Social media platforms empower us in several ways, from information
dissemination to consumption. While these platforms are useful in promoting
citizen journalism, public awareness etc., they have misuse potentials.
Malicious users use them to disseminate hate-speech, offensive content, rumor
etc. to gain social and political agendas or to harm individuals, entities and
organizations. Often times, general users unconsciously share information
without verifying it, or unintentionally post harmful messages. Some of such
content often get deleted either by the platform due to the violation of terms
and policies, or users themselves for different reasons, e.g., regrets. There
is a wide range of studies in characterizing, understanding and predicting
deleted content. However, studies which aims to identify the fine-grained
reasons (e.g., posts are offensive, hate speech or no identifiable reason)
behind deleted content, are limited. In this study we address this gap, by
identifying deleted tweets, particularly within the Arabic context, and
labeling them with a corresponding fine-grained disinformation category. We
then develop models that can predict the potentiality of tweets getting
deleted, as well as the potential reasons behind deletion. Such models can help
in moderating social media posts before even posting.

本篇研究旨在识别被删除的推文，确定其对应的细分类别以及开发模型以预测推文被删除的可能性及其背后的原因，帮助在推文发出之前进行社交媒体帖子审查。

发布前删除推文的检测与推理

Detecting and Reasoning of Deleted Tweets before they are Posted

A key challenge for automatic hate-speech detection on social media is the
separation of hate speech from other instances of offensive language. Lexical
detection methods tend to have low precision because they classify all messages
containing particular terms as hate speech and previous work using supervised
learning has failed to distinguish between the two categories. We used a
crowd-sourced hate speech lexicon to collect tweets containing hate speech
keywords. We use crowd-sourcing to label a sample of these tweets into three
categories: those containing hate speech, only offensive language, and those
with neither. We train a multi-class classifier to distinguish between these
different categories. Close analysis of the predictions and the errors shows
when we can reliably separate hate speech from other offensive language and
when this differentiation is more difficult. We find that racist and homophobic
tweets are more likely to be classified as hate speech but that sexist tweets
are generally classified as offensive. Tweets without explicit hate keywords
are also more difficult to classify.

该研究利用众包的方法，通过使用集成的多分类器和众包获得的仇恨言论词汇，成功区分了具有仇恨言辞、仅具有冒犯性言辞以及没有冒犯性言辞的推文。