基于循环神经网络的异步社交媒体流行度建模
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
摘要：
了解和预测在线项目的流行度是社会媒体分析中一个重要的开放性问题。最近在数据驱动的预测以及将受欢迎程度与外部促销联系起来方面取得了相当大的进展。然而，现有的方法通常专注于单一的外部影响源，而对于许多类型的在线内容，如YouTube视频或新闻文章，注意力是由多个异构源同时驱动的-例如微博或传统媒体报道。在这里，我们提出了RNN-MAS，一种用于异步流建模的递归神经网络。它是一个序列生成器，通过联合推理连接不同粒度的多个流。RNN-MAS不仅比目前最先进的Youtube流行度预测系统高出17%，而且还能捕捉复杂的动态，例如看不见的影响的季节性趋势。我们定义了两个新指标：推广分数量化了Youtube视频从一个推广单位中获得的受欢迎程度;响度级别捕捉了特定用户对视频的推文效果。我们使用响度水平来比较由一个高关注用户（在前1%的最关注用户中）推广的视频与由一组中等关注用户推广的视频的效果。我们发现，结果取决于推广的内容类型：超级用户在推广Howto和Gaming视频方面更成功，而普通用户群体对Activism视频更具影响力。这项工作提供了更准确和可解释的流行预测，以及计算工具，为内容生产者和营销人员分配资源的促销活动。
英文摘要：
Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously - e.g. microblogs or traditional media coverage. Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. We show RNN-MAS not only to outperform the current state-of-the-art Youtube popularity prediction system by 17%, but also to capture complex dynamics, such as seasonal trends of unseen influence. We define two new metrics: promotion score quantifies the gain in popularity from one unit of promotion for a Youtube video; the loudness level captures the effects of a particular user tweeting about the video. We use the loudness level to compare the effects of a video being promoted by a single highly-followed user (in the top 1% most followed users) against being promoted by a group of mid-followed users. We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos. This work provides more accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.
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。
