Although there is an unprecedented effort to provide adequate responses in
terms of laws and policies to hate content on social media platforms, dealing
with hatred online is still a tough problem. Tackling hate speech in the
standard way of content deletion or user suspension may be charged with
censorship and overblocking. One alternate strategy, that has received little
attention so far by the research community, is to actually oppose hate content
with counter-narratives (i.e. informed textual responses). In this paper, we
describe the creation of the first large-scale, multilingual, expert-based
dataset of hate speech/counter-narrative pairs. This dataset has been built
with the effort of more than 100 operators from three different NGOs that
applied their training and expertise to the task. Together with the collected
data we also provide additional annotations about expert demographics, hate and
response type, and data augmentation through translation and paraphrasing.
Finally, we provide initial experiments to assess the quality of our data.

该研究描述了多个 NGO 的百余名熟练操作员联合创建的第一个大尺度的恶意言论及对抗文本对的多语种数据集，并提供了关于数据扩增、恶意类型和回应类型等额外的注释。

CONAN - COunter NArratives through Nichesourcing: 一个多语言的回应数据集，以打击网络仇恨言论

CONAN -- COunter NArratives through Nichesourcing: a Multilingual  Dataset of Responses to Fight Online Hate Speech

Informal settlements are home to the most socially and economically
vulnerable people on the planet. In order to deliver effective economic and
social aid, non-government organizations (NGOs), such as the United Nations
Children's Fund (UNICEF), require detailed maps of the locations of informal
settlements. However, data regarding informal and formal settlements is
primarily unavailable and if available is often incomplete. This is due, in
part, to the cost and complexity of gathering data on a large scale. To address
these challenges, we, in this work, provide three contributions. 1) A brand new
machine learning data-set, purposely developed for informal settlement
detection. 2) We show that it is possible to detect informal settlements using
freely available low-resolution (LR) data, in contrast to previous studies that
use very-high resolution (VHR) satellite and aerial imagery, something that is
cost-prohibitive for NGOs. 3) We demonstrate two effective classification
schemes on our curated data set, one that is cost-efficient for NGOs and
another that is cost-prohibitive for NGOs, but has additional utility. We
integrate these schemes into a semi-automated pipeline that converts either a
LR or VHR satellite image into a binary map that encodes the locations of
informal settlements.

该研究提出了一种新的机器学习数据集，可以用于检测非正式聚居地，同时表明使用免费的低分辨率卫星图像进行非正式聚居地侦测是可行的，并演示了两种有效的分类方案，可将这些方案集成到半自动化流水线中。