在多个社交网络中挖掘5亿名主题专家
Mining Half a Billion Topical Experts Across Multiple Social Networks
摘要：
挖掘社交媒体上的话题专家是一个因其广泛应用而受到广泛关注的问题。在这里，我们提出了第一项研究，该研究结合了来自四个主要社交网络的数据- Twitter，Facebook，Google+和LinkedIn，以及维基百科图表和互联网网页文本和元数据，以排名全球用户群体中的主题专家。我们对来自各种数据源（如消息文本、用户列表、网页、社交图表和维基百科）的37个特征进行了深入分析。这项大规模研究包括90天滑动窗口内的120多亿条消息和580亿条社交图边。比较表明，来自Twitter列表，维基百科，互联网网页和Twitter追随者的功能是特别好的专业指标。我们使用这些特征在包含近90，000个标签的大型地面真实数据集上训练专业知识排名模型。该模型应用于一个生产系统，该系统每天对9,000多个主题领域的6.5亿多名专家进行排名。我们提供的结果和例子，我们的专家排名系统的有效性，以及经验验证。最后，我们通过开放的REST API提供主题专业知识数据，以供更广泛的使用。
英文摘要：
Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks -- Twitter, Facebook, Google+ and LinkedIn, along with the Wikipedia graph and internet webpage text and metadata, to rank topical experts across the global population of users. We perform an in-depth analysis of 37 features derived from various data sources such as message text, user lists, webpages, social graphs and wikipedia. This large-scale study includes more than 12 billion messages over a 90-day sliding window and 58 billion social graph edges. Comparison reveals that features derived from Twitter Lists, Wikipedia, internet webpages and Twitter Followers are especially good indicators of expertise. We train an expertise ranking model using these features on a large ground truth dataset containing almost 90,000 labels. This model is applied within a production system that ranks over 650 million experts in more than 9,000 topical domains on a daily basis. We provide results and examples on the effectiveness of our expert ranking system, along with empirical validation. Finally, we make the topical expertise data available through open REST APIs for wider use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
