{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:39:21Z","timestamp":1760150361845,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Culture, Research, and Technology of Indonesia","award":["009\/E5\/PG.02.00.PL\/2023","1333\/IT2\/T\/HK.00.01\/2022"],"award-info":[{"award-number":["009\/E5\/PG.02.00.PL\/2023","1333\/IT2\/T\/HK.00.01\/2022"]}]},{"name":"Institut Teknologi Sepuluh Nopember","award":["009\/E5\/PG.02.00.PL\/2023","1333\/IT2\/T\/HK.00.01\/2022"],"award-info":[{"award-number":["009\/E5\/PG.02.00.PL\/2023","1333\/IT2\/T\/HK.00.01\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>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 (\u00b11700 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\u2013BERT\u2013BiLSTM 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.<\/jats:p>","DOI":"10.3390\/informatics10040086","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T01:48:45Z","timestamp":1700531325000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Detection of Persuasive Content in Electronic News"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2597-5165","authenticated-orcid":false,"given":"Brian Rizqi Paradisiaca","family":"Darnoto","sequence":"first","affiliation":[{"name":"Informatics Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6560-2975","authenticated-orcid":false,"given":"Daniel","family":"Siahaan","sequence":"additional","affiliation":[{"name":"Informatics Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-7628","authenticated-orcid":false,"given":"Diana","family":"Purwitasari","sequence":"additional","affiliation":[{"name":"Informatics Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1177\/0002764219878224","article-title":"\u201cFake News\u201d Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content","volume":"65","author":"Molina","year":"2019","journal-title":"Am. 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