Name
Abstract | ||
|---|---|---|
We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles. Specifically, in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is to be verified or not. For this task, we create a publicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce the cost of manual fact-checking process. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Social media,Computer science,Microblogging,Natural language processing,Artificial intelligence |
DocType | Volume | Citations |
Journal | abs/1810.11663 | 0 |
PageRank | References | Authors |
0.34 | 0 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsubasa Tagami | 1 | 0 | 0.34 |
| Hiroki Ouchi | 2 | 18 | 8.08 |
| Hiroki Asano | 3 | 0 | 0.34 |
| Kazuaki Hanawa | 4 | 3 | 4.52 |
| Kaori Uchiyama | 5 | 0 | 0.34 |
| Kaito Suzuki | 6 | 0 | 0.34 |
| Kentaro Inui | 7 | 1008 | 120.35 |
| Atsushi Komiya | 8 | 0 | 0.34 |
| Atsuo Fujimura | 9 | 0 | 0.34 |
| Hitofumi Yanai | 10 | 0 | 0.34 |
| Ryo Yamashita | 11 | 0 | 0.34 |
| Akinori Machino | 12 | 0 | 0.34 |