Optimizing Content with A/B Headline Testing: Changing Newsroom Practices
Optimizing Content with A/B Headline Testing: Changing Newsroom Practices
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DOI：
10.17645/mac.v7i1.1801
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发表时间：
2019-01-01
影响因子：
3.1
通讯作者：
Diakopoulos, Nicholas
中科院分区：
文献类型：
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作者：
Hagar, Nick;Diakopoulos, Nicholas
受众分析是现代新闻编辑室越来越重要的一部分，因为出版商寻求最大限度地扩大其内容的覆盖面和商业潜力。在收集到的大量受众数据的基础上，可以应用算法方法，根据历史模式预测和优化内容的性能。这项工作特别关注围绕新闻编辑室中A/B标题测试的使用的内容优化实践。使用这种方法，数字新闻编辑室可能会对每篇文章进行多达12个标题的观众测试，收集数据，使优化算法能够收敛到在某些指标（如点击率）方面最佳的标题。本文介绍了一项访谈研究的结果，该研究阐明了A/B测试算法正在改变工作流程和标题写作实践的方式，以及塑造这一过程的社会动态及其在美国新闻编辑室的实施。
Audience analytics are an increasingly essential part of the modern newsroom as publishers seek to maximize the reach and commercial potential of their content. On top of a wealth of audience data collected, algorithmic approaches can then be applied with an eye towards predicting and optimizing the performance of content based on historical patterns. This work focuses specifically on content optimization practices surrounding the use of A/B headline testing in newsrooms. Using such approaches, digital newsrooms might audience-test as many as a dozen headlines per article, collecting data that allows an optimization algorithm to converge on the headline that is best with respect to some metric, such as the click-through rate. This article presents the results of an interview study which illuminate the ways in which A/B testing algorithms are changing workflow and headline writing practices, as well as the social dynamics shaping this process and its implementation within US newsrooms.