Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

Choraś, Michał; Demestichas, Konstantinos; Giełczyk, Agata; Herrero, Álvaro; Ksieniewicz, Paweł; Remoundou, Konstantina; Urda, Daniel; Woźniak, Michał

doi:10.1016/j.asoc.2020.107050

Cited by 91 publications

(31 citation statements)

References 41 publications

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“…Finally, to the best of our knowledge, there are only five references, namely, Refs. [28][29][30][31], which have introduced the SMS methodology for conducting literature review on fake news detection. In what follows, we describe each of these references in order to identify its advantages and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

“…The outcome of this SMS indicated that most studies focus on providing solutions to detect misinformation spread on social media platforms. In [29], the authors have presented a systematic mapping study related to fake news detection based machine learning techniques. The objective of the proposed SMS is analyzing the current state the research on machine learning techniques used to combat fake news phenomenon and identifying the main challenges and methodological gaps to motivate future research.…”

Section: Related Workmentioning

confidence: 99%

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Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Limam

Aqil

Yafooz

et al. 2021

Studies in Computational Intelligence

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This last decade, the amount of data exchanged on the Internet and more specifically on social media networks is growing exponentially. Fake News phenomenon has become a major problem threatening the credibility of these social networks. Machine Learning (ML) techniques represent a promising solution to deal with this issue. For that, several solutions and algorithms using Machine Learning have been proposed in literature in the recent time for detecting fake news generated by different digital media platforms. This chapter aims to conduct a systematic mapping study to analyze and synthesize studies concerning the utilization of machine learning techniques for detecting fake news. Therefore, a total number of 76 relevant papers published on this subject between 1 January 2010 and 30 June 2021 were carefully selected. The selected articles were classified and analyzed according to eight criteria: channel and year of publication, research type, study domain, study platform, study context, study category, feature, and machine learning techniques used to handle categorical data. The results showed that most of the selected papers use both features text/content and linguistic to design machine learning models. Furthermore, SVM technique, and Deep Neural Network (DNN) technique were the most binary classification algorithms used to combat fake news.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Limam

Aqil

Yafooz

et al. 2021

Studies in Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…The former ones are actually fake news, but the latter ones, even if are close to fake news, are characterized by the entirely different intentions of the author. As highlighted by [11], the difference between the two is very subtle. It is sometimes difficult even for people to distinguish between them, particularly those who do not have a particular sense of humor.…”

Section: Fake News Detectionmentioning

confidence: 99%

“…Among the latest techniques arising, there is the detection of non-common tokens, specific text elements, repeated words, question and exclamation marks, emoticons, etc. [11]. These are usually neglected during the traditional preprocessing phase of the text.…”

Section: Text Feature Selectionmentioning

confidence: 99%

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

Cuzzocrea¹,

Bosco²,

Maiorana³

et al. 2021

JVLC

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“…These studies aim to identify all relevant evidence on the topic/area or research question. Up to now, many such studies were published in Computer Science journals or conferences [48][49][50][51]. They are found to be useful for educational purposes as well, providing a good starting point for a PhD work [52].…”

Section: Systematic Reviewmentioning

confidence: 99%

Attraction Basins in Metaheuristics: A Systematic Mapping Study

2021

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Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction basins in the domain of metaheuristics. Objective: To identify research trends, potential issues, and proposed solutions on attraction basins in the field of metaheuristics. Research goals were inspired by the previous paper, published in 2021, where attraction basins were used to measure exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening, iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data extraction. Results: Attraction basins within discrete domains are understood far better than those within continuous domains. Attraction basins on dynamic problems have hardly been investigated. Multi-objective problems are investigated poorly in both domains, although slightly more often within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction basins and a general framework that would unite all different definitions/implementations used for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics reveal that the concept alone is poorly exploited, as well as identify open issues where researchers may improve their research.

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Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study

Cited by 91 publications

References 41 publications

Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

Attraction Basins in Metaheuristics: A Systematic Mapping Study

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