Detecção e identificação de notícias falsas em redes sociais utilizando abordagem de ciência de redes

Abstract

This work aims to deepen and foster research on computational technological tools for combating fake news on social media platforms, utilizing complex networks and artificial intelligence. This research discusses the importance of addressing this issue and how to apply complex networks in the development of technological solutions to identify and combat the dissemination of false information. The research methodology involves in-depth data analysis and a study of computational tools involving artificial intelligence, complex networks, data processing, and literature reviews. For data analysis, information on fake news will be collected using the FakeNewsNet database, taking into account entity relationships and extracting relevant characteristics related to the problem. Finally, complex network techniques will be employed to model and classify fake news using machine learning. Regarding computational tools, techniques of artificial intelligence such as decision trees, random forests, and deep neural networks will be explored. Natural language processing approaches will also be considered to analyze the content of news articles and identify features that can indicate the veracity or falsity of information. The literature review will examine relevant studies and works related to the topic, seeking theoretical foundation and important references in the field of combating fake news. Initiatives aimed at raising awareness and educating the public about fake news will be explored as well. The issues raised by this research underscore the importance of developing and utilizing advanced technological tools to combat fake news and promote the dissemination of accurate and reliable information. The application of complex networks and artificial intelligence in this context contributes to a more efficient and systematic approach in identifying and combating fake news, with the goal of protecting society and preserving the integrity of information on social media platforms.