arXiv 1010.02372010-10-01cs.CYcs.SIphysics.soc-ph

用随机模型描述和预测Web用户的社会动态

Using Stochastic Models to