We present TwHIN-BERT, a multilingual language model trained on in-domain
data from the popular social network Twitter. TwHIN-BERT differs from prior
pre-trained language models as it is trained with not only text-based
self-supervision, but also with a social objective based on the rich social
engagements within a Twitter heterogeneous information network (TwHIN). Our
model is trained on 7 billion tweets covering over 100 distinct languages
providing a valuable representation to model short, noisy, user-generated text.
We evaluate our model on a variety of multilingual social recommendation and
semantic understanding tasks and demonstrate significant metric improvement
over established pre-trained language models. We will freely open-source
TwHIN-BERT and our curated hashtag prediction and social engagement benchmark
datasets to the research community.

TwHIN-BERT 是一种多语言语言模型，使用来自 Twitter 的专业领域数据进行训练，并通过具有基于 Twitter 异构信息网络（TwHIN）中丰富的社交互动的社交目标的训练使其不同于以前的预训练语言模型。我们在各种多语言社交推荐和语义理解任务上评估了我们的模型，并展示了相对于已建立的预训练语言模型的重大指标改进。

TwHIN-BERT：用于多语种推文具有社交增强的预训练语言模型

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations

The automatic identification of harmful content online is of major concern
for social media platforms, policymakers, and society. Researchers have studied
textual, visual, and audio content, but typically in isolation. Yet, harmful
content often combines multiple modalities, as in the case of memes, which are
of particular interest due to their viral nature. With this in mind, here we
offer a comprehensive survey with a focus on harmful memes. Based on a
systematic analysis of recent literature, we first propose a new typology of
harmful memes, and then we highlight and summarize the relevant state of the
art. One interesting finding is that many types of harmful memes are not really
studied, e.g., such featuring self-harm and extremism, partly due to the lack
of suitable datasets. We further find that existing datasets mostly capture
multi-class scenarios, which are not inclusive of the affective spectrum that
memes can represent. Another observation is that memes can propagate globally
through repackaging in different languages and that they can also be
multilingual, blending different cultures. We conclude by highlighting several
challenges related to multimodal semiotics, technological constraints, and
non-trivial social engagement, and we present several open-ended aspects such
as delineating online harm and empirically examining related frameworks and
assistive interventions, which we believe will motivate and drive future
research.

该综述以有害网络迷因为研究对象，提出了新的分类方法，调查了相关研究现状，发现现有的数据集存在问题，迷因可通过多种语言进行传播，涉及的多模态语言和技术难题需要面对。

检测和理解有害的网络恶搞，一项调查

Detecting and Understanding Harmful Memes: A Survey

As the COVID-19 virus quickly spreads around the world, unfortunately,
misinformation related to COVID-19 also gets created and spreads like wild
fire. Such misinformation has caused confusion among people, disruptions in
society, and even deadly consequences in health problems. To be able to
understand, detect, and mitigate such COVID-19 misinformation, therefore, has
not only deep intellectual values but also huge societal impacts. To help
researchers combat COVID-19 health misinformation, therefore, we present CoAID
(Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare
misinformation, including fake news on websites and social platforms, along
with users' social engagement about such news. CoAID includes 4,251 news,
296,000 related user engagements, 926 social platform posts about COVID-19, and
ground truth labels. The dataset is available at:
this https URL

为了理解、检测和缓解 COVID-19 的不实信息，我们提出了 CoAID（Covid-19 健康保健误信息数据集），其中包括 4,251 条有关 COVID-19 健康保健不实信息的新闻、296,000 个相关用户参与以及 926 个有关 COVID-19 的社交媒体帖子和标签数据。