Effective communication during health crises is critical, with social media
serving as a key platform for public health experts (PHEs) to engage with the
public. However, it also amplifies pseudo-experts promoting contrarian views.
Despite its importance, the role of emotional and moral language in PHEs'
communication during COVID-19 remains under explored. This study examines how
PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing
on emotional and moral language and their engagement with political elites.
Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to
January 2021, alongside public responses, we identified key priorities and
differences in messaging strategy. PHEs prioritize masking, healthcare,
education, and vaccines, using positive emotional language like optimism. In
contrast, pseudo-experts discuss therapeutics and lockdowns more frequently,
employing negative emotions like pessimism and disgust. Negative emotional and
moral language tends to drive engagement, but positive language from PHEs
fosters positivity in public responses. PHEs exhibit liberal partisanship,
expressing more positivity towards liberals and negativity towards conservative
elites, while pseudo-experts show conservative partisanship. These findings
shed light on the polarization of COVID-19 discourse and underscore the
importance of strategic use of emotional and moral language by experts to
mitigate polarization and enhance public trust.

研究了 COVID-19 期间公共卫生专家和伪专家在推特上的沟通，聚焦情绪和道德语言及其与政治精英的互动，发现专家更注重口罩、医疗、教育和疫苗，使用积极情绪语言，而伪专家更多讨论治疗方法和封锁，使用消极情绪语言，专家的积极语言促进公众回应的积极性，专家表达了自由主义的政治立场，而伪专家则显示了保守主义的立场，这些发现揭示了 COVID-19 话语的极化现象，强调专家战略性使用情绪和道德语言以缓解极化问题并增强公众信任的重要性。

EpiTwitter: COVID-19 大流行期间的公共卫生信息传播

#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic

Generating emotional language is a key step towards building empathetic
natural language processing agents. However, a major challenge for this line of
research is the lack of large-scale labeled training data, and previous studies
are limited to only small sets of human annotated sentiment labels.
Additionally, explicitly controlling the emotion and sentiment of generated
text is also difficult. In this paper, we take a more radical approach: we
exploit the idea of leveraging Twitter data that are naturally labeled with
emojis. More specifically, we collect a large corpus of Twitter conversations
that include emojis in the response, and assume the emojis convey the
underlying emotions of the sentence. We then introduce a reinforced conditional
variational encoder approach to train a deep generative model on these
conversations, which allows us to use emojis to control the emotion of the
generated text. Experimentally, we show in our quantitative and qualitative
analyses that the proposed models can successfully generate high-quality
abstractive conversation responses in accordance with designated emotions.

借助基于表情符号的标注语料以及引入强化条件变分编码器，论文提出了一种生成具有特定情感的高质量抽象会话回复的深度生成模型。