arXiv 2401.131332024-01-23cs.CLcs.SI

分析尼日利亚网络空间中的 COVID-19 疫苗接种情绪：来自人工标注 Twitter 数据集的洞见

Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset

Northeastern University（东北大学）；HausaNLP（豪萨自然语言处理实验室）；Bayero University Kano（卡诺巴耶罗大学）；University of Porto（波尔图大学）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；University Of Maiduguri（迈杜古里大学）；University of Abuja（阿布贾大学）

Ibrahim Said Ahmad, Lukman Jibril Aliyu, Abubakar Auwal Khalid, Saminu Muhammad Aliyu, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Bala Mairiga Abduljalil, B… 展开作者

Ibrahim Said Ahmad, Lukman Jibril Aliyu, Abubakar Auwal Khalid, Saminu Muhammad Aliyu, Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Bala Mairiga Abduljalil, Bello Shehu Bello, Amina Imam Abubakar

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AI总结：

本文构建人工标注的多语言尼日利亚 Twitter 数据集，并利用基于 transformer 的 LLM 分析 COVID-19 疫苗情绪，发现整体偏中性且跨语言微调仍具竞争力。

摘要：

在抗击 COVID-19 大流行方面已经取得了许多成功，最初采用了封锁、社交距离和佩戴口罩等各种预防措施。最近，人们研发了多种疫苗，以帮助预防 COVID-19 感染或降低其严重程度。尽管这些预防措施和疫苗有效，但 Twitter 等社交媒体平台上仍大量传播着若干争议。本文中，我们探索使用最先进的、基于 transformer 的语言模型来研究尼日利亚民众对疫苗的接受度。我们利用相关话题标签和关键词爬取多语言推文，构建了一个新颖数据集。分析与可视化显示，大多数推文对 COVID-19 疫苗表达中性情绪，部分人表达正面看法，且对特定疫苗类型没有强烈偏好，不过 Moderna 获得了略多的正面情绪。我们还发现，使用合适数据集微调预训练 LLM 可以获得具有竞争力的结果，即使该 LLM 最初并未在该数据集的特定语言上进行预训练。

英文摘要：

Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language of that dataset.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。