A Content-based Approach for the Analysis and Classification of Vaccine-related Stances on Twitter: the Italian Scenario
A Content-based Approach for the Analysis and Classification of Vaccine-related Stances on Twitter: the Italian Scenario
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Twitter 上疫苗相关立场的基于内容的分析和分类方法：意大利情景
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Marco Brambilla
中科院分区：
文献类型：
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作者：
Marco Di Giovanni;L. Corti;S. Pavanetto;Francesco Pierri;Andrea Tocchetti;Marco Brambilla
在SARS-CoV-2爆发一年后，已经成功开发了几种疫苗来防止其传播，疫苗推广活动正在世界范围内进行。然而，越来越多的人仍然对接种疫苗犹豫不决，这对实现群体免疫构成了严重威胁。我们在Twitter上收集和分析意大利人关于COVID-19疫苗的在线对话。我们定义了一种基于标签的半自动方法，将大量推文标记为支持者或对疫苗持怀疑态度。我们调查了数据的地理，时间和词汇分布，并训练了一个准确的二进制分类器，该分类器预测了推文对疫苗的立场，即，它会贴上“Pro-vax”或“No-vax”标签。这种分类方法可与其他经确认的技术同时使用，以及时发现和防止有关疫苗的负面和误导性信息的传播，确保更高的疫苗接种率。
One year after the outbreak of the SARS-CoV-2, several vaccines have been successfully developed to prevent its spreading, and vaccine roll-out campaigns are taking place world-wide. However, an increasing number of individuals is still hesitant towards getting vaccinated, and this poses a serious threat to reaching herd immunity. We collect and analyze Italian online conversations about COVID-19 vaccines on Twitter. We define a hashtag-based semi-automatic approach to label large volumes of tweets as supporters or skeptical about the vaccine. We investigate the geographical, temporal and lexical distribution of data, and we train an accurate binary classifier that predicts the stance of tweets towards vaccines, i.e., it applies a “Pro-vax” or “No-vax” label. This classifi-cation approach can be used, in parallel with other affirmed techniques, to promptly detect and prevent the spread of negative and misleading messages about vaccines, ensuring higher rates of vaccine uptake.
影响因子：
5.5
作者：
Kang GJ;Ewing-Nelson SR;Mackey L;Schlitt JT;Marathe A;Abbas KM;Swarup S
通讯作者：
Swarup S