Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection

Have we found a solution for health misinformation? A ten-year systematic review of health misinformation literature 2013-2022

BACKGROUND

Health misinformation (HM) has emerged as a prominent social issue in recent years, driven by declining public trust, popularisation of digital media platforms and escalating public health crisis. Since the Covid-19 pandemic, HM has raised critical concerns due to its significant impacts on both individuals and society as a whole. A comprehensive understanding of HM and HM-related studies would be instrumental in identifying possible solutions to address HM and the associated challenges.

METHODS

Following the PRISMA procedure, 11,739 papers published from January 2013 to December 2022 were retrieved from five electronic databases, and 813 papers matching the inclusion criteria were retained for further analysis. This article critically reviewed HM-related studies, detailing the factors facilitating HM creation and dissemination, negative impacts of HM, solutions to HM, and research methods employed in those studies.

RESULTS

A growing number of studies have focused on HM since 2013. Results of this study highlight that trust plays a significant while latent role in the circuits of HM, facilitating the creation and dissemination of HM, exacerbating the negative impacts of HM and amplifying the difficulty in addressing HM.

CONCLUSION

For health authorities and governmental institutions, it is essential to systematically build public trust in order to reduce the probability of individuals acceptation of HM and to improve the effectiveness of misinformation correction. Future studies should pay more attention to the role of trust in how to address HM.

Automating fake news detection using PPCA and levy flight-based LSTM

In recent years, rumours and fake news are spreading widely and very rapidly all over the world. Such circumstances lead to the propagation and production of an inaccurate news article. Also, misinformation and fake news are increased by the user without proper verification. Hence, it is necessary to restrict the spreading of fake information on mass media and to promote confidence all over the world. For this purpose, this paper recognizes the detection of fake news in an effective manner. The proposed methodology in detecting fake news consists of four different phases namely the data pre-processing phase, feature reduction phase, feature extraction phase as well as the classification phase. During data pre-processing, the input data are pre-processed by employing tokenization, stop-words deletion as well as stemming. In the second phase, the features are reduced by employing PPCA to enhance accuracy. Then the extracted feature is provided to the classification phase where LSTM-LF algorithm is utilized to classify the news as fake or real optimally. Furthermore, this paper utilizes four different datasets namely the Buzzfeed dataset, GossipCop dataset, ISOT dataset as well as Politifact dataset for evaluation. The performance evaluation and the comparative analysis are conducted and the analysis reveals that the proposed approach provides better performances when compared to other fake detection-based approaches.

Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks, performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area.

An Analysis of the Deleterious Impact of the Infodemic during the COVID-19 Pandemic in Brazil: A Case Study Considering Possible Correlations with Socioeconomic Aspects of Brazilian Demography

Due to COVID-19, a huge amount of incorrect information has been disseminated on the internet, which may interfere with the disease’s advance. This study analyzes the behavior of the Brazilian population during the pandemic, employing queries of infodemic data searched on Google Trends and relating them to socioeconomic and political indicators in the country. The z-score technique was used to standardize the data; and for multivalued analysis, dendrograms and the Elbow method detected similar patterns among Brazilian states. The result was divided into three analyses. In the analysis of the research trend of infodemic terms, the themes “Prevention and Beliefs” and “Treatment” prevailed. In the exploratory analysis, socioeconomic indicators related to income and education, as well as government programs, showed no impact on infodemic searches; but the results suggest that the states that supported the Brazilian president in the 2018 election, where he obtained more than 50% of the votes, were the states that most searched for infodemic terms: a total of 46.58% more infodemic searches than in the other states. In the multivalued analysis, the socioeconomic indicators used showed similarities in the patterns, highlighting a cluster containing 77% of all Brazilian states. The study concludes that denial about the pandemic and the influence of political leadership can influence infodemic information searches, contributing to a disorganization in the control of disease control and prevention measures.

