based on freely available information can revolutionize healthcare in manners similar to the open-source revolution by using information made available by the public, the government, third and fourth sectors, or others, allowing new forms of preventions, cures, treatments, and support structures.

#ColonCancer: Social Media Discussions About Colorectal Cancer During the COVID-19 Pandemic

PURPOSE

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Social media platforms such as Twitter are extensively used to communicate about cancer care, yet little is known about the role of these online platforms in promoting early detection or sharing the lived experiences of patients with CRC. This study tracked Twitter discussions about CRC and characterized participating users to better understand public communication and perceptions of CRC during the COVID-19 pandemic.

METHODS

Tweets containing references to CRC were collected from January 2020 to April 2021 using Twitter's Application Programming Interface. Account metadata was used to predict user demographic information and classify users as either organizations, individuals, clinicians, or influencers. We compared the number of impressions across users and analyzed the content of tweets using natural language processing models to identify prominent topics of discussion.

RESULTS

There were 72,229 unique CRC-related tweets by 31,170 users. Most users were male (66%) and older than 40 years (57%). Individuals accounted for most users (44%); organizations (35%); clinicians (19%); and influencers (2%). Influencers made the most median impressions (35,853). Organizations made the most overall impressions (1,067,189,613). Tweets contained the following topics: bereavement (20%), appeals for early detection (20%), research (17%), National Colorectal Cancer Awareness Month (15%), screening access (14%), and risk factors (14%).

CONCLUSION

Discussions about CRC largely focused on bereavement and early detection. Online coverage of National Colorectal Cancer Awareness Month and personal experiences with CRC effectively stimulated goal-oriented tweets about early detection. Our findings suggest that although Twitter is commonly used for communicating about CRC, partnering with influencers may be an effective strategy for improving communication of future public health recommendations related to CRC.

A Comprehensive Survey on Sentiment Analysis in Twitter Data

The literature scrutinizes on diverse techniques that are associated with sentiment analysis in twitter data. It reviews several research papers and states the significant analysis. Initially, the analysis depicts various schemes that are contributed in different papers. Subsequently, the analysis also focuses on various features and it also analyses the sentiment analysis in twitter data that is exploited in each paper. Furthermore, this paper provides the detailed study regarding the performance measures and maximum performance achievements in each contribution. Finally, it extends the various research issues which can be useful for the researchers to accomplish further research on sentiment analysis in twitter data.

Whose cancer? Visualising the distribution of mentions to cancer sites on instagram

This article presents a quantitative analysis of mentions to cancer on Instagram. Using thousands of images with cancer-related hashtags, we build several visualisations to capture their distribution. Source images are clustered by their visual traits and by the incidence, prevalence, and mortality of the cancer site they refer to. Our goal is three-fold: to provide a quantitative basis for future research on the representation of cancer online; to offer an interpretation of the sources of the imbalanced representation of the different cancer sites; and to motivate a debate on how that representation may affect patients and families.

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.

A comprehensive social media data processing and analytics architecture by using big data platforms: a case study of twitter flood-risk messages

The main objective of the article is to propose an advanced architecture and workflow based on Apache Hadoop and Apache Spark big data platforms. The primary purpose of the presented architecture is collecting, storing, processing, and analysing intensive data from social media streams. This paper presents how the proposed architecture and data workflow can be applied to analyse Tweets with a specific flood topic. The secondary objective, trying to describe the flood alert situation by using only Tweet messages and exploring the informative potential of such data is demonstrated as well. The predictive machine learning approach based on Bayes Theorem was utilized to classify flood and no flood messages. For this study, approximately 100,000 Twitter messages were processed and analysed. Messages were related to the flooding domain and collected over a period of 5 days (14 May - 18 May 2018). Spark application was developed to run data processing commands automatically and to generate the appropriate output data. Results confirmed the advantages of many well-known features of Spark and Hadoop in social media data processing. It was noted that such technologies are prepared to deal with social media data streams, but there are still challenges that one has to take into account. Based on the flood tweet analysis, it was observed that Twitter messages with some considerations are informative enough to be used to estimate general flood alert situations in particular regions. Text analysis techniques proved that Twitter messages contain valuable flood-spatial information.

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study

Background

Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users’ behavior.

Objective

This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication.

Methods

In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study.

Results

The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008).

Conclusions

Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression.

