Understanding public discourse on emergency use of unproven therapeutics is
essential to monitor safe use and combat misinformation. We developed a natural
language processing (NLP)-based pipeline to understand public perceptions of
and stances on COVID-19-related drugs on Twitter across time. This
retrospective study included 609,189 US-based tweets between January 29th, 2020
and November 30th, 2021 on four drugs that gained wide public attention during
the COVID-19 pandemic: 1) Hydroxychloroquine and Ivermectin, drug therapies
with anecdotal evidence; and 2) Molnupiravir and Remdesivir, FDA-approved
treatment options for eligible patients. Time-trend analysis was used to
understand the popularity and related events. Content and demographic analyses
were conducted to explore potential rationales of people's stances on each
drug. Time-trend analysis revealed that Hydroxychloroquine and Ivermectin
received much more discussion than Molnupiravir and Remdesivir, particularly
during COVID-19 surges. Hydroxychloroquine and Ivermectin were highly
politicized, related to conspiracy theories, hearsay, celebrity effects, etc.
The distribution of stance between the two major US political parties was
significantly different (p<0.001); Republicans were much more likely to support
Hydroxychloroquine (+55%) and Ivermectin (+30%) than Democrats. People with
healthcare backgrounds tended to oppose Hydroxychloroquine (+7%) more than the
general population; in contrast, the general population was more likely to
support Ivermectin (+14%). We make all the data, code, and models available at
this https URL.

本文运用自然语言处理技术分析 2020 年 1 月至 2021 年 11 月期间 Twitter 上针对 COVID-19 相关药物的 609,189 条帖子，发现羟氯喹和伊维菌素等药物受到的关注远远高于莫卢比瑞韦和瑞德西韦，政治化现象明显，与阴谋论、名人效应等相关，而不同政治党派和人群对药物的立场存在显著差异。

使用 Twitter 数据了解公众对 COVID-19 相关药物批准和非标签使用的看法

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications

Efficient and reliable social bot classification is crucial for detecting
information manipulation on social media. Despite rapid development,
state-of-the-art bot detection models still face generalization and scalability
challenges, which greatly limit their applications. In this paper we propose a
framework that uses minimal account metadata, enabling efficient analysis that
scales up to handle the full stream of public tweets of Twitter in real time.
To ensure model accuracy, we build a rich collection of labeled datasets for
training and validation. We deploy a strict validation system so that model
performance on unseen datasets is also optimized, in addition to traditional
cross-validation. We find that strategically selecting a subset of training
data yields better model accuracy and generalization than exhaustively training
on all available data. Thanks to the simplicity of the proposed model, its
logic can be interpreted to provide insights into social bot characteristics.

本研究提出一种基于最少账户元数据的框架，实现了对 Twitter 公共推文全量实时分析的高效和可靠的社交机器人分类，通过对训练和验证的大量数据集进行严格的验证系统，找到了挑选子集进行训练的方式比全量训练更好的模型精度和泛化性。

可扩展且具有普适性的社交机器人检测通过数据选择

Scalable and Generalizable Social Bot Detection through Data Selection

The online hate speech is proliferating with several organization and
countries implementing laws to ban such harmful speech. While these
restrictions might reduce the amount of such hateful content, it does so by
restricting freedom of speech. Thus, an promising alternative supported by
several organizations is to counter such hate speech with more speech. In this
paper, We analyze hate speech and the corresponding counters (aka
counterspeech) on Twitter. We perform several lexical, linguistic and
psycholinguistic analysis on these user accounts and obverse that counter
speakers employ several strategies depending on the target community. The
hateful accounts express more negative sentiments and are more profane. We also
find that the hate tweets by verified accounts have much more virality as
compared to a tweet by a non-verified account. While the hate users seem to use
words more about envy, hate, negative emotion, swearing terms, ugliness, the
counter users use more words related to government, law, leader. We also build
a supervised model for classifying the hateful and counterspeech accounts on
Twitter and obtain an F-score of 0.77. We also make our dataset public to help
advance the research on hate speech.

本研究发现 Twitter 的在线仇恨言论与相应的反言论使用了不同的语言和策略，并且经过一些分析和建模，能够对这些用户进行可靠的分类以进一步研究。

分析 Twitter 上的仇恨与对立言论账户

Analyzing the hate and counter speech accounts on Twitter

As a consequence of the accelerated globalization process, today major cities
all over the world are characterized by an increasing multiculturalism. The
integration of immigrant communities may be affected by social polarization and
spatial segregation. How are these dynamics evolving over time? To what extent
the different policies launched to tackle these problems are working? These are
critical questions traditionally addressed by studies based on surveys and
census data. Such sources are safe to avoid spurious biases, but the data
collection becomes an intensive and rather expensive work. Here, we conduct a
comprehensive study on immigrant integration in 53 world cities by introducing
an innovative approach: an analysis of the spatio-temporal communication
patterns of immigrant and local communities based on language detection in
Twitter and on novel metrics of spatial integration. We quantify the "Power of
Integration" of cities --their capacity to spatially integrate diverse
cultures-- and characterize the relations between different cultures when
acting as hosts or immigrants.

通过对 Twitter 数据的语言检测和空间整合性的新度量，我们对全球 53 个城市中移民整合进行了全面的研究，并量化了城市的 “整合能力” 以及移民和本地社区相互作用的特征。