Online social networks have become a fundamental component of our everyday
life. Unfortunately, these platforms are also a stage for hate speech. Popular
social networks have regularized rules against hate speech. Consequently,
social networks like Parler and Gab advocating and claiming to be free speech
platforms have evolved. These platforms have become a district for hate speech
against diverse targets. We present in our paper a pipeline for detecting hate
speech and its targets and use it for creating Parler hate targets'
distribution. The pipeline consists of two models; one for hate speech
detection and the second for target classification, both based on BERT with
Back-Translation and data pre-processing for improved results. The source code
used in this work, as well as other relevant sources, are available at:
this https URL

本文介绍了一种基于 BERT 的管道用于检测网络仇恨言论及其目标对象，并使用此方法创建了 Parler 网络上的目标对象分布。

使用 BERT 在 Parler 上检测仇恨言论的目标

Hate Speech Targets Detection in Parler using BERT

On 6 January 2021, a mob of right-wing conservatives stormed the USA Capitol
Hill interrupting the session of congress certifying 2020 Presidential election
results. Immediately after the start of the event, posts related to the riots
started to trend on social media. A social media platform which stood out was a
free speech endorsing social media platform Parler; it is being claimed as the
platform on which the riots were planned and talked about. Our report presents
a contrast between the trending content on Parler and Twitter around the time
of riots. We collected data from both platforms based on the trending hashtags
and draw comparisons based on what are the topics being talked about, who are
the people active on the platforms and how organic is the content generated on
the two platforms. While the content trending on Twitter had strong resentments
towards the event and called for action against rioters and inciters, Parler
content had a strong conservative narrative echoing the ideas of voter fraud
similar to the attacking mob. We also find a disproportionately high
manipulation of traffic on Parler when compared to Twitter.

通过社交媒体趋势话题、人群、内容等方面对 Twitter 和 Parler 平台上与美国国会大厦冲击事件相关的推文进行对比，结果发现 Parler 平台上的内容呈现出对投票欺诈和冲击事件的强烈支持的保守主义叙事，而且 Parler 上的流量操纵比例也显著高于 Twitter。