Deep Learning for Native Advertisement Detection in Electronic News: A Comparative Study

Darnoto, Brian Rizqi Paradisiaca; Siahaan, Daniel; Purwitasari, Diana

doi:10.1109/eeccis54468.2022.9902953

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…On the persuasive dataset, bold numbers in Table 6 show that the TextRank-BERT-BiLSTM model achieved an accuracy of up to 95%. Our proposed TextRank-BERT-BiLSTM model obtained the same result as our previous research, in which BERT-BiLSTM was used to detect native ads [10]. Our proposed model obtained the same result using more than one dataset.…”

Section: Discussionsupporting

confidence: 72%

“…The studies that are the most related to our study are those on the detection of fake news, in which the researchers conducted various experiments on fake news to evaluate their proposed methods. We also compared the results of this study to our previous research [10]. The following are the studies that we compared our results with: programming process easier (Kartika et al, 2020).…”

Section: Compared Methodsmentioning

confidence: 91%

“…BERT-BiLSTM FNID-FakeNewsNet dataset [5] 0.90 BERT-BiLSTM FNID-LIAR dataset [5] 0.39 BERT for Indonesia fake news [6] 0.90 Ensemble-based deep learning model [29] 0.898 BiLSTM for Arabic fake news [12] 0.848 BERT-BiLSTM for detection of native ads [10] 0.95 Ours (TextRank-BERT-BiLSTM)…”

Section: Methods Accuracymentioning

confidence: 99%

“…Furthermore, we explore the use of text summarization models, TextRank and LSA, as well as compare the performance of two classifiers, BiLSTM and CNN, using word-embedding BERT and roBERTa. BiLSTM and CNN are models that were used in our previous research, and both models outperformed the LSTM model [10]. The results obtained in the research "Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data" conducted by Tran were optimistic and reliable enough to complete the task, helped reduce the occurrence of hate or offensive comments, and established a healthy and safe environment [11].…”

Section: Introductionmentioning

confidence: 92%

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Automated Detection of Persuasive Content in Electronic News

Darnoto,

Siahaan,

Purwitasari

2023

Informatics

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Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differentiate news with or without the persuasive content. Recognizing persuasive sentences with a text summarization and classification approach is important to understand persuasive messages effectively. Text summarization identifies arguments and key points, while classification separates persuasive sentences based on the linguistic and semantic features used. Our proposed architecture includes text summarization approaches to shorten sentences without persuasive content and then using classifiers model to detect those with persuasive indication. In this paper, we compare the performance of latent semantic analysis (LSA) and TextRank in text summarization methods, the latter of which has outperformed in all trials, and also two classifiers of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). We have prepared a dataset (±1700 data and manually persuasiveness-labeled) consisting of news articles written in the Indonesian language collected from a nationwide electronic news portal. Comparative studies in our experimental results show that the TextRank–BERT–BiLSTM model achieved the highest accuracy of 95% in detecting persuasive news. The text summarization methods were able to generate detailed and precise summaries of the news articles and the deep learning models were able to effectively differentiate between persuasive news and real news.

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Section: Discussionsupporting

confidence: 72%

Section: Compared Methodsmentioning

confidence: 91%