Hoax Analyzer for Indonesian News Using Deep Learning Models

Nayoga, Bagas Pradipabista; Adipradana, Ryan; Suryadi, Ryan; Suhartono, Derwin

doi:10.1016/j.procs.2021.01.059

Cited by 32 publications

(33 citation statements)

References 19 publications

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“…NN classifier showed the best outcome of 99.89 and 99.60% for F1-Score and Geometric-mean, respectively. Recently, [72] improved CNN to propose a novel method called Deep 1D-CNN. The proposed model achieves very good performance with 97.9% accuracy.…”

Section: The Most Efficient Methods For Misinformation Classification...mentioning

confidence: 99%

“…Among all methods there are ten machine learning algorithms, seven feature extraction techniques, and one NN method that showed the most efficiency in detecting Covid-19 misinformation. [72] implemented deep learning models to classify fake news and DNN models outperform out of all. [27] proposed semi-supervised neural network model to detect Covid-19 fake news that achieves a 0.95 F1 Score on CTF, outperforming the best baseline by 9%.…”

Section: Deep Neural Networkmentioning

confidence: 99%

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Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

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The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

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Section: The Most Efficient Methods For Misinformation Classification...mentioning

confidence: 99%

Section: Deep Neural Networkmentioning

confidence: 99%

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

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“…Because Chinese is gradually transformed from hieroglyphics, the symmetry shown in Chinese is even more unique than other languages. Chinese has perfect symmetry in text, phonetics, grammar, and rhetoric, which is also a big difference from other languages [ 10 ]. The overall shape of Chinese characters is square, and each word has a special meaning and a single syllable, which once again highlights the superiority of Chinese.…”

Section: Theoretical Analysis and Research Designmentioning

confidence: 99%

Couplet Analysis of Linguistic Topology Using Deep Neural Networks in Cognitive Linguistics

Zhu

Wang

Fang

2022

Computational Intelligence and Neuroscience

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The work reported here primarily aims to realize the automatic generation of couplets using the linguistic topology of deep neural network (DNN). First, the symmetry, topology, and cognitive linguistics of language are explored to lay a theoretical foundation for subsequent model establishment and analysis. Then, the recurrent neural network (RNN) is employed to build the Seq2Seq model, and Liweng’s Guide to Rhyme (an ancient Chinese enlightenment reading material to poetry creation) is imported into the Seq2Seq model as a basic corpus. Eventually, the entire system is implemented automatically on TensorFlow. The system undergoes tests of the five-character quatrain, the seven-character quatrain, the couplet, and the part-of-speech detection. Results demonstrate that both the first and the second lines of the couplet present an excellent correspondence regarding sentences and words. After some famous verses are entered, the second line of the couplet obtained is quite vivid and appropriate. Meanwhile, the results can be generated quickly and meet the requirements on rhyme and couplet matching. This model can input verses according to users’ own needs and generate the second line of the couplet quickly, showing good correspondence in words, part-of-speech, and sentence pattern. Because the couplet belongs to Chinese traditional culture, it has a strong local Chinese cultural flavor. The system designed based on computer technology can help people learn and experience the charm of couplets.

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“…Komponen P(A|B) adalah hasil probabilitas posterior berdasakan kondisi X. Sebaliknya P(B|A) merupakan probabilitas berdasarkan kondisi A. Kemudian P(A) merupakan probabilitas prior atau probablitas hipotesis. Sedangkan P(B) merupakan probabilitas B [12].…”

Section: 𝑃(𝐴|𝐵) = 𝑃(𝐵|𝐴)𝑃(𝐴) 𝑃(𝐵)unclassified

Text Mining Literature Review on Indonesian Social Media

Thenata

2021

JEPIN

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Era sekarang jumlah berita dari berbagai media sosial yang tersebar dalam waktu singkat dan kebutuhan masyarakat untuk mengkonsumsi berita dalam berbagai referensi dapat mempengaruhi kehidupan masyarakat. Hal ini menyebabkan data yang tersebar dapat dikumpulkan dan dimanfaatkan oleh pemerintah, pengusaha, analisis, ataupun peneliti untuk mengidentifikasi tren, mengembangkan bisnis, memprediksi perilaku pelanggan dan lain sebagainya. Pengumpulan data berita dari media sosial tersebut dapat menggunakan text mining yang melibatkan algoritma yakni Naive Bayes, K-NN, dan SVM. Namun, penggunaan algoritma pada studi kasus yang tidak sesuai dapat memberikan hasil yang tidak optimal. Oleh karena itu, penelitian ini akan menganalisis algoritma text mining yang diimplementasikan pada media sosial berbahasa Indonesia dengan memakai metode systematic literature review. Metode ini dimulai dengan melakukan tahap planning yang menetapkan pertanyaan penelitian, kata pencarian, sumber literatur digital, dan standard literatur. Dilanjutkan dengan tahap conducting yang memilih dan mencocokan standard literatur, serta ekstraksi data. Kemudian tahap reporting yang melakukan analisis hasil ekstraksi data sehingga bisa menemumkan informasi dan pengetahuan. Tolak ukur yang menjadi acuan untuk perbandingan yakni pengujian confusion matrix berupa accuracy, precision, dan recall. Adapun hasil dari penelitian ini ditemukan algoritma Naive Bayes memberikan hasil yang stabil tapi kurang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia. Sedangkan algortima K-NN dan SVM ditemukan memberikan hasil yang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia yang dibuktikan dengan accuracy (50%-98.13%), precision (58.22%-98.48%), dan recall (21.05%-98%).

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Hoax Analyzer for Indonesian News Using Deep Learning Models

Cited by 32 publications

References 19 publications

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Couplet Analysis of Linguistic Topology Using Deep Neural Networks in Cognitive Linguistics

Text Mining Literature Review on Indonesian Social Media

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