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The previous method of detecting fraudulent news headlines was mainly laborious manual review. While the total number of news headlines goes as high as 1.48 million, manual review becomes practically infeasible. For news headline text data, attention mechanism has powerful processing capability. In this paper, we propose the models based on LSTM and attention layer, which fit the context of news headlines efficiently and can detect fraudulent news headlines quickly and accurately. Based on multi\u2010head attention mechanism eschewing recurrent unit and reducing sequential computation, we build Mini\u2010Transformer Deep Learning model to further improve the classification performance.<\/jats:p>","DOI":"10.1155\/2021\/6679661","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T20:05:20Z","timestamp":1615838720000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fraudulent News Headline Detection with Attention Mechanism"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8409-2429","authenticated-orcid":false,"given":"Hankun","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3820-8128","authenticated-orcid":false,"given":"Daojing","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-229X","authenticated-orcid":false,"given":"Sammy","family":"Chan","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"e_1_2_10_1_2","unstructured":"Symantec Internet Security Threat Report Symantec Corporation 2018 https:\/\/docs.broadcom.com\/doc\/istr-23-03-2018-en."},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_10_3_2","unstructured":"BahdanauD. 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