Breaking news: Unveiling a new dataset for Portuguese news classification and comparative analysis of approaches.

Every day thousands of news are published on the web and filtering tools can be used to extract knowledge on specific topics. The categorization of news into a predefined set of topics is a subject widely studied in the literature, however, most works are restricted to documents in English. In this work, we make two contributions.

A review of semi-supervised learning for text classification.

A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained.

Link Prediction Based on Stochastic Information Diffusion.

Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems. Although many LP techniques have been developed in the prior art, most of them consider only static structures of the underlying networks, rarely incorporating the network's information flow. Exploiting the impact of dynamic streams, such as information diffusion, is still an open research topic for LP.

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.

Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources.

Evaluating link prediction by diffusion processes in dynamic networks.

Link prediction (LP) permits to infer missing or future connections in a network. The network organization defines how information spreads through the nodes. In turn, the spreading may induce changes in the connections and speed up the network evolution.