In this work, we demonstrate how existing classifiers for identifying toxic
comments online fail to generalize to the diverse concerns of Internet users.
We survey 17,280 participants to understand how user expectations for what
constitutes toxic content differ across demographics, beliefs, and personal
experiences. We find that groups historically at-risk of harassment - such as
people who identify as LGBTQ+ or young adults - are more likely to to flag a
random comment drawn from Reddit, Twitter, or 4chan as toxic, as are people who
have personally experienced harassment in the past. Based on our findings, we
show how current one-size-fits-all toxicity classification algorithms, like the
Perspective API from Jigsaw, can improve in accuracy by 86% on average through
personalized model tuning. Ultimately, we highlight current pitfalls and new
design directions that can improve the equity and efficacy of toxic content
classifiers for all users.

本研究调查了 17280 名互联网用户对什么构成毒性内容的期望不同，发现那些历史上处于骚扰风险中的群体更可能将 Reddit、Twitter 或 4chan 上的随机评论标记为有毒，而那些亲身经历过骚扰的人也更可能这样做。基于我们的发现，我们展示了目前的一款面向所有用户的毒性分类算法 Perspective API，如何通过个性化模型调整平均提高 86％的准确性。最终，我们强调当前的缺陷和新的设计方向，以提高毒性内容分类器的公平性和效果。

为多元视角设计有害内容分类

Designing Toxic Content Classification for a Diversity of Perspectives

In recent years, Internet users are reporting Adverse Drug Events (ADE) on
social media, blogs and health forums. Because of the large volume of reports,
pharmacovigilance is seeking to resort to NLP to monitor these outlets. We
propose for the first time the use of the SpanBERT architecture for the task of
ADE extraction: this new version of the popular BERT transformer showed
improved capabilities with multi-token text spans. We validate our hypothesis
with experiments on two datasets (SMM4H and CADEC) with different text
typologies (tweets and blog posts), finding that SpanBERT combined with a CRF
outperforms all the competitors on both of them.

本文提出了应用 SpanBERT 架构通过 NLP 监控社交媒体、博客和健康论坛上的不良药物事件反应（Adverse Drug Events），并在两个数据集上验证了它在多词汇语境中的优越性。

通过 SpanBERT 在不同文本类型上改进不良药物事件提取

Improving Adverse Drug Event Extraction with SpanBERT on Different Text  Typologies

Internet users generate content at unprecedented rates. Building intelligent
systems capable of discriminating useful content within this ocean of
information is thus becoming a urgent need. In this paper, we aim to predict
the usefulness of Amazon reviews, and to do this we exploit features coming
from an off-the-shelf argumentation mining system. We argue that the usefulness
of a review, in fact, is strictly related to its argumentative content, whereas
the use of an already trained system avoids the costly need of relabeling a
novel dataset. Results obtained on a large publicly available corpus support
this hypothesis.

本文旨在利用现成的论证挖掘系统来预测 Amazon 评论的有用性，结果表明评论的有用性与其论证内容紧密相关。

使用现成论证挖掘预测亚马逊评论的有用性

Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf  Argumentation Mining

Some users of social media are spreading racist, sexist, and otherwise
hateful content. For the purpose of training a hate speech detection system,
the reliability of the annotations is crucial, but there is no universally
agreed-upon definition. We collected potentially hateful messages and asked two
groups of internet users to determine whether they were hate speech or not,
whether they should be banned or not and to rate their degree of offensiveness.
One of the groups was shown a definition prior to completing the survey. We
aimed to assess whether hate speech can be annotated reliably, and the extent
to which existing definitions are in accordance with subjective ratings. Our
results indicate that showing users a definition caused them to partially align
their own opinion with the definition but did not improve reliability, which
was very low overall. We conclude that the presence of hate speech should
perhaps not be considered a binary yes-or-no decision, and raters need more
detailed instructions for the annotation.

通过收集潜在令人厌恶的消息并询问两组互联网用户是否为仇恨言论，是否应禁止以及他们的冒犯程度的判断，证明表达清晰的定义及更详细的指示是应对社交媒体上恶言恶语扩散并标记仇恨言论的必要措施。