The debate around misinformation and its potentially detrimental effects on public opinion is complex and multifaceted, to the extent that even the relevant academic research has not found unanimity on the prevalence and consumption of misinformation compared with mainstream content. The methodological framework presented here emphasises the importance of considering data representative of the complexity of the phenomenon and metrics that control for possible scale effects. By combining statistical, econometric and machine learning models, we shed light on the real impact of misinformation about a subject of general interest and social relevance, such as vaccines, on both the information available to citizens and their news diet. Our results show the prominent role achieved by misinformation sources in the news ecosystem, but also - and above all - the inability of mainstream media to drive the public debate over time on issues that are particularly sensitive and emotional. Taking properly account for the temporal dynamics of public debate seems crucial to prevent the latter from moving into uncontrolled spaces where false narratives are more easily conveyed and entrenched.
翻译:围绕错误信息及其对公众舆论的潜在有害影响的辩论是复杂和多方面的,因为即使有关的学术研究也没有发现与主流内容相比,错误信息的流行和消费情况与主流内容相比是一致的。这里介绍的方法框架强调,必须考虑数据代表现象的复杂性以及控制可能的规模效应的衡量标准。通过将统计、计量经济和机器学习模式结合起来,我们揭示了错误信息对一个普遍关注和具有社会相关性的主题,例如疫苗,对公民可获得的信息及其新闻饮食的真正影响。我们的结果显示,错误信息来源在新闻生态系统中取得了显著作用,但最重要的是,主流媒体无法在时间上推动公众辩论特别敏感和情绪性的问题。适当考虑公众辩论的时间动态,对于防止后者进入不受控制的空间,因为错误叙述更容易传播和扎根至关重要。