Anticipating Attention: On the Predictability of News Headline Tests
Anticipating Attention: On the Predictability of News Headline Tests
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DOI：
10.1080/21670811.2021.1984266
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发表时间：
2021-09-23
影响因子：
5.4
通讯作者：
DeWilde, Burton
中科院分区：
文献类型：
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作者：
Hagar, Nick;Diakopoulos, Nicholas;DeWilde, Burton
新闻标题在新闻受众的在线注意力决策和新闻机构吸引注意力的努力中都发挥着重要作用。大量的研究集中在开发更有效的标题写作的普遍适用的语法。在这项工作中，我们衡量的重要性，一些理论上有动机的文本功能的标题性能。使用由数百家新闻出版商运行的数十万个标题A/B测试的语料库，我们开发并评估了一个机器学习模型来预测标题测试结果。我们发现，该模型表现出适度的性能高于基线，并进一步估计在这一领域的基于内容的预测的经验上限，表明非基于内容的因素在测试结果中的重要作用。总之，这些结果表明，任何特定的标题写作方法只有边际影响，理解读者行为和标题上下文是预测新闻注意力决策的关键。
Headlines play an important role in both news audiences' attention decisions online and in news organizations' efforts to attract that attention. A large body of research focuses on developing generally applicable heuristics for more effective headline writing. In this work, we measure the importance of a number of theoretically motivated textual features to headline performance. Using a corpus of hundreds of thousands of headline A/B tests run by hundreds of news publishers, we develop and evaluate a machine-learned model to predict headline testing outcomes. We find that the model exhibits modest performance above baseline and further estimate an empirical upper bound for such content-based prediction in this domain, indicating an important role for non-content-based factors in test outcomes. Together, these results suggest that any particular headline writing approach has only a marginal impact, and that understanding reader behavior and headline context are key to predicting news attention decisions.