Look who's talking now: Cancer in primary care on Twitter. An observational study

Background

Twitter is a microblogging platform that helps share information. It is a dynamic tool that has been embraced by many user types including consumers and healthcare professionals (HCPs). Currently, there are no data on how cancer in primary care features on Twitter.

Aim

To explore the type of users and information shared about cancer in primary care on Twitter.

Design & setting

A descriptive exploratory study took place of publicly available Twitter data.

Method

Tweets were searched between July 2015 and June 2017 for ‘GP’, ‘general practice’, ‘primary care’, or ‘general practitioner’ in conjunction with ‘cancer’. A 20% random sample was coded for geographic location, user type, type of tweet, and theme. Tweet sentiment was analysed using R package sentimentr. Content that gained traction was compared by combining original tweets, retweets, favourites, and duration.

Results

There were a total of 3413 tweets from 1611 users in 44 countries. Consumers were the largest user group followed by health organisations, healthcare professionals, and the media. The most common theme across user types was diagnostic delay. Other themes that emerged included cancer screening, symptom awareness, and early diagnosis. Consumers published more negative tweets, particularly in relation to diagnostic delay. Health organisations focused on symptom awareness and screening. Over half of media tweets were stories that featured delayed diagnosis or screening.

Conclusion

A broad range of users engage with Twitter to share information about cancer in primary care. Content is different between user groups, but diagnostic delay and symptom awareness are common themes. Healthcare and professional organisations may need to consider approaches to counter negative messages about diagnostic delay.

Understanding the Public's Emotions about Cancer: Analysis of Social Media Data

Cancer survivors suffer from emotional distress, which varies depending on several factors. However, existing emotion management programs are insufficient and do not take into consideration all of the factors. Social media provides a platform for understanding the emotions of the public. The aim of this study was to explore the relationship between the public's emotions about cancer and factors affecting emotions using social media data. We used 321,339 posts on cancer and emotions relating to cancer extracted from 22 social media channels between 1 January 2014, and 30 June 2017. The factors affecting emotions were analyzed using association rule mining and social network analysis. Hope/gratitude was the most frequently mentioned emotion group on social media followed by fear/anxiety/overwhelmed, sadness/depression/loneliness/guilt, and anger/denial. Acute survival stage, treatment method, and breast cancer were associated with hope/gratitude. Early stage, gastrointestinal problems, fatigue/pain/fever, and pancreatic cancer were associated with fear/anxiety/overwhelmed. Surgery, hair loss/skin problems, and fatigue/pain/fever were associated with sadness/depression/loneliness/guilt. Acute survival stage and hair loss/skin problems were associated with anger/denial. We found that emotions concerning cancer differed depending on the cancer type, cancer stage, survival stage, treatment, and symptoms. These findings could guide the development of tailored emotional management programs for cancer survivors that meet the public's needs more effectively.

Head and Neck Cancer and Social Media: The Patient Experience and Cancer Survivorship

OBJECTIVES/HYPOTHESIS

To characterize the head and neck cancer patients' lived experiences with survivorship through Instagram and examine opportunities for health professionals to provide support and outreach specifically targeting these needs.

STUDY DESIGN

Descriptive observational study.

METHODS

We analyzed key head and neck cancer-related hashtags by querying medical and layman terminology. The top English-language posts for #headandneckcancer underwent further content examination using thematic analysis based in grounded theory for categorization for user engagement (determined by "likes" and comments), type of content, and category of the account that created the post. Of the survivorship posts by patients, the content of posts in top user accounts was further analyzed.

RESULTS

There were 11,600 Instagram posts on #headandneckcancer, 1,300 posts on #headandneckcancerawareness, 1,100 posts on #headandneckcancersurvivor, and several thousand posts for additional layman terms. The majority of posts were from patients (65%), with few from head and neck surgeons or medical organizations (26%). User engagement was primarily by nonmedical accounts (95%). Posts by patients discussed medical appointments and treatments (81%), managing treatment effects and symptoms (66%), and cancer screening and prevention (23%). Specific concerns included fatigue (53%), postsurgical cosmetic appearance (27%), and weight and nutrition (34%).

CONCLUSIONS

Our study suggests that Instagram accounts can be intimate records of the patient experience, and gaining a better understanding of the daily experience of survivorship may be critical for head and neck surgeons and other oncology providers to provide truly comprehensive cancer care.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:E1214-E1219, 2021.

Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter

BACKGROUND

Racial and ethnic minority groups often face worse patient experiences compared with the general population, which is directly related to poorer health outcomes within these minority populations. Evaluation of patient experience among racial and ethnic minority groups has been difficult due to lack of representation in traditional health care surveys.

OBJECTIVE

This study aims to assess the feasibility of Twitter for identifying racial and ethnic disparities in patient experience across the United States from 2013 to 2016.

METHODS

In total, 851,973 patient experience tweets with geographic location information from the United States were collected from 2013 to 2016. Patient experience tweets included discussions related to care received in a hospital, urgent care, or any other health institution. Ordinary least squares multiple regression was used to model patient experience sentiment and racial and ethnic groups over the 2013 to 2016 period and in relation to the implementation of the Patient Protection and Affordable Care Act (ACA) in 2014.

RESULTS

Racial and ethnic distribution of users on Twitter was highly correlated with population estimates from the United States Census Bureau's 5-year survey from 2016 (r²=0.99; P<.001). From 2013 to 2016, the average patient experience sentiment was highest for White patients, followed by Asian/Pacific Islander, Hispanic/Latino, and American Indian/Alaska Native patients. A reduction in negative patient experience sentiment on Twitter for all racial and ethnic groups was seen from 2013 to 2016. Twitter users who identified as Hispanic/Latino showed the greatest improvement in patient experience, with a 1.5 times greater increase (P<.001) than Twitter users who identified as White. Twitter users who identified as Black had the highest increase in patient experience postimplementation of the ACA (2014-2016) compared with preimplementation of the ACA (2013), and this change was 2.2 times (P<.001) greater than Twitter users who identified as White.

CONCLUSIONS

The ACA mandated the implementation of the measurement of patient experience of care delivery. Considering that quality assessment of care is required, Twitter may offer the ability to monitor patient experiences across diverse racial and ethnic groups and inform the evaluation of health policies like the ACA.

Conversations and Misconceptions About Chemotherapy in Arabic Tweets: Content Analysis

Background

Although chemotherapy was first introduced for the treatment of cancer more than 60 years ago, the public understanding and acceptance of chemotherapy is still debatable. To the best of our knowledge, no study has assessed the conversations and misconceptions about chemotherapy as a treatment for cancer on social media platforms among the Arabic-speaking populations.

Objective

The aim of this study was to assess the types of conversations and misconceptions that were shared on Twitter regarding chemotherapy as a treatment for cancer among the Arabic-speaking populations.

Methods

All Arabic tweets containing any of the representative set of keywords related to chemotherapy and written between May 1, 2017 and October 31, 2017 were retrieved. A manual content analysis was performed to identify the categories of the users, general themes of the tweets, and the common misconceptions about chemotherapy. A chi-square test for independence with adjusted residuals was used to assess the significant associations between the categories of the users and the themes of the tweets.

Results

A total of 402,157 tweets were retrieved, of which, we excluded 309,602 retweets and 62,651 irrelevant tweets. Therefore, 29,904 tweets were included in the final analysis. The majority of the tweets were posted by general users (25,774/29,904, 86.2%), followed by the relatives and friends of patients with cancer (1913/29,904, 6.4%). The tweets were classified into 9 themes; prayers and wishes for the well-being of patients undergoing chemotherapy was the most common theme (20,288/29,904, 67.8%), followed by misconceptions about chemotherapy (2084/29,904, 7.0%). There was a highly significant association between the category of the users and the themes of the tweets (χ²₄₀= 16904.4, P<.001).

Conclusions

Our findings support those of the previous infodemiology studies that Twitter is a valuable social media platform for assessing public conversations, discussions, and misconceptions about various health-related topics. The most prevalent theme of the tweets in our sample population was supportive messages for the patients undergoing chemotherapy, thereby suggesting that Twitter could play a role as a support mechanism for such patients. The second most prevalent theme of the tweets in our study was the various misconceptions about chemotherapy. The findings of our exploratory analysis can help physicians and health care organizations tailor educational efforts in the future to address different misconceptions about chemotherapy, thereby leading to increased public acceptance of chemotherapy as a suitable mode of treatment for cancer.

Examining Public Communication About Kidney Cancer on Twitter

PURPOSE

Several studies have described the content of Twitter conversations about lung, breast, and prostate cancer, but little is known about how the public uses Twitter to discuss kidney cancer. We sought to characterize the content of conversations on Twitter about kidney cancer and the participants engaged in these dialogues.

