Name
Abstract | ||
|---|---|---|
E-mail systems and online social media platforms are ideal places for news dissemination, but a serious problem is the spread of fraudulent news headlines. 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-head attention mechanism eschewing recurrent unit and reducing sequential computation, we build Mini-Transformer Deep Learning model to further improve the classification performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1155/2021/6679661 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
DocType | Volume | ISSN |
Journal | 2021 | 1687-5265 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hankun Liu | 1 | 0 | 0.34 |
| Daojing He | 2 | 1013 | 58.40 |
| Sammy Chan | 3 | 902 | 66.93 |