A systematic mapping on automatic classification of fake news in social media

Souza, João Victor de; Gomes, Jorão; Filho, Fernando Marques de Souza; Oliveira, Alessandreia Marta de; Souza, Jairo Francisco de

doi:10.1007/s13278-020-00659-2

Cited by 40 publications

(25 citation statements)

References 120 publications

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“…However, a new wave of studies has been exploring how to use social networks to classify topics (Himelboim et al 2017) or even identify users' roles and actions in political conversations (Santos and Maurer 2020;Cossu et al 2016). Topic models, such as the latent Dirichlet allocation (LDA) method, became the norm to extract the main subject of the texts (de Souza et al 2020). However, previous studies have identified that some of those methods, like LDA, require more lexicon and more corpus (Zhao et al 2011;Hong and Davison 2010;Ceron et al 2021).…”

Section: Work On Twittermentioning

confidence: 99%

“…Another approach was using a dictionary learningbased framework for topic modeling in social media (Kasiviswanathan et al 2011). Last, but important, LDA together text mining techniques became two of the most popular, if not the most ones, forms to identify topics nowadays (Blei et al 2003;de Souza et al 2020). On the other hand, tweets are compromised to only 280 characters, which limits their corpus, challenging to cluster topics using LDA, as shown in previous studies (Zhao et al 2011;Hong and Davison 2010).…”

Section: Work On Twittermentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 fake news diffusion across Latin America

Ceron

Sanseverino

de-Lima-Santos

et al. 2021

Soc. Netw. Anal. Min.

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Fact-checking verifies a multitude of claims and remains a promising solution to fight fake news. The spread of rumors, hoaxes, and conspiracy theories online is evident in times of crisis, when fake news ramped up across platforms, increasing fear and confusion among the population as seen in the COVID-19 pandemic. This article explores fact-checking initiatives in Latin America, using an original Markov-based computational method to cluster topics on tweets and identify their diffusion between different datasets. Drawing on a mixture of quantitative and qualitative methods, including time-series analysis, network analysis and in-depth close reading, our article proposes an in-depth tracing of COVID-related false information across the region, comparing if there is a pattern of behavior through the countries. We rely on the open Twitter application programming interface connection to gather data from public accounts of the six major fact-checking agencies in Latin America, namely Argentina (Chequeado), Brazil (Agência Lupa), Chile (Mala Espina Check), Colombia (Colombia Check from Consejo de Redacciín), Mexico (El Sabueso from Animal Polótico) and Venezuela (Efecto Cocuyo). In total, these profiles account for 102,379 tweets that were collected between January and July 2020. Our study offers insights into the dynamics of online information dissemination beyond the national level and demonstrates how politics intertwine with the health crisis in this period. Our method is capable of clustering topics in a period of overabundance of information, as we fight not only a pandemic but also an infodemic, evidentiating opportunities to understand and slow the spread of false information.

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Section: Work On Twittermentioning

confidence: 99%

Section: Work On Twittermentioning

confidence: 99%

COVID-19 fake news diffusion across Latin America

Ceron

Sanseverino

de-Lima-Santos

et al. 2021

Soc. Netw. Anal. Min.

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show abstract

“…De Souza et al [26] reviewed the different types of features related to fake news detection methods and data sets, and they considered that SA was a useful feature to quickly verify the accuracy of information on social media. Finally, Antonakaki et al [27] recently presented a survey on current research topics in Twitter, determining that sentiment analysis was one of the four main branches of research involving Twitter and that one of the major threats for this social network is the dissemination of fake news through it.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, we follow the approach used in other reviews on fake news published in scientific journals in the last two years [4,6,19,23,25]. The other possibility would have been to follow the approach, less common in computer science than in other disciplines such as those related to healthcare, of carrying out a systematic review according to some guidelines, as it was the case of [18,20,26].…”

Section: Sentiment Analysis For Fake News Detectionmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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“…This can help companies by providing more interest or information about their users. Therefore, an increasing number of scholars are studying the field of text topic extraction, which has become one of the most essential and fundamental technologies in natural language processing (NLP) and is widely used in emergency situations handling (Kejriwal and Zhou 2020;Interdonato et al 2019), the news (de Souza et al 2020;Park et al 2020), product review analysis (Santos et al 2020), and other aspects.…”

Section: Introductionmentioning

confidence: 99%

Topics extraction in incremental short texts based on LSTM

Zhang

2020

Soc. Netw. Anal. Min.

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With the development of online social media, the topic extraction of short text has become an important research field. How to extract the topic, especially new topics that have not been recognized, from increasing and updated short texts has attracted the attention of scholars. This paper focuses on constructing a system based on long short-term memory (LSTM) model in deep learning. Firstly, the short text is converted to a word vector matrix by the word2vec model. After that, two models based on LSTM were designed. One is used to recognize whether the text belongs to an existing topic or a new one. The other identifies whether two text samples belong to the same topic or not. Finally, a hierarchical clustering model is used to find the number of new topics based on the output information of the two LSTM models. The experimental results show that the system constructed in this paper can identify new text topics well and achieve good algorithm performance.

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A systematic mapping on automatic classification of fake news in social media

Cited by 40 publications

References 120 publications

COVID-19 fake news diffusion across Latin America

COVID-19 fake news diffusion across Latin America

Sentiment Analysis for Fake News Detection

Topics extraction in incremental short texts based on LSTM

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