News Labeling as Early as Possible: Real or Fake?
TABLE II: NEC results compared to competitive models on Sina Weibo and Twitter Dataset
['[EMPTY]', 'Sina Weibo Fake', 'Sina Weibo Fake', 'Sina Weibo Fake', 'Sina Weibo Real', 'Sina Weibo Real', 'Sina Weibo Real', 'Twitter Fake', 'Twitter Fake', 'Twitter Fake', 'Twitter Real', 'Twitter Real', 'Twitter Real']
For a fair comparison between models, we set the input length of baseline models similar to our model stopping point. As illustrated, the NEC model outperforms previous models in terms of news labeling correctness. As shown, NEC stops the labeling at about 5 time steps for Sina Weibo and 20 time steps for Twitter datasets, while other models use the whole message stream to achieve their labeling. Despite this significant advantage, NEC has a close competition with other models in term of accuracy. As depicted, despite the existence of variable event lengths, the earliness variance is low.