论文标题
“双疫苗接种，5G提升！”：从社交媒体中学习对Covid-19的态度
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media
论文作者
论文摘要
为了解决疫苗犹豫不决，这会损害Covid-19疫苗接种运动的努力，必须了解公共疫苗接种态度并及时掌握其变化。尽管具有可靠性和可信赖性，但基于调查的传统态度收集是耗时且昂贵的，无法遵循疫苗接种态度的快速发展。我们利用社交媒体上的文本帖子通过提出深入学习框架来实时提取和跟踪用户的疫苗接种立场。为了解决与疫苗相关话语中常用的讽刺和讽刺性的语言特征的影响，我们将用户社交网络邻居的最新帖子集成到框架中，以帮助检测用户的真实态度。根据我们从Twitter的注释数据集，与最新的仅一本文本模型相比，从我们框架实例化的模型可以提高态度提取的性能高达23％。使用此框架，我们成功地验证了使用社交媒体跟踪现实生活中疫苗接种态度的演变的可行性。我们进一步显示了我们框架的一种实际用途，它通过验证了通过社交媒体感知到的信息来预测用户疫苗犹豫不决的可能性的可能性。
To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.
