Identifying Modes of User Engagement with Online News and Their Relationship to Information Gain in Text
Identifying Modes of User Engagement with Online News and Their Relationship to Information Gain in Text
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DOI：
10.1145/3178876.3186180
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Nir Grinberg
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文献类型：
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作者：
Nir Grinberg
先前的工作建立了服务器记录的用户参与度测量（例如点击率）的好处，以改善搜索引擎和推荐系统的结果。尽管出版商现在有能力使用客户端日志以精细的分辨率测量数百万人如何与他们的内容进行交互，但客户端对点击后行为的测量相对较少受到关注。在这项研究中，我们研究了用户参与的模式，在一个大型的，客户端日志数据集超过770万页面浏览量（包括移动的和非移动的设备）的66,821篇新闻文章，从七个流行的新闻出版商。对于每个页面视图，我们使用三个汇总统计数据：停留时间，用户在页面上到达的最远位置，以及通过任何形式的输入（触摸，鼠标移动等）与页面交互的数量。我们发现，这些汇总统计数据的简单转换揭示了六种典型的阅读模式，范围从扫描到广泛的阅读，并持续跨网站。此外，我们开发了一种新的衡量文本中的信息增益的方法，以捕捉文章主体中思想的发展，并研究信息增益与文章参与度的关系。最后，我们表明，我们的新措施的信息增益是特别有用的预测阅读的新闻文章出版前，该措施捕捉到独特的信息，否则。
Prior work established the benefits of server-recorded user engagement measures (e.g. clickthrough rates) for improving the results of search engines and recommendation systems. Client-side measures of post-click behavior received relatively little attention despite the fact that publishers have now the ability to measure how millions of people interact with their content at a fine resolution using client-side logging. In this study, we examine patterns of user engagement in a large, client-side log dataset of over 7.7 million page views (including both mobile and non-mobile devices) of 66,821 news articles from seven popular news publishers. For each page view we use three summary statistics: dwell time, the furthest position the user reached on the page, and the amount of interaction with the page through any form of input (touch, mouse move, etc.). We show that simple transformations on these summary statistics reveal six prototypical modes of reading that range from scanning to extensive reading and persist across sites. Furthermore, we develop a novel measure of information gain in text to capture the development of ideas within the body of articles and investigate how information gain relates to the engagement with articles. Finally, we show that our new measure of information gain is particularly useful for predicting reading of news articles before publication, and that the measure captures unique information not available otherwise.