METHODS

This qualitative study analyzed the content of 2,097 tweets that contained the key words kidney cancer from August 1 to 22, 2017. Tweets were categorized by content domain of conversations related to kidney cancer on Twitter and user types of participants in these dialogues.

RESULTS

Among the 2,097 kidney cancer-related tweets analyzed, 858 (41.4%) were authored by individuals, 865 (41.2%) by organizations, and 364 (17.4%) by media sites. The most common content discussed was support (29.3%) and treatment (26.5%). Among the 2,097 tweets, 825 were unique tweets, and 1,272 were retweets. The most common unique tweets were about clinical trials (23.9%), most often authored by media sites. The most common retweets were about treatment (38.5%), most often authored by organizations.

CONCLUSION

Twitter dialogues about kidney cancer are most commonly related to support and treatment. Our findings provide insights that may inform the design of new interventions that use social media as a tool to improve communication of kidney cancer information. Additional efforts are needed to improve our understanding of the value and direct utility of social media in improving kidney cancer care.

Exploring Strategies for Using Social Media to Self-Manage Health Care When Living With and Beyond Breast Cancer: In-Depth Qualitative Study

Background

As breast cancer survival rates improve and structural health resources are increasingly being stretched, health providers require people living with and beyond breast cancer (LwBBC) to self-manage aspects of their care.

Objective

This study aimed to explore how women use and experience social media to self-manage their psychosocial needs and support self-management across the breast cancer continuum.

Methods

The experiences of 21 women (age range 27-64 years) were explored using an in-depth qualitative approach. The women varied in the duration of their experiences of LwBBC, which facilitated insights into how they evolve and change their self-management strategies over time. Semistructured interviews were analyzed inductively using a thematic analysis, a polytextual analysis, and voice-centered relational methods.

Results

The use of multiple social media platforms, such as YouTube, Facebook, WhatsApp, and Twitter, enabled women to self-manage aspects of their care by satisfying needs for timely, relevant, and appropriate support, by navigating identities disrupted by diagnosis and treatment and by allowing them to (re)gain a sense of control. Women described extending their everyday use of multiple platforms to self-manage their care. However, women experienced social media as both empowering and dislocating, as their engagement was impacted by their everyday experiences of LwBBC.

Conclusions

Health care professionals (HCPs) need to be more aware, and open to the possibilities, of women using multiple social media resources as self-management tools. It is important for HCPs to initiate value-free discussions and create the space necessary for women to share how social media resources support a tailored and timely self-managed approach to their unique psychosocial needs.

Text Mining in Big Data Analytics

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

What Is Missing for You to Be Happy? Comparison of the Pursuit of Happiness Among Cancer Patients, Informal Caregivers, and Healthy Individuals

CONTEXT

After cancer diagnosis, personal value priorities may change in a way that would transform such values and how life is perceived by cancer patients and their caregivers, including happiness and its pursuit.

OBJECTIVES

The objective of the study was to analyze and compare what cancer patients, informal caregivers, and healthy population believe that would make them happy.

METHODS

A qualitative content analysis was performed on the responses to a single question: "What is missing for you to be happy?" Narratives of cancer patients (n = 242, face-to-face interview), informal caregivers (n = 125, face-to-face interview), and healthy participants (n = 1,671, recruited through social media, online survey) were analyzed. Word clouds were created for each group of participants. Contents were identified and frequencies were compared among participants by means of chi-square and Fisher's exact tests.

RESULTS

Overall, participants were pursuing better health (n = 288, 14.1%), better interpersonal relationships (n = 456, 22.4%), money (n = 412, 20.2%), and work-related aspects (n = 481, 23.6%). Cancer patients and informal caregivers sought better health and cure more often than when compared to healthy people (P < 0.001). Among cancer patients, survivors' profile tended to be similar to that of the healthy population concerning what they need to be happy. Unexpectedly, "cure" (22.7%) was more frequent among participants with incurable cancer.

CONCLUSION

Regardless of the group they were in, participants sought happiness in what they considered to be important to their lives, but it was something they did not have at the time of the interview. Psychoeducational and cognitive-behavioral strategies focused on how to deal with life expectations among people facing cancer are awaited.

The Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and Ejaculatory Dysfunctions

INTRODUCTION

Twitter is a social network based on "tweets," short messages of up to 280 characters. Social media has been investigated in health care research to ascertain positive or negative feelings associated with several conditions but never in sexual medicine.

AIM

To assess perceptions related to erectile dysfunction (ED) and premature ejaculation (PE) among Twitter users.

METHODS

Data collection was performed on a daily basis between May 24-October 9, 2018 (138 days) via an automated script. Data collection was then performed after data cleaning. The statistical software R and the rtweet packages were used in both phases.

RESULTS

We collected 11,000 unique tweets for PE and 30,546 unique tweets for ED. After data cleaning, we analyzed 7,020 tweets on PE and 22,648 tweets on ED by analyzing the most recurring words and the clusters describing word associations. The most popular words for ED were "Treatment," "Health," and "Viagra," whereas "Sex," "Sexual," and "Cure" were the top 3 for PE. Word clusters suggest the presence of some recurring themes, such as medical terms being grouped together. Additionally, tweets reflect the general feelings triggered by specific events, such as pieces of news pertaining to sexual dysfunctions.

CLINICAL IMPLICATIONS

Tweets on sexual dysfunctions are posted every day, with more tweets on ED than on PE. Treatment is among the chief topics discussed for both conditions, although health concerns differ between PE and DE tweets.

STRENGTH AND LIMITATIONS

This is the first analysis conducted on Tweets in the field of andrology and sexual medicine. A significant number of tweets were collected and analyzed. However, quantitative assessment of the sentiment was not feasible.

CONCLUSION

Sexual dysfunctions are openly discussed on social media, and Twitter analysis could help understand the needs and interests of the general population on these themes. Sansone A, Cignarelli A, Ciocca G, et al. The Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and Ejaculatory Dysfunctions. Sex Med 2019;7:464-471.

The social dynamics of lung cancer talk on Twitter, Facebook and Macmillan.org.uk

People with lung cancer and others affected by the condition are using social media to share information and support, but little is known about how these behaviours vary between different platforms. To investigate this, we extracted posts from Twitter (using relevant hashtags), the Lung Cancer Support Group on Facebook and the Macmillan.org.uk lung cancer discussion forum for a single month. Interaction Process Analysis revealed that all three platforms were used more for giving than seeking information, opinion or suggestions. However, interaction types (including sentiment) varied between platforms, reflecting their digital architectures, user-base and inclusion of a moderator. For example, a higher percentage of information-seeking and sentiment marked the Macmillan.org.uk, compared with Twitter and the Facebook Group. Further analysis of the messages using a four-dimensional typology of social support revealed that emotional and informational support types were most prevalent on the Macmillan.org.uk forum, closely followed by the Facebook Group. Contrary to expectations, Twitter posts showed the most companionship support, reflecting the use of hashtags as user-generated signals of community belonging and interests. Qualitative analysis revealed an unanticipated sub-category of spiritual support, which featured uniquely in the Lung Cancer Support Group on Facebook. There was little evidence of trolling or stigma, although some users remarked that lung cancer was unfairly resourced compared with other cancers. These findings provide new insights about how people affected by lung cancer use social media and begin to elucidate the value of different platforms as channels for patient engagement and support, or as potential research data sources.

Using Social Media to Identify Consumers’ Sentiments towards Attributes of Health Insurance during Enrollment Season

This study aims to identify sentiments that consumers have about health insurance by analyzing what they discuss on Twitter. The objective was to use sentiment analysis to identify attitudes consumers express towards health insurance and health care providers. We used an Application Programming Interface to gather tweets from Twitter with the words “health insurance” or “health plan” during health insurance enrollment season in the United States in 2016‒2017. Word association was used to find words associated with “premium,” “access,” “network,” and “switch.” Sentiment analysis established which specific emotions were associated with insurance and medical providers, using the NRC Emotion Lexicon, identifying emotions. We identified that provider networks, prescription drug benefits, political preferences, and norms of other consumers matter. Consumers trust medical providers but they fear unexpected health events. The results suggest that there is a need for different algorithms to help consumers find the plans they want and need. Consumers buying health insurance in the Affordable Care Act marketplaces in the United States choose lower-cost plans with limited benefits, but at the same time express fear about unexpected health events and unanticipated costs. If we better understand the origin of the sentiments that drive consumers, we may be able to help them better navigate insurance plan options and insurers can better respond to their needs.

