Media news are making a large part of public opinion and, therefore, must not
be fake. News on web sites, blogs, and social media must be analyzed before
being published. In this paper, we present linguistic characteristics of media
news items to differentiate between fake news and real news using machine
learning algorithms. Neural fake news generation, headlines created by
machines, semantic incongruities in text and image captions generated by
machine are other types of fake news problems. These problems use neural
networks which mainly control distributional features rather than evidence. We
propose applying correlation between features set and class, and correlation
among the features to compute correlation attribute evaluation metric and
covariance metric to compute variance of attributes over the news items.
Features unique, negative, positive, and cardinal numbers with high values on
the metrics are observed to provide a high area under the curve (AUC) and
F1-score.

使用机器学习算法分析媒体新闻的语言特征，以区分真假新闻，主要解决神经假新闻生成、机器生成的标题、文本和图像标题之间的语义矛盾等问题。同时提出应用特征集和类别、特征之间的相关性计算相关属性评估指标和计算属性变量的协方差指标来模拟这些问题。其中独特、负面、积极和基数数字特征在度量中得分高，能够提供高的 AUC 和 F1-score。

虚假新闻的预测语言线索：一个社会人工智能问题

Predictive linguistic cues for fake news: a societal artificial intelligence problem

The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action proposals selected with human point-supervision perform as
well as using expensive box annotations. Rather than asking users to provide
point supervision, we propose fully automatic visual cues that replace manual
point annotations. We call the cues pseudo-annotations, introduce five of them,
and propose a correlation metric for automatically selecting and combining
them. Thorough evaluation on challenging action localization datasets shows
that we reach results comparable to results with full box supervision. We also
show that pseudo-annotations can be leveraged during testing to improve weakly-
and strongly-supervised localizers.

本文提出了一种使用视觉线索代替手动点注释的伪注释，引入了五种伪注释，并提出了一个自动选择和组合它们的相关性度量，从而实现只通过分类标签确定视频中动作的时空位置的算法。在具有挑战性的行动定位数据集上的彻底评估显示，我们达到了与完全盒子监督结果相当的结果。我们还展示了伪注释如何在测试期间被利用来改善弱监督和强监督本地化器。