Fake news identification: a comparison of parts-of-speech and N-grams with neural networks

Stoick, Brandon; Snell, Nicholas; Straub, Jeremy

doi:10.1117/12.2521250

Cited by 6 publications

(3 citation statements)

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“…Apart from word-based features such as n-grams, syntactic features such as POS tags are also exploited to capture linguistic characteristics of texts. Stoick, Snell & Straub (2019) stated that previous linguistic work suggests part-of-speech and n-gram frequencies are often different between fake and real articles. He created two models and concluded that some aspects of the fake articles remained readily identifiable, even when the classifier was trained on a limited number of examples.…”

Section: Related Workmentioning

confidence: 99%

Using of n-grams from morphological tags for fake news classification

Kapusta

Drlík

Munk

2021

PeerJ Computer Science

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Research of the techniques for effective fake news detection has become very needed and attractive. These techniques have a background in many research disciplines, including morphological analysis. Several researchers stated that simple content-related n-grams and POS tagging had been proven insufficient for fake news classification. However, they did not realise any empirical research results, which could confirm these statements experimentally in the last decade. Considering this contradiction, the main aim of the paper is to experimentally evaluate the potential of the common use of n-grams and POS tags for the correct classification of fake and true news. The dataset of published fake or real news about the current Covid-19 pandemic was pre-processed using morphological analysis. As a result, n-grams of POS tags were prepared and further analysed. Three techniques based on POS tags were proposed and applied to different groups of n-grams in the pre-processing phase of fake news detection. The n-gram size was examined as the first. Subsequently, the most suitable depth of the decision trees for sufficient generalization was scoped. Finally, the performance measures of models based on the proposed techniques were compared with the standardised reference TF-IDF technique. The performance measures of the model like accuracy, precision, recall and f1-score are considered, together with the 10-fold cross-validation technique. Simultaneously, the question, whether the TF-IDF technique can be improved using POS tags was researched in detail. The results showed that the newly proposed techniques are comparable with the traditional TF-IDF technique. At the same time, it can be stated that the morphological analysis can improve the baseline TF-IDF technique. As a result, the performance measures of the model, precision for fake news and recall for real news, were statistically significantly improved.

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Section: Related Workmentioning

confidence: 99%

Using of n-grams from morphological tags for fake news classification

Kapusta

Drlík

Munk

2021

PeerJ Computer Science

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“…Another tool are POS Tags, assigning individual words to Parts-of-speech and returning their percentage share in the document [42]. The basic information about the grammar of the text and the presumed emotions of the author obtained in this way are supplemented with the knowledge acquired from SlangNet [43], Colloquial WordNet [44], SentiWordNet [45] and SentiStrength [46], returning, in turn, information about slang and colloquial expressions, indicating the author's sentiment and defining it as positive or negative.…”

Section: Psycholinguistic Featuresmentioning

confidence: 99%

Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

Choraś¹,

Demestichas²,

Giełczyk³

et al. 2021

Preprint

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Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.

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“…One group worked on extending a facial feature steganography technique that was previously proposed by Marella, Straub and Bernard [31]. A second group worked on creating a user interface for cybersecurity command decision making while a third group looked into deceptive content identification, based on prior work [32]- [34]. It is likely that at least one of the research option groups will end up publishing a conference paper on their work.…”

Section: Immersion Experiencementioning

confidence: 99%

Development of a HyFlex Defensive Security Course

Straub

2021 ASEE Virtual Annual Conference Content Access Proceedings

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Fake news identification: a comparison of parts-of-speech and N-grams with neural networks

Cited by 6 publications

References 2 publications

Using of n-grams from morphological tags for fake news classification

Using of n-grams from morphological tags for fake news classification

Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

Development of a HyFlex Defensive Security Course

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