Analyzing issue cycles usually begins with observing selected events and then tracking the course of media coverage. This approach collapses when the events of interest are hidden, overlain, or even distorted by extensive coverage of other events. One such complicated case is news about terrorism in Africa. While previous studies have started from single media hypes, we propose modeling the general pattern of such issue cycles with distributed lag models on a large-scale data basis. In order to assess the utility of distributed lag models, two basic principles of issue cycles are derived from theory and empirically tested. Furthermore, using the Global Database of Events, Language, and Tone, we evaluate the usefulness of automated methods for news research. Although the data are quite noisy, automated content analysis combined with distributed lag models is a promising approach for studying issue cycles. The model can be used to visualize issue cycles. In the case of news about terrorism in Africa, we found a sudden increase in coverage, followed by a second local maximum after a few weeks.

分析发布周期通常从观察选定事件开始，然后跟踪媒体报道的过程。当感兴趣的事件被其他事件的广泛覆盖而隐藏，覆盖甚至扭曲时，这种方法将崩溃。一个如此复杂的案例是有关非洲恐怖主义的新闻。虽然以前的研究是从单一媒体炒作开始的，但我们建议在大规模数据的基础上使用分布式滞后模型对此类发行周期的一般模式进行建模。为了评估分布式滞后模型的效用，从理论上得出了发行周期的两个基本原理，并进行了实证检验。此外，使用事件，语言和语气全球数据库，我们评估了新闻报道自动化方法的实用性。尽管数据很嘈杂，自动化的内容分析与分布式滞后模型相结合是研究问题周期的一种有前途的方法。该模型可用于可视化发布周期。就有关非洲恐怖主义的新闻而言，我们发现报道的范围突然增加，几周后又出现第二次当地最高记录。