How Health-Related Misinformation Spreads Across the Internet: Evidence for the "Typhoon Eye" Effect

Online health-related misinformation has become a major problem in society and in-depth research is needed to understand its propagation patterns and underlying mechanisms. This study proposes a psychological typhoon eye effect to understand how health-related misinformation spreads during the pandemic using two national studies. In Study 1, we collected online search data from the United States and China to explore the relationship between the physical distance from the epicenter and the spread of health-related misinformation. Two common pieces of health-related misinformation were examined: "Microwaves kill coronavirus" in the United States and "Taking a hot bath can prevent against COVID-19" in China. Our results indicated a "typhoon eye effect" in the spread of two actual pieces of health-related misinformation using online data from the United States and China. In Study 2, we fabricated a piece of health-related misinformation, "Wash Clothes with Salt Water to Block Infection," and measured the spread behavior and perceived credibility of the misinformation. Again, we observed a typhoon eye effect on the spread behavior as well as the perceived credibility of health-related misinformation among people with limited education. In addition, based on the stimulus-organism-response theory, perceived credibility could serve as a mediator in the relationship between physical distance from the epicenter and the spread of health-related misinformation. Our results highlight the importance of psychological approaches to understanding the propagation patterns of health-related misinformation. The present findings provide a new perspective for development of prevention and control strategies to reduce the spread of health-related misinformation during pandemics.

BlindDate recommender: A context-aware ontology-based dating recommendation platform

Online dating sites have become popular platforms for those individuals who utilise the Internet to develop a personal or romantic relationship. Unlike typical recommenders systems, which attempt to suggest items such as films, songs, books and so on. According to a user’s interests, dating recommender systems provide services that people can use to find potential romantic partners. Since these services have a higher expectancy of users, online dating sites are considering the introduction of recommender systems in order to build an improved dating network. Different kinds of techniques based on content-based, collaborative filtering or hybrid techniques exist. In this article, we introduce BlindDate recommender, a context-based platform that utilises semantic technologies to describe users’ preferences more precisely. We utilise DBPedia repositories to obtain information that is subsequently used to enrich a previously generated ontology model. The instances inserted into the ontology enable the matching algorithms that we have generated to identify potential matches between users. In order to validate the performance of the platform, we utilise a real-world data set that has produced relevant results enhancing the accuracy compared with other well-known approaches and identifying the discriminant parameters used in the dating domain. More specifically, the proposed approach attains 0.79, 0.8 and 0.55 in the I-Precision, I-Recall and I-F-measure, respectively, when employed in separate topics.