Combining Crowdsourcing and Deep Learning to Assess Public Opinion on CRISPR-Cas9

The discovery of the CRISPR-Cas9-based gene editing method has opened un-precedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development, and the almost instantaneous global spread of news, its important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semi-structured data in almost real-time and give direct access to the content of the conversations. Such data can now be mined and analyzed quickly because of recent developments in machine learning and natural language processing. Here, we used BERT, an attention-based transformer model, in combination with statistical methods to analyse the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events, and to observe how trending topics changed over time. Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset.