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Mr. Nicollas Oliveira
Universidade Federal Fluminense (UFF)

Basic Info


Research Keywords & Expertise

0 Machine Learning
0 Fake News
0 Text Summarization
0 Natural language Processing (NLP)
0 Ontology Learning

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Short Biography

Nicollas Rodrigues de Oliveira is currently a PhD student in Telecommunications Engineering at Universidade Federal Fluminense. He got his master's degree in Telecommunications Engineering from Universidade Federal Fluminense (UFF) in 2020, and he graduated in Telecommunications Engineering at the same university in 2018. Between 2016 and 2018, he was a volunteer student, and later, he was a scholarship holder for a scientific initiation project.

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Review
Published: 18 January 2021 in Information
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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.

ACS Style

Nicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information 2021, 12, 38 .

AMA Style

Nicollas de Oliveira, Pedro Pisa, Martin Lopez, Dianne de Medeiros, Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information. 2021; 12 (1):38.

Chicago/Turabian Style

Nicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. 2021. "Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges." Information 12, no. 1: 38.

Conference paper
Published: 07 October 2020 in 2020 4th Conference on Cloud and Internet of Things (CIoT)
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Most of the knowledge achieved from research activities are available as computer-like unstructured data written in natural-language papers. Automatically retrieving and representing knowledge from natural-language papers as input for computer processing is complex and challenging. In this paper, we propose a novel syntactic-relationship approach based on natural language processing, efficiently applying clustering algorithms to generate knowledge taxonomies about specific domain texts automatically. The approach considers a cloud computing case study through the collection and analysis of a set of recent publications. To assess our proposal, we conducted a quantitative comparison between different clustering-intrinsic metrics. Results showed higher popularity and coverage of the present proposal than the state-of-the-art, especially when using hierarchical clustering. The differential of our proposal lies in building a well-informative representation of knowledge with only three-quarters of the original textual data, and without any ground truth labeling.

ACS Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M.F. Mattos. A Syntactic-Relationship Approach to Construct Well-Informative Knowledge Graphs Representation. 2020 4th Conference on Cloud and Internet of Things (CIoT) 2020, 75 -82.

AMA Style

Nicollas Oliveira, Dianne S. V. Medeiros, Diogo M.F. Mattos. A Syntactic-Relationship Approach to Construct Well-Informative Knowledge Graphs Representation. 2020 4th Conference on Cloud and Internet of Things (CIoT). 2020; ():75-82.

Chicago/Turabian Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M.F. Mattos. 2020. "A Syntactic-Relationship Approach to Construct Well-Informative Knowledge Graphs Representation." 2020 4th Conference on Cloud and Internet of Things (CIoT) , no. : 75-82.

Journal article
Published: 08 July 2020 in IEEE Signal Processing Letters
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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.

ACS Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters 2020, 27, 1250 -1254.

AMA Style

Nicollas Oliveira, Dianne S. V. Medeiros, Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters. 2020; 27 ():1250-1254.

Chicago/Turabian Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. 2020. "A Sensitive Stylistic Approach to Identify Fake News on Social Networking." IEEE Signal Processing Letters 27, no. : 1250-1254.

Conference paper
Published: 01 February 2020 in 2020 International Conference on Computing, Networking and Communications (ICNC)
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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.

ACS Style

Nicollas Oliveira; H. A. Lucio Reis; Natalia C. Fernandes; A. M. Carlos Bastos; S. V. Dianne Medeiros; Diogo Mattos. Natural Language Processing Characterization of Recurring Calls in Public Security Services. 2020 International Conference on Computing, Networking and Communications (ICNC) 2020, 1009 -1013.

AMA Style

Nicollas Oliveira, H. A. Lucio Reis, Natalia C. Fernandes, A. M. Carlos Bastos, S. V. Dianne Medeiros, Diogo Mattos. Natural Language Processing Characterization of Recurring Calls in Public Security Services. 2020 International Conference on Computing, Networking and Communications (ICNC). 2020; ():1009-1013.

Chicago/Turabian Style

Nicollas Oliveira; H. A. Lucio Reis; Natalia C. Fernandes; A. M. Carlos Bastos; S. V. Dianne Medeiros; Diogo Mattos. 2020. "Natural Language Processing Characterization of Recurring Calls in Public Security Services." 2020 International Conference on Computing, Networking and Communications (ICNC) , no. : 1009-1013.