Challenges and Development in Malay Natural Language Processing

doi:10.31838/jcr.07.03.10

Cited by 3 publications

(1 citation statement)

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“…In addition, Malay language processing faces challenges due to lack of text corpora, POS taggers and other resources [31]. The texts were translated by Google Translator and verified by a linguistic expert, who corrected and improved some of the translations.…”

Section: Data Pre-processingmentioning

confidence: 99%

Covid-19 Infodemic – Understanding Content Features in Detecting Fake News Using a Machine Learning Approach

(Corresponding Author),

Zing,

Laporte

2023

MJCS

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The use of content features, particularly textual and linguistic for fake news detection is under-researched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study investigates a selection of content features such as word bigrams, part of speech distribution etc. to improve fake news detection. We performed a series of experiments on a new dataset gathered during the COVID-19 pandemic using Decision Tree, K-Nearest Neighbor, Logistic Regression, Support Vector Machine and Random Forest. Random Forest yielded the best results, followed closely by Support Vector Machine, across all setups. In general, both the textual and linguistic features were found to improve fake news detection when used separately, however, combining them into a single model did not improve the detection significantly. Differences were also noted between the use of bigrams and part of speech tags. The study shows that textual and linguistic features can be used successfully in detecting fake news using the traditional machine learning approach as opposed to deep learning.

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Section: Data Pre-processingmentioning

confidence: 99%