The rise of social media has enabled the widespread propagation of fake news,
text that is published with an intent to spread misinformation and sway
beliefs. Rapidly detecting fake news, especially as new events arise, is
important to prevent misinformation.
While prior works have tackled this problem using supervised learning
systems, automatedly modeling the complexities of the social media landscape
that enables the spread of fake news is challenging. On the contrary, having
humans fact check all news is not scalable. Thus, in this paper, we propose to
approach this problem interactively, where humans can interact to help an
automated system learn a better social media representation quality. On real
world events, our experiments show performance improvements in detecting
factuality of news sources, even after few human interactions.

社交媒体的兴起使得虚假新闻的广泛传播成为可能，而虚假新闻被故意发布以传播错误信息和影响人们的信仰。本文介绍了一种新的方法，通过人机交互的方式改善自动化系统对社交媒体的表示质量，在真实事件中实验表明，在少量人机交互之后，系统检测新闻可信度的性能得到了提升。

交互式学习社交媒体表达提高新闻来源事实准确性检测

Interactively Learning Social Media Representations Improves News Source  Factuality Detection

Poor performance of quantitative analysis in histopathological Whole Slide
Images (WSI) has been a significant obstacle in clinical practice. Annotating
large-scale WSIs manually is a demanding and time-consuming task, unlikely to
yield the expected results when used for fully supervised learning systems.
Rarely observed disease patterns and large differences in object scales are
difficult to model through conventional patient intake. Prior methods either
fall back to direct disease classification, which only requires learning a few
factors per image, or report on average image segmentation performance, which
is highly biased towards majority observations. Geometric image augmentation is
commonly used to improve robustness for average case predictions and to enrich
limited datasets. So far no method provided sampling of a realistic posterior
distribution to improve stability, e.g. for the segmentation of imbalanced
objects within images. Therefore, we propose a new approach, based on diffusion
models, which can enrich an imbalanced dataset with plausible examples from
underrepresented groups by conditioning on segmentation maps. Our method can
simply expand limited clinical datasets making them suitable to train machine
learning pipelines, and provides an interpretable and human-controllable way of
generating histopathology images that are indistinguishable from real ones to
human experts. We validate our findings on two datasets, one from the public
domain and one from a Kidney Transplant study.

通过扩充临床数据集中的少数样本，提高机器学习模型稳定性的一种新方法 —— 基于扩散模型的图像生成方法，可以生成与真实图像难以区分的组织病理学图像。

组织病理学中基于现实数据丰富的强健图像分割

Realistic Data Enrichment for Robust Image Segmentation in  Histopathology

Limited labeled data is becoming the largest bottleneck for supervised
learning systems. This is especially the case for many real-world tasks where
large scale annotated examples are either too expensive to acquire or
unavailable due to privacy or data access constraints. Weak supervision has
shown to be a good means to mitigate the scarcity of annotated data by
leveraging weak labels or injecting constraints from heuristic rules and/or
external knowledge sources. Social media has little labeled data but possesses
unique characteristics that make it suitable for generating weak supervision,
resulting in a new type of weak supervision, i.e., weak social supervision. In
this article, we illustrate how various aspects of social media can be used to
generate weak social supervision. Specifically, we use the recent research on
fake news detection as the use case, where social engagements are abundant but
annotated examples are scarce, to show that weak social supervision is
effective when facing the little labeled data problem. This article opens the
door for learning with weak social supervision for other emerging tasks.

本文探讨如何利用社交媒体生成弱社交监督来缓解标签数据的匮乏问题，并以社交媒体上的假新闻检测为例，展示了弱社交监督在面对标记数据问题时的有效性，为其他新兴任务学习提供了新思路。