Predicting consumer behavior with Web search
Predicting consumer behavior with Web search
复制标题
DOI：
10.1073/pnas.1005962107
复制
发表时间：
2010-10-12
影响因子：
11.1
通讯作者：
Watts, Duncan J.
中科院分区：
文献类型：
--
作者：
Goel, Sharad;Hofman, Jake M.;Watts, Duncan J.
最近的研究表明，网络搜索量可以“预测现在”，这意味着它可以用来准确地跟踪结果，如失业率，汽车和房屋销售，以及疾病流行率在真实的时间。在这里，我们表明，消费者在网上搜索的内容也可以提前几天甚至几周预测他们的集体未来行为。具体来说，我们使用搜索查询量来预测故事片的首周末票房收入，视频游戏的第一个月销售额，以及Billboard Hot 100排行榜上的歌曲排名，发现在所有情况下，搜索计数都高度预测未来的结果。我们还发现，搜索次数通常会提高基线模型在其他公开数据上的性能，其中提高幅度从适度到戏剧性不等，具体取决于所讨论的应用程序。最后，我们重新审视了之前关于跟踪流感趋势的工作，并表明，也许令人惊讶的是，搜索数据相对于简单自回归模型的实用性并不高。我们的结论是，在没有其他数据源的情况下，或在预测性能的小的改进是材料，搜索查询提供了一个有用的指南，在不久的将来。
Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.