Social media conversations frequently suffer from toxicity, creating
significant issues for users, moderators, and entire communities. Events in the
real world, like elections or conflicts, can initiate and escalate toxic
behavior online. Our study investigates how real-world events influence the
origin and spread of toxicity in online discussions across various languages
and regions. We gathered Reddit data comprising 4.5 million comments from 31
thousand posts in six different languages (Dutch, English, German, Arabic,
Turkish and Spanish). We target fifteen major social and political world events
that occurred between 2020 and 2023. We observe significant variations in
toxicity, negative sentiment, and emotion expressions across different events
and language communities, showing that toxicity is a complex phenomenon in
which many different factors interact and still need to be investigated. We
will release the data for further research along with our code.

我们研究了真实世界事件对在线讨论中毒性起源和传播的影响，通过收集来自六种不同语言（荷兰语、英语、德语、阿拉伯语、土耳其语和西班牙语）的 Reddit 数据，包含了 15 个重大社会政治事件在 2020 年至 2023 年期间发生时产生的 4.5 百万评论和 31 千篇帖子，我们观察到了不同事件和语言社区中毒性、负面情绪和情感表达的显著变化，显示出毒性是一个复杂的现象，需要进一步研究，并将发布数据和代码供进一步研究使用。

跨语言现实事件中的有毒性

Grounding Toxicity in Real-World Events across Languages

Recent language models have shown impressive multilingual performance, even
when not explicitly trained for it. Despite this, concerns have been raised
about the quality of their outputs across different languages. In this paper,
we show how disparity in the treatment of different languages arises at the
tokenization stage, well before a model is even invoked. The same text
translated into different languages can have drastically different tokenization
lengths, with differences up to 15 times in some cases. These disparities
persist across the 17 tokenizers we evaluate, even if they are intentionally
trained for multilingual support. Character-level and byte-level models also
exhibit over 4 times the difference in the encoding length for some language
pairs. This induces unfair treatment for some language communities in regard to
the cost of accessing commercial language services, the processing time and
latency, as well as the amount of content that can be provided as context to
the models. Therefore, we make the case that we should train future language
models using multilingually fair tokenizers.

研究显示，语言模型在处理不同语言时存在标记化差异，导致部分语言社区无法公平访问商业语言服务、处理时间和网络延迟，并限制了模型学习的上下文，因此建议使用多语种公平标记器来训练未来的语言模型。

语言模型标记器引入了语言之间的不公平性

Language Model Tokenizers Introduce Unfairness Between Languages

We propose a simple method to extract the community structure of large
networks. Our method is a heuristic method that is based on modularity
optimization. It is shown to outperform all other known community detection
method in terms of computation time. Moreover, the quality of the communities
detected is very good, as measured by the so-called modularity. This is shown
first by identifying language communities in a Belgian mobile phone network of
2.6 million customers and by analyzing a web graph of 118 million nodes and
more than one billion links. The accuracy of our algorithm is also verified on
ad-hoc modular networks. .

利用基于模块度优化的启发式算法提取大型网络的社群结构，并且证实它的优越性和精准性在比利时移动电话网络和超过 10 亿个链接的网络图中得到了很好的效果。