Deciding among Fake, Satirical, Objective and Legitimate news

Morais, Janaína Ignácio de; Abonizio, Hugo Queiroz; Tavares, Gabriel Marques; Fonseca, André Azevedo da; Barbon, Sylvio

doi:10.1145/3330204.3330231

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Morais et al [26] proposed a Decision Support System (DSS) based on a multi-label text classification pipeline for news into two conceptual classes: objective/satirical and legitimate/fake. For this, the authors used a Portuguese dataset collected from Brazilian sites and considered the stylistic features from the news in DSS.…”

Section: Definitions and Related Workmentioning

confidence: 99%

“…Nonetheless, the work is limited to a Portuguese dataset with features proposed and evaluated only on the Portuguese language structure. Different from Reference [26], in this work, we considered different languages and evaluated the extracted features in a language-independent setup. Krishnan and Chen [27] and Sousa et al [28] focused on identifying fake news spread directly from social networks, specifically Twitter in those cases.…”

Section: Definitions and Related Workmentioning

confidence: 99%

“…The FNC does not include news written in PT. Thus, to overcome this gap, following the collecting method in Reference [26], we extracted news from two widely known Brazilian satirical portals: Sensacionalista and Diário Pernambucano. Therefore, the corpora used our work is backed both by literature and open source community datasets.…”

Section: News Datasetsmentioning

confidence: 99%

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Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

et al. 2020

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Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF.

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Section: Definitions and Related Workmentioning

confidence: 99%

Section: Definitions and Related Workmentioning

confidence: 99%

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Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

et al. 2020

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“…This paper is an extended version of a previous work [de Morais et al 2019], including the study of significant features groups and if it is possible to obtain the same result compared with the initial experiments using fewer features. The remainder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 85%

A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese

Morais

Abonizio

Tavares

et al. 2020

iSys

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Currently, there has been a significant increase in the diffusion of fake news worldwide, especially the political class, where the possible misinformation that can be propagated, appearing at the elections debates around the world. However, news with a recreational purpose, such as satirical news, is often confused with objective fake news. In this work, we decided to address the differences between objectivity and legitimacy of news documents, where each article is treated as belonging to two conceptual classes: objective/satirical and legitimate/fake. Therefore, we propose a DSS (Decision Support System) based on a Text Mining (TM) pipeline with a set of novel textual features using multi-label methods for classifying news articles on these two domains. For this, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared with a multi-class approach. Also, a set of real-life news data was collected from several Brazilian news portals for these experiments. Results obtained reported our DSS as adequate (0.80 f1-score) when addressing the scenario of misleading news, challenging the multi-label perspective, where the multi-class methods (0.01 f1-score) overcome by the proposed method. Moreover, it was analyzed how each stylometric features group used in the experiments influences the result aiming to discover if a particular group is more relevant than others. As a result, it was noted that the complexity group of features could be more relevant than others.

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“…No segundo grupo, os trabalhos são voltados para detecção de Fake News escritas na língua portuguesa. Em [de Morais et al 2019] e [Durier and Garcia 2019]é levada em conta apenas a classificação gramatical das palavras presentes nos textos das notícias. Por meio de outra abordagem, o método proposto em [Faustini and Covões 2019] utiliza apenas a análise de polaridade.…”

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Um Método Linguístico que combina Polaridade, Emoção e Aspectos Gramaticais para Detecção de Fake News em Inglês

Testoni¹,

Souza²,

Freire³

et al. 2021

Anais Do X Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2021)

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O uso crescente dos meios digitais aumentou a proliferação de Fake News. Métodos baseados na abordagem linguística que consideram o sentimento (i.e. polaridade ou emoções) presente nos textos das notícias têm mostrado resultados promissores para detectar, automaticamente, esse tipo de notícia nos meios digitais. Embora as emoções nos textos possam fornecer informações significativas para identificar Fake News, esses métodos eram restritos à extração de polaridade quando aplicados a notícias escritas em inglês. Assim, o presente trabalho propõe um protótipo baseado na abordagem linguística que considera a polaridade e as emoções para detectar Fake News escritas em inglês. O protótipo proposto foi adaptado de um método bem-sucedido, baseado no sentimento, que foi concebido e aplicado a notícias escritas em português. O protótipo proposto apresentou resultados promissores nos experimentos, superando os baselines em até 3,59 pontos percentuais.

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Deciding among Fake, Satirical, Objective and Legitimate news

Cited by 8 publications

References 26 publications

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese

Um Método Linguístico que combina Polaridade, Emoção e Aspectos Gramaticais para Detecção de Fake News em Inglês

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