Plastic Surgery and Social Media: Examining Perceptions

BACKGROUND

Social media play an important role in plastic surgery, yet there are limited studies in the literature to guide plastic surgeons' social networking practices. To address this deficiency and provide further insight, the authors set out to investigate the public's attitude toward plastic surgery using Twitter, a popular social media platform. The authors examined a large body of messages (tweets) related to plastic surgery using novel techniques of natural language processing and sentiment analysis.

METHODS

The authors collected over 1 million tweets with the keywords "plastic," "cosmetic," "aesthetic," and "reconstruction" surgery spanning from 2012 to 2016 from the Twitter Gardenhose feed. Using hedonometrics, the authors extracted the average happiness/positivity (havg) of tweets and created word-shift graphs to determine the most influential words.

RESULTS

The positivity scores for keywords "plastic," "cosmetic," "aesthetic," and "reconstruction" surgery were 5.72, 6.00, 6.16, and 6.09, respectively. In relation to "plastic," keywords "cosmetic" and "aesthetic" were more positive because they lacked antagonistic words, such as "fake," "ugly," "bad," "fails," or "wrong." The keyword "reconstruction," however, was more positively associated than the term "plastic" because of an increase in positive words, such as "honor," "amazing," "successful," and "respect."

CONCLUSIONS

Tweets containing the term "plastic" surgery trended toward negativity, and may be explained by the increase in unfavorable, associative words. Conversely, related terms such as "aesthetic," "cosmetic," and "reconstruction" were more favorably regarded because of the lack of antagonistic words and the presence of supportive words. The authors' results are informative and may serve to guide plastic surgeons' social media practices.

An unsupervised and customizable misspelling generator for mining noisy health-related text sources

BACKGROUND

Data collection and extraction from noisy text sources such as social media typically rely on keyword-based searching/listening. However, health-related terms are often misspelled in such noisy text sources due to their complex morphology, resulting in the exclusion of relevant data for studies. In this paper, we present a customizable data-centric system that automatically generates common misspellings for complex health-related terms, which can improve the data collection process from noisy text sources.

MATERIALS AND METHODS

The spelling variant generator relies on a dense vector model learned from large, unlabeled text, which is used to find semantically close terms to the original/seed keyword, followed by the filtering of terms that are lexically dissimilar beyond a given threshold. The process is executed recursively, converging when no new terms similar (lexically and semantically) to the seed keyword are found. The weighting of intra-word character sequence similarities allows further problem-specific customization of the system.

RESULTS

On a dataset prepared for this study, our system outperforms the current state-of-the-art medication name variant generator with best F1-score of 0.69 and F14-score of 0.78. Extrinsic evaluation of the system on a set of cancer-related terms demonstrated an increase of over 67% in retrieval rate from Twitter posts when the generated variants are included.

DISCUSSION

Our proposed spelling variant generator has several advantages over past spelling variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low, as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable. While the base system is fully unsupervised, we show how supervision may be employed to adjust weights for task-specific customizations.

CONCLUSION

The performance and relative simplicity of our proposed approach make it a much-needed spelling variant generation resource for health-related text mining from noisy sources. The source code for the system has been made publicly available for research.

Lung Cancer Messages on Twitter: Content Analysis and Evaluation

PURPOSE

The aim of this project was to describe and evaluate the levels of lung cancer communication across the cancer prevention and control continuum for content posted to Twitter during a 10-day period (September 30 to October 9) in 2016.

METHODS

Descriptive and inferential statistics were used to identify relationships between tweet characteristics in lung cancer communication on Twitter and user-level data. Overall, 3,000 tweets published between September 30 and October 9 were assessed by a team of three coders. Lung cancer-specific tweets by user type (individuals, media, and organizations) were examined to identify content and structural message features. The study also assessed differences by user type in the use of hashtags, directed messages, health topic focus, and lung cancer-specific focus across the cancer control continuum.

RESULTS

Across the universe of lung cancer tweets, the majority of tweets focused on treatment and the use of pharmaceutical and research interventions, followed by awareness and prevention and risk topics. Among all lung cancer tweets, messages were most consistently tweeted by individual users, and personal behavioral mobilizing cues to action were rare.

