Confronting misinformation related to health and the environment comprises one of the major global concerns. Therefore, this systematic literature review, aims to identify the most used strategies to confront misinformation related to health, and the environment. The relevance of the interventions was assessed considering the frequency with which they are used and reported as effective. Five widely used databases were searched between 2010 and 2021 (Web of Science, Scopus, PsycINFO, Science Direct, IEEE Xplore). A total of 14.285 records were initially retrieved. Then, after applying inclusion and exclusion criteria, 32 peer-reviewed papers were included and analyzed in depth through this review. The results indicate that interventions based on credible information (debunking) were the most used among the included studies, followed by exposure and correction (debunking), inoculation, information, and media literacy (prebunking), and deliberation prompts (nudging). Most {interventions had }an effect size between small and medium, but most effects are limited to a specific myth/belief. We also found that most studies are conducted in the U.S. Therefore, experimental replication with same and different beliefs as outcomes and interventions cross-cultural adaptation to other countries are recommended.

The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.