关于在线信息行为的数据的日益增长的可用性为政治传播研究带来了新的可能性。但是，这些数据的数量和多样性使它们难以分析，并提示需要开发自动化内容方法，这些方法依赖于广泛的自然语言处理技术（例如机器学习或基于神经网络）。在本文中，我们讨论如何使用这些技术来检测不同平台的政治内容。使用三个验证数据集，其中包括来自在线平台的各种政治和非政治文本文档，我们系统地比较了依赖词典，监督机器学习或神经网络的三组检测技术的性能。我们还使用大型检测模型的大集合（n = 66）检查了不同数据预处理模式（例如，驱动和停止词）对这些技术的低成本实现的影响。我们的结果表明，预处理对模型性能的影响有限，与基于神经网络和机器学习模型所获得的嘈杂数据的最佳结果相比，基于嘈杂的数据的基于词典模型的更强性能。

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our ultra-fast GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial.