This paper presents the findings of an exploratory study on the continuously
generating Big Data on Twitter related to the sharing of information, news,
views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic,
with a specific focus on the Omicron variant, which is the globally dominant
variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron
variant were studied, and the specific characteristics of tweets that were
analyzed include - sentiment, language, source, type, and embedded URLs. The
findings of this study are manifold. First, from sentiment analysis, it was
observed that 50.5% of tweets had a neutral emotion. The other emotions - bad,
good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the
tweets, respectively. Second, the findings of language interpretation showed
that 65.9% of the tweets were posted in English. It was followed by Spanish,
French, Italian, and other languages. Third, the findings from source tracking
showed that Twitter for Android was associated with 35.2% of tweets. It was
followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other
sources. Fourth, studying the type of tweets revealed that retweets accounted
for 60.8% of the tweets, it was followed by original tweets and replies that
accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of
embedded URL analysis, the most common domain embedded in the tweets was found
to be this http URL, which was followed by this http URL, this http URL, and other
domains. Finally, to support similar research in this field, we have developed
a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2
omicron variant since the first detected case of this variant on November 24,
2021.

本研究关注在社交媒体 Twitter 上有关 COVID-19 Omicron 变种的持续产生的大数据，主要分析了推文的情感、语言、来源、类型和嵌入式 URL，发现情感中性是主要的表达方式，源于 Twitter for Android 的推文占据了很大一部分，而转推是最常见的类型，提供了一个由 50 万条推文组成的 Twitter 数据集，以支持类似的领域研究。

关于 SARS-CoV-2 Omicron 变异株推文的探索性研究：情感分析、语言解释、来源跟踪、类型分类和嵌入式 URL 检测的洞见

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection