The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in size and reliability, has been released. However, this dataset has
not been comprehensively studied to its potential. In this paper, we conduct
the first comparative study of various learning models on Hate and Abusive
Speech on Twitter, and discuss the possibility of using additional features and
context data for improvements. Experimental results show that bidirectional GRU
networks trained on word-level features, with Latent Topic Clustering modules,
is the most accurate model scoring 0.805 F1.

本文研究了在 Twitter 上关于仇恨言论和辱骂的数据集，考察了不同的学习模型和额外特征的加入，并得出了使用双向 GRU 网络结合潜在主题聚类模型的最优模型，F1 得分为 0.805。

推特上滥用语言的比较研究

Comparative Studies of Detecting Abusive Language on Twitter

Over the past few years, online aggression and abusive behaviors have
occurred in many different forms and on a variety of platforms. In extreme
cases, these incidents have evolved into hate, discrimination, and bullying,
and even materialized into real-world threats and attacks against individuals
or groups. In this paper, we study the Gamergate controversy. Started in August
2014 in the online gaming world, it quickly spread across various social
networking platforms, ultimately leading to many incidents of cyberbullying and
cyberaggression. We focus on Twitter, presenting a measurement study of a
dataset of 340k unique users and 1.6M tweets to study the properties of these
users, the content they post, and how they differ from random Twitter users. We
find that users involved in this "Twitter war" tend to have more friends and
followers, are generally more engaged and post tweets with negative sentiment,
less joy, and more hate than random users. We also perform preliminary
measurements on how the Twitter suspension mechanism deals with such abusive
behaviors. While we focus on Gamergate, our methodology to collect and analyze
tweets related to aggressive and bullying activities is of independent
interest.

本文研究了 2014 年网游世界的 Gamergate 争端，通过对 Twitter 用户发布的数据进行测量，发现参与这一 “Twitter 之战” 的用户往往拥有更多的朋友和关注者，发布具有负面情绪和仇恨的推文，并探讨了 Twitter 的暂停机制处理此类滥用行为的初步措施。