Avoiding misleading information: A study of complementary medicine online information for cancer patients

The quality of informational social support in online health communities: A content analysis of cancer-related discussions

Objective

Informational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.

Methods

A multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.

Results

The results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.

Conclusions

This study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.

A survey of uncover misleading and cyberbullying on social media for public health

Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

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

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integrative medicines (CAIMs) have been being touted as both preventative and curative. We conducted sentiment and emotion analysis with the intent of understanding CAIM content related to COVID-19 being generated on Twitter across 9 months.

METHODS

Tweets relating to CAIM and COVID-19 were extracted from the George Washington University Libraries Dataverse Coronavirus tweets dataset from March 03 to November 30, 2020. We trained and tested a machine learning classifier using a large, pre-labelled Twitter dataset, which was applied to predict the sentiment of each CAIM-related tweet, and we used a natural language processing package to identify the emotions based on the words contained in the tweets.

RESULTS

Our dataset included 28 713 English-language Tweets. The number of CAIM-related tweets during the study period peaked in May 2020, then dropped off sharply over the subsequent three months; the fewest CAIM-related tweets were collected during August 2020 and remained low for the remainder of the collection period. Most tweets (n = 15 612, 54%) were classified as positive, 31% were neutral (n = 8803) and 15% were classified as negative (n = 4298). The most frequent emotions expressed across tweets were trust, followed by fear, while surprise and disgust were the least frequent. Though volume of tweets decreased over the 9 months of the study, the expressed sentiments and emotions remained constant.

CONCLUSION

The results of this sentiment analysis enabled us to establish key CAIMs being discussed at the intersection of COVID-19 across a 9-month period on Twitter. Overall, the majority of our subset of tweets were positive, as were the emotions associated with the words found within them. This may be interpreted as public support for CAIM, however, further qualitative investigation is warranted. Such future directions may be used to combat misinformation and improve public health strategies surrounding the use of social media information.

Machine Learning in Detecting COVID-19 Misinformation on Twitter

Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of our daily lives. These social media platforms are effective tools for disseminating news, photos, and other types of information. In addition to the positives of the convenience of these platforms, they are often used for propagating malicious data or information. This misinformation may misguide users and even have dangerous impact on society’s culture, economics, and healthcare. The propagation of this enormous amount of misinformation is difficult to counter. Hence, the spread of misinformation related to the COVID-19 pandemic, and its treatment and vaccination may lead to severe challenges for each country’s frontline workers. Therefore, it is essential to build an effective machine-learning (ML) misinformation-detection model for identifying the misinformation regarding COVID-19. In this paper, we propose three effective misinformation detection models. The proposed models are long short-term memory (LSTM) networks, which is a special type of RNN; a multichannel convolutional neural network (MC-CNN); and k-nearest neighbors (KNN). Simulations were conducted to evaluate the performance of the proposed models in terms of various evaluation metrics. The proposed models obtained superior results to those from the literature.

Seeking or contributing? Evidence of knowledge sharing behaviours in promoting patients' perceived value of online health communities

BACKGROUND

Health knowledge, as an important resource of online health communities (OHCs), attracts users to engage in OHCs and improve the traffics within OHCs, thereby promoting the development of OHCs. Seeking and contributing health knowledge are basic activities in OHCs and are helpful for users to solve their health-related problems, improve their health conditions and thus influence their evaluation of OHCs (ie perceived value of OHCs). However, how do patients' health knowledge seeking and health knowledge contributing behaviours together with other factors influence their perceived value of OHCs? We still have little knowledge.

OBJECTIVE

In order to address the above gap, we root the current study in social cognitive theory and prior related literature on health knowledge sharing in OHCs and patients' perceived value. We treat health knowledge seeking and health knowledge contributing behaviours as behavioural factors and structural social capital as an environmental factor and explore their impacts on patients' perceived value of OHCs.

DESIGN

We have built a theoretical model composed of five hypotheses. We have designed a questionnaire composed of four key constructs and then collected data via an online survey.

SETTING AND PARTICIPANTS

We have distributed the questionnaire in two Chinese OHCs. We obtained a sample of 352 valid responses that were completed by patients having a variety of conditions.

RESULTS

The empirical results indicate that health knowledge seeking and health knowledge contributing have positive impacts on patients' perceived value of OHCs. The impact of health knowledge seeking on patients' perceived value of OHCs is greater than the impact of health knowledge contributing. In addition, structural social capital moderates the effects of health knowledge seeking and health knowledge contributing on patients' perceived value of OHCs. It weakens the effect of health knowledge seeking but enhances the effect of health knowledge contributing on patients' perceived value of OHCs.

CONCLUSIONS

These findings contribute to the literature on patients' perceived value of OHCs and on the role of structural social capital in OHCs. For OHC managers, they should provide their users more opportunities to seek or contribute health knowledge in their communities.

Attitudes, Provider and Treatment Selection of Complementary and Integrative Health among Individuals with Pain-Related Conditions

Complementary and integrative therapies are used by people to address many conditions, including pain-related conditions. There has been concern about the quality of online health information, including information pertaining to complementary and integrative health (CIH). In this qualitative interview study, we sought to investigate how individuals interact with CIH-related information online and how this might affect their subsequent behavior. We conducted semi-structured interviews with 14 individuals with chronic pain conditions. We report findings based on three main themes: individuals' beliefs about CIH; approach to CIH, including how people view provider information and personalize their CIH use strategy; and factors that affect trust in the information encountered. Overall, study participants believed there was value in CIH therapies and that treatments were effective. Many described experiences that had influenced their views of complementary therapies over time. We also found that individuals form impressions of CIH providers based on structural and personal characteristics, particularly cost and proximity, that are conveyed in information to which they are exposed. These findings have various implications. First, over time individuals with chronic pain conditions develop their own beliefs and attitudes, which play a role in their selection of providers and modalities relating to CIH. Health care providers should consider how people view information relating to, and make decisions about, CIH therapies and work collaboratively with patients to develop effective health management strategies. Information services should also consider patients' perspectives in developing websites and other informational materials.

Exploring the Factors Influencing Consumers to Voluntarily Reward Free Health Service Contributors in Online Health Communities: Empirical Study

BACKGROUND

Rewarding health knowledge and health service contributors with money is one possible approach for the sustainable provision of health knowledge and health services in online health communities (OHCs); however, the reasons why consumers voluntarily reward free health knowledge and health service contributors are still underinvestigated.

OBJECTIVE

This study aimed to address the abovementioned gap by exploring the factors influencing consumers' voluntary rewarding behaviors (VRBs) toward contributors of free health services in OHCs.

METHODS

On the basis of prior studies and the cognitive-experiential self-theory (CEST), we incorporated two health service content-related variables (ie, informational support and emotional support) and two interpersonal factors (ie, social norm compliance and social interaction) and built a proposed model. We crawled a dataset from a Chinese OHC for mental health, coded it, extracted nine variables, and tested the model with a negative binomial model.

RESULTS

The data sample included 2148 health-related questions and 12,133 answers. The empirical results indicated that the effects of informational support (β=.168; P<.001), emotional support (β=.463; P<.001), social norm compliance (β=.510; P<.001), and social interaction (β=.281; P<.001) were significant. The moderating effects of social interaction on informational support (β=.032; P=.02) and emotional support (β=-.086; P<.001) were significant. The moderating effect of social interaction on social norm compliance (β=.014; P=.38) was insignificant.

CONCLUSIONS

Informational support, emotional support, social norm compliance, and social interaction positively influence consumers to voluntarily reward free online health service contributors. Social interaction enhances the effect of informational support but weakens the effect of emotional support. This study contributes to the literature on knowledge sharing in OHCs by exploring the factors influencing consumers' VRBs toward free online health service contributors and contributes to the CEST literature by verifying that the effects of experiential and rational systems on individual behaviors can vary while external factors change.

Improving cancer survivors' e-health literacy via online health communities (OHCs): a social support perspective

PURPOSE

Cancer survivors should have adequate e-health literacy to help them better use online health information. Online health communities (OHCs) can offer cancer survivors different types of social support that can represent another resource to improve health outcomes. However, there is little knowledge of how these OHC are directly related to a cancer survivors' e-health literacy. This study explores how different types of social support in OHCs are associated with cancer survivors' e-health literacy.

METHODS

A questionnaire was developed to collect data from two Chinese OHCs used by cancer survivors. The questionnaire is composed of two parts: six sociodemographic variables (i.e., gender, age, city, education, tenure, and prior Internet experience), two scales for informational support behaviors (i.e., health knowledge seeking and provision of health knowledge), a measure of emotional support within such a setting, and a measure of e-health literacy. Based on 162 complete samples, we determined the measurement properties of the scales used, provided descriptive statistics on major sociodemographic variables and conducted bivariate and multivariable hierarchical regression.

RESULTS

For cancer survivors, females demonstrate higher levels of e-health literacy. Higher education level was related to higher e-health literacy. Health knowledge seeking, contributing to health knowledge, and emotional support were all positively associated with e-health literacy. The interaction effect between health knowledge and emotional support is positively associated with e-health literacy.

CONCLUSIONS

Informational support and emotional support, as two major subtypes of social support within resources available in OHCs, are positively associated with e-health literacy among cancer survivors.

IMPLICATIONS FOR CANCER SURVIVORS

Cancer survivors might benefit from an active strategy for improving personal e-health literacy that includes more active informational involvement and emotional support rather than a passive lurking through e-health information and seeking and reading postings in OHCs.