The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention we develop a keyword-based computational measure comparing an online post to the scientific article's abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time - posing a critical concern for researchers, policymakers, and citizens alike - but suggest that multi-platform discussions may improve information retention overall.

翻译：公众对准确科学传播的关注，在近期公共卫生危机中尤为凸显，揭示了内容在线上传播时常丢失关键信息的问题。然而，由于数据收集的挑战，对该现象的多平台分析仍十分有限。通过收集Altmetric LLC追踪的研究提及数据，我们考察了博客网站、Facebook、新闻网站、Twitter和维基百科上超过400万条在线帖子中提及的9765篇最受关注的科学文章的信息保留情况。为此，我们提出一个基于突发性分析的框架，用于研究不同时间跨平台的科学在线讨论。为衡量信息保留程度，我们开发了一种基于关键词的计算方法，将在线帖子与科学文章摘要进行比对。我们使用领域专家标注的基准数据评估该衡量方法。主要发现有三：其一，信息保留普遍处于较低水平，呈现明显的损失轨迹，但注意力突发始于社交媒体时例外；其二，不同平台的信息保留程度存在显著差异；其三，涉及更多平台的讨论序列往往与更高的信息保留率相关。这些发现揭示了信息随时间推移的显著流失趋势——这对研究者、政策制定者及普通公众构成关键警示——但同时也表明，多平台讨论可能整体上改善信息保留状况。