Search Results (5)

The nuclear accident that accompanied the Great East Japan Earthquake of 11 March, 2011, was also an information disaster. A serious problem that arose after the accident and persisted for a long time was the damage caused by harmful rumors (DCBHR). In 2016, a cross-sectional questionnaire survey on health and information was conducted in Fukushima. The eligible population of this survey was 2000 Fukushima residents, which included those in the evacuated areas. We received 861 responses. Data were analyzed using the responses to the question about perceived DCBHR as the objective variable and the sources of information residents trusted and the media they used as explanatory variables. A multiple logistic regression analysis revealed that those who trusted government ministries and local commercial TV were significantly associated with no effect. In contrast, those who used Internet sites and blogs were significantly associated with a negative effect. This study underlines the pivotal importance of media and information, literacy, and education and discusses how these should be improved to avoid DCBHR in the future. Furthermore, accurate information should be made available to all sections of the population to diminish DCBHR. Full article

(1) Background: Rumors are a special type of information. Based on the classic theory of the communication of information, the “5W” communication model, this article aims to build a new model and thus explains the generation and communication of Internet health rumors. (2) Methods: The authors selected 50 Internet health rumors, which were widely spread in widely used websites and social media in China, then grounded theory is used to perform the qualitative analysis of the Internet health rumors. (3) Results: Three Core Concepts are abstracted after qualitative analysis. An internal dynamic mutual assistance mechanism of the communication of rumors is built and illustrated. Based on Lasswell’s “5W” communication model, the authors develop an eight-element communication model for Internet health rumors to illustrate the generation and communication of Internet health rumors. (4) Conclusions: By removing one or several elements of this new model, the chain of the communication of Internet health rumors could be cut off, which is valuable information for the government or websites to manage communication of Internet health rumors. Full article

Adolescents’ Internet health information usage has rarely been investigated. Adolescents seek all kinds of information from the Internet, including health information, which affects their Health Literacy that eHealth Literacy (eHL). This study is a retrospective observational study, we have total of 500 questionnaires were distributed, 87% of which were recovered, and we explored the channels that adolescents use to search for health information, their ability to identify false information, and factors affecting the type and content of health information queried. Adolescents believe that the Internet is a good means to seek health information because of its instant accessibility, frequent updating, convenience, and lack of time limits. More boys use the Internet to seek health information than girls in junior high schools (p = 0.009). The Internet is an important source of health information for adolescents but contains extensive misinformation that adolescents cannot identify. Additionally, adolescent boys and girls are interested in different health issues. Therefore, the government should implement measures to minimize misinformation on the Internet and create a healthy, educational online environment to promote Adolescents’ eHealth Literacy (eHL). Full article

The SARS-CoV-2 (COVID-19) pandemic devastated the world economy. Global infections and deaths altered the behaviors of generations. The Internet acted as an incredible vehicle for communication but was also a source of unfounded rumors. Unfortunately, this freedom of information sharing and fear of COVID-19 fostered unfounded claims about transmission (e.g., 5G networks spread the disease). With negligible enforcement to stop the spread of rumors and government officials spouting unfounded claims, falsities became ubiquitous. Organizations, public health officials, researchers, and businesses spent limited resources addressing rumors instead of implementing policies to overcome challenges (e.g., speaking to defiant mask wearers versus safe reopening actions). The researchers defined COVID-19 transmission misinformation as false beliefs about the spread and prevention of contracting the disease. Design and validation of the 12-item COVID-19 Transmission Misinformation Scale (CTMS) provides a measure to identify transmission misinformation believers. Indirect COVID-19 transmission misinformation beliefs with a fear of COVID-19 decreased wearing a mask in public intentions. Callousness exacerbated COVID-19 transmission misinformation beliefs as a moderator. Full article

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score. Full article