Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.

对社交网络上的讨论建模是一项具有挑战性的任务，尤其是当我们考虑敏感话题时，例如政治或医疗保健。然而，隐藏在这些辩论中的知识有助于调查趋势和观点，并在用户处理特定主题时识别用户的凝聚力。为此，我们提出了一种通用的多层网络方法来调查社交网络上的讨论。为了证明我们模型的有效性，我们将其应用于包含有关 COVID-19 疫苗观点的推文的 Twitter 数据集。我们为每条思路（即亲 vaxxer、中立和反 vaxxer）提取了一组相关的标签（即黄金标准标签）。然后，由于我们的多层网络模型，我们发现反 vaxxers 倾向于拥有更密集 (+14.39%) 和更具凝聚力 (+64. 2%），这导致反 vaxxers 之间的交互次数高于 pro-vaxxers（+393.89%）。最后，我们报告了我们的方法与基于单一网络分析的方法之间的比较。我们证明了我们的模型在提取具有更多节点 (+40.46%)、边缘 (+39.36%) 以及与其他方法的邻居互动 (+28.56%) 的自我网络的影响者方面的有效性。因此，这些有影响力的用户更值得分析，可以提供更有价值的信息。46%)、边缘 (+39.36%) 以及与其他方法的邻居交互 (+28.56%)。因此，这些有影响力的用户更值得分析，可以提供更有价值的信息。46%)、边缘 (+39.36%) 以及与其他方法的邻居交互 (+28.56%)。因此，这些有影响力的用户更值得分析，可以提供更有价值的信息。