The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

Human inefficiency to distinguish between true and false facts poses fake news as a threat to logical truth, which deteriorates democracy, journalism, and credibility in governmental institutions. In this letter, we propose a computational-stylistic analysis based on natural language processing, efficiently applying machine learning algorithms to detect fake news in texts extracted from social media. The analysis considers news from Twitter, from which approximately 33,000 tweets were collected, assorted between real and proven false. In assessing the quality of detection, 86% accuracy, and 94% precision stand out even employing a dimensional reduction to one-sixth of the number of original features. Our approach introduces a minimum overhead, while it has the potential of providing a high confidence index on discriminating fake from real news.

The COVID-19 pandemic has highlighted the necessity for agile health services that enable reliable and secure information exchange, but achieving proper, private, and secure sharing of EMRs remains a challenge due to diverse data formats and fragmented records across multiple data silos, resulting in hindered coordination between healthcare teams, potential medical errors, and delays in patient care. While centralized EMR systems pose privacy risks and data format diversity complicates interoperability, blockchain technology offers a promising solution by providing decentralized storage, ensuring data integrity, enhancing access control, eliminating intermediaries, and increasing efficiency in healthcare. By focusing on a Brazilian case study, this paper explores the significance of EMR standards, security challenges, and blockchain-based approaches to promote interoperability and secure data sharing in the healthcare industry.

Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance of today’s cities and fostering the creation of smart cities. Texts in natural language often compose such data. Nevertheless, the inference of useful information from a linguistic-computational analysis of natural language data is an open challenge. In this paper, we propose a clustering method to analyze textual data employing the unsupervised machine learning algorithms k-means and hierarchical clustering. We assess different vector representation methods for text, similarity metrics, and the number of clusters that best matches the data. We evaluate the methods using a real database of a public record service of security occurrences. The results show that the k-means algorithm using Euclidean distance extracts non-trivial knowledge, reaching up to 93% accuracy in a set of test samples while identifying the 12 most prevalent occurrence patterns.