We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news.A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan.We show how a style analysis can distinguish hyperpartisan news from the mainstream (F 1 = 0.78), and satire from both (F 1 = 0.81).But stylometry is no silver bullet as style-based fake news detection does not work (F 1 = 0.46).We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream.This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way.Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.

We extend our system for radio astronomical monitoring by a cross-validation filter, separating near Earth radio frequency interference (RFI) from deep space signals. The filter searches for similar signals in a nearby frequency band, coming from a different spatial direction than the tested signal. The filter passes the signals which do not have such duplicates. We apply this technique to a database of SETI Institute (setilive.org), containing 1.5 millions of sky observations in a frequency range 0.5-11.2~GHz, where our primary selection identified 28 strong signals possessing extraterrestrial (ET) signature. Cross-validation allows to filter out 24 of those signals as satellite RFI. We present parameters for the remaining 4 signals and discuss statistical significance of these findings.