Since the Russian invasion of Ukraine, a large volume of biased and partisan news has been spread via social media platforms. As this may lead to wider societal issues, we argue that understanding how partisan news sharing impacts users' communication is crucial for better governance of online communities. In this paper, we perform a measurement study of partisan news sharing. We aim to characterize the role of such sharing in influencing users' communications. Our analysis covers an eight-month dataset across six Reddit communities related to the Russian invasion. We first perform an analysis of the temporal evolution of partisan news sharing. We confirm that the invasion stimulates discussion in the observed communities, accompanied by an increased volume of partisan news sharing. Next, we characterize users' response to such sharing. We observe that partisan bias plays a role in narrowing its propagation. More biased media is less likely to be spread across multiple subreddits. However, we find that partisan news sharing attracts more users to engage in the discussion, by generating more comments. We then built a predictive model to identify users likely to spread partisan news. The prediction is challenging though, with 61.57% accuracy on average. Our centrality analysis on the commenting network further indicates that the users who disseminate partisan news possess lower network influence in comparison to those who propagate neutral news.

翻译：自俄罗斯入侵乌克兰以来，大量带有偏见和党派色彩的新闻通过社交媒体平台传播。鉴于这可能引发更广泛的社会问题，我们认为理解党派新闻分享如何影响用户沟通对于优化网络社区治理至关重要。本文开展了党派新闻分享的测量研究，旨在刻画此类分享在影响用户沟通中的作用。我们的分析涵盖了与俄乌冲突相关的六个Reddit社区长达八个月的数据集。首先，我们分析了党派新闻分享的时间演化规律，证实冲突事件刺激了目标社区的讨论热度，并伴随着党派新闻分享量的增长。随后，我们刻画了用户对此类分享的响应特征，观察到党派偏倚会限制新闻传播范围：偏见程度越高的媒体越难以在多个子版块间扩散。然而，我们发现党派新闻分享能够通过产生更多评论来吸引更多用户参与讨论。最后，我们构建了预测模型以识别可能传播党派新闻的用户群体，但预测任务具有挑战性，平均准确率仅为61.57%。对评论网络的中心性分析进一步表明，传播党派新闻的用户相较于传播中立新闻的用户拥有更低的网络影响力。