Online Information of COVID-19: Visibility and Characterization of Highest Positioned Websites by Google between March and April 2020-A Cross-Country Analysis

Background: The WHO has used the term “infodemic” to describe the vast amount of false and true information that was making it difficult for people to find reliable information when they needed it. The infodemic spreads faster than COVID-19 itself. The main objective of the study was to characterize and analyze content about COVID-19 returned by Google during the pandemic and compare it between countries. Methods: The study was conducted between 30 March and 27 April 2020. The information was searched through local Google websites using the “COVID-19”, “Coronavirus”, “SARS-CoV-2” and “fake news” keywords. The search was conducted in Australia, France, Germany, Italy, Poland, Singapore, Spain, UK and the USA. The total number of the analyzed webpages was 685. Results: The most frequent types were News websites 47% (324/685) and Governmental 19% (131/685) while the least were Health portals 2% (17/685) and Scientific journals 5% (35/635), p < 0.001. United States and Australia had the highest share of Governmental websites. There was a positive correlation between the amount of preventive information and a number of SARS-CoV-2 infections in countries. The higher the number of tests performed, the higher was the amount of information about prevention available online. Conclusions: Online information is usually available on news and government websites and refers to prevention. There were differences between countries in types of information available online. The highest positioned (the first 20) websites for COVID-19, Coronavirus and SARS-CoV-2 keywords returned by Google include true information.

Accuracy of health-related information regarding COVID-19 on Twitter during a global pandemic

This study was performed to analyze the accuracy of health-related information on Twitter during the coronavirus disease 2019 (COVID-19) pandemic. Authors queried Twitter on three dates for information regarding COVID-19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health-related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using χ ² analysis and Mann-Whitney U. A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7-38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234-14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health-related COVID-19 tweets inaccurate indicating that the public should not rely on COVID-19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact-checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.

Gender, education, and digital generations as determinants of attitudes toward health information for health workers in West Java, Indonesia

Health information is a commodity heavily sought by Indonesians because of the increasing consciousness of a healthy lifestyle. However, the circulation of health information is consistently disrupted by misinformation and disinformation, particularly on social media and chatting platforms such as WhatsApp. Identified misinformation and disinformation can be found on the official web page run by the Ministry of Communication and Information (https://trustpositif.kominfo.go.id/). Digital information exchange often involves health care workers; they are considered a credible source of health information. The purpose of this study was to delineate the attitudes of health care workers toward health information, determined by gender, educational attainment, and age differences. Health information in this study was information circulated on WhatsApp. We divided the age differences into four digital generations: baby boomers and Generations X, Y, and Z. We used the t-test and analysis of education and age differences when using the analysis of variance to demonstrate the differences among determinants factors of respondents—617 health care workers in West Java—in using WhatsApp when receiving and sharing health information. The results support that attitudes toward health information are determined by education attainment and differences in generation and that gender differences have no effect.

"Fake news" in dermatology. Results from an observational, cross-sectional study

BACKGROUND

Social networks have become a means for disseminating information on health-related matters.

OBJECTIVE

Describe the characteristics and analyze the accuracy of the dermatology content that is most often shared on the most popular social networks.

MATERIALS AND METHODS

The content most often shared on social networks (Facebook, Pinterest, Twitter, and Reddit) between March 2019 and March 2020 was analyzed using the keywords: acne, alopecia/hair loss, psoriasis, eczema, melanoma, skin cancer, rash, and rosacea. The total number of interactions, skin disease, topic, and origin was collected from each of the records. The content was analyzed and was categorized as precise, confusing, or imprecise based on the scientific evidence available.

RESULTS

A total of 385 websites were included. About 44.7% of the shared content was rated as imprecise, 20% as confusing, and 35.3% as precise. The records classified as imprecise obtained a higher mean number of interactions (P < 0.05). No differences were found in terms of the level of certainty and the dermatosis studied, whereas they did exist in relation to their topic and origin (P < 0.001). Of the contents classified as imprecise, the most frequent topic and origin were "alternative medicines" and "individual opinions, articles not affiliated with health institutions, nor peer reviewed," respectively.

CONCLUSIONS

The majority of the contents often shared on social networks are below acceptable quality standards. Strategies are needed to discredit imprecise information and promote the dissemination of evidence-based dermatology information.

Online Food Shopping: A Conceptual Analysis for Research Propositions

Shopping foods online is different from shopping other things online. To stimulate more thinking and enrich potential future research imagination, this paper reviews for online food shopping features, offers a commentary, and proposes future research directions. The propositions include the following: (1) The design and implementation of online food shopping (eco)systems should engage the consumers and other stakeholders to co-create collective and social values; (2) A better fit between technologies’ and food businesses’ natures could generate better applications for online food shopping; (3) A business model with sound finance systems becomes the core of a healthy online food ecosystem; (4) The interaction and transformation between online (virtual) and offline (virtual) food businesses determines the dynamic development of future food shopping.