CONCLUSIONS

Lung cancer advocates, as well as patient and medical advocacy organizations, with an interest in expanding the reach and effectiveness of social media efforts should monitor the topical nature of public tweets across the cancer continuum and consider integrating cues to action as a strategy to increase engagement and behavioral activation pertaining to lung cancer reduction efforts.

The Bigger Picture: Picking the Right Soap Box-Is it Possible to Connect with Different Audience Targets (Practitioners and Patients) from the Same Platform?

The use of Internet and social media has skyrocketed in the past decade. It did not take long until physicians realized that they could use social media as a tool for communication with patients and colleagues. Since then use of social media has exploded and the information that has become available for physicians and their patients is remarkable. In addition, because of the immediacy of the platform, messages that are incorrect or not desired can be rapidly promoted, whether deliberately or accidentally. To obtain the best use of social media, the right platform should be chosen, and this varies depending on the group one is trying to reach, and the message or visibility desired. In this article, we review the variety of options available to users.

Breaking International Barriers: #ColorectalSurgery Is #GlobalSurgery

Colorectal surgeons have lagged behind other professionals in the use of social media. Currently, Twitter is the most widely utilized social platform for professional purposes among them. Connection and contagion are the two key actions that, together with immediate feedback and quantifiable impact, favor the use of Twitter over other social networks. In early 2016, a group of colorectal surgeons launched the #colorectalsurgery hashtag and, in less than 1 year, the ecosystem has incorporated over 2,600 users that generated over 24,000 tweets and 100 million impressions. "Live-Tweeting" surgical conferences by attendees including institutional or society accounts have greatly contributed to the success of the initiative. However, there are some barriers to a more wide adoption of social media, such as misrepresentation of non-peer-reviewed data, challenges to intellectual property protection, or even damage to the professional image. Active engagement with the #colorectalsurgery community may result in benefits for the global surgery community through information sharing, social interactions, personal branding, and research.

Disease-specific hashtags and the creation of Twitter medical communities in hematology and oncology

Twitter is being increasingly used for information gathering and dissemination of ideas in both medical practice and scientific research. A major limitation to its use has been the surplus of available information and difficulty in categorizing that information into topics of individual interest. However, a Twitter feature known as the hashtag (#), which denotes a specific category or topic, helps in streamlining this wealth of information. The creation and adoption of disease-specific hashtags by healthcare stakeholders has led to a greater uniformity of medical discussions that can be retrieved and referenced at later time-points. As new disease-specific hashtags are created for hematologic and oncologic diseases, more users can connect across the world, even for the rarest of cancer subtypes. A major challenge for this emerging application will be the development of specific and easily identifiable hashtags over time to add more clarity, while still trying to grow Twitter users and expand its reach.

Social media and colorectal cancer: A systematic review of available resources

AIM

Social media (SM) can provide information and medical knowledge to patients. Our aim was to review the literature and web-based content on SM that is used by Colorectal Cancer (CRC) patients, as well as surgeons' interaction with SM.

METHOD

Studies published between 2006 and 2016 were assessed. We also assessed the impact of several hashtags on Twitter with a freeware (Symplur).

RESULTS

Nine studies were included assessing Twitter (78%), Forums/Cancer-survivor networks (33%), and Facebook (22%). Aims included use of SM by CRC patients (67%), cancer-specific usage of SM with different types of cancer (44%), content credibility (33%), and influence in CRC awareness (33%). Prevention was the most common information that CRC patients looked for, followed by treatment side-effects. Only 2% of CRC SM users are doctors. SM use by colorectal consultants was suboptimal. Only 38% of surgeons had a LinkedIn account (most with less than 50 connections), and 3% used Twitter. A steep increase of tweets was observed for searched Hashtags over time, which was more marked for #ColonCancer (+67%vs+38%, #Coloncancer vs #RectalCancer). Participants engaged with colon cancer increased by 85%, whereas rectal cancer ones increased by 29%. The hashtag '#RectalCancer' was mostly tweeted by colorectal surgeons. The official twitter account of American Society of Colorectal Surgeons (@fascrs_updates) was the most active account.

CONCLUSION

CRC patients and relatives are increasingly engaging with SM. CRC surgeons' participation is poor, but we confirm a trend toward a greater involvement. Most SM lack of authoritative validation and the quality of shared content still is largely anecdotic and not scientifically evidenced-based. However, SM may offer several advantages over conventional information sharing sources for CRC patients and surgeons, and create connections with mutual enrichment.