SciLander：绘制科学新闻图景
SciLander: Mapping the Scientific News Landscape
摘要：
COVID-19大流行助长了社交媒体和整个网络上错误信息的传播。这种被称为“信息流行病”的现象将信息的真实性和可信度方面的挑战推向了新的高度，因为它在误导性内容中大量引入了看似科学和技术的内容。尽管现有的大量工作是建模和预测错误信息，但对具有固有不确定性和不断变化的发现（如COVID-19）的非常复杂的科学主题的覆盖，提供了许多现有工具无法轻松解决的新挑战。为了解决这些问题，我们介绍了SciLander，一种用于学习基于科学主题的新闻源报道的表示方法。SciLander为新闻来源提取了四个异质指标;两个通用指标，捕捉（1）来源之间新闻故事的复制，以及（2）使用相同的术语来表示不同的事物（即，术语的语义变化），以及两个科学指标，即（1）行话的使用和（2）对具体引用的立场。我们使用这些指标作为源协议的信号，对正（相似）和负（不相似）样本进行采样，并将它们结合在一个统一的框架中，训练具有三重边际损失目标的无监督新闻源嵌入。我们在一个新的COVID-19数据集上评估我们的方法，该数据集包含来自500个来源的近100万篇新闻文章，时间跨度为自2020年疫情开始以来的18个月。我们的研究结果表明，我们的模型学习的特征在新闻准确性分类任务上优于最先进的基线方法。此外，聚类分析表明，学习表征编码信息的可靠性，政治倾向，和党派偏见，这些来源。
英文摘要：
The COVID-19 pandemic has fueled the spread of misinformation on social media and the Web as a whole. The phenomenon dubbed `infodemic' has taken the challenges of information veracity and trust to new heights by massively introducing seemingly scientific and technical elements into misleading content. Despite the existing body of work on modeling and predicting misinformation, the coverage of very complex scientific topics with inherent uncertainty and an evolving set of findings, such as COVID-19, provides many new challenges that are not easily solved by existing tools. To address these issues, we introduce SciLander, a method for learning representations of news sources reporting on science-based topics. SciLander extracts four heterogeneous indicators for the news sources; two generic indicators that capture (1) the copying of news stories between sources, and (2) the use of the same terms to mean different things (i.e., the semantic shift of terms), and two scientific indicators that capture (1) the usage of jargon and (2) the stance towards specific citations. We use these indicators as signals of source agreement, sampling pairs of positive (similar) and negative (dissimilar) samples, and combine them in a unified framework to train unsupervised news source embeddings with a triplet margin loss objective. We evaluate our method on a novel COVID-19 dataset containing nearly 1M news articles from 500 sources spanning a period of 18 months since the beginning of the pandemic in 2020. Our results show that the features learned by our model outperform state-of-the-art baseline methods on the task of news veracity classification. Furthermore, a clustering analysis suggests that the learned representations encode information about the reliability, political leaning, and partisanship bias of these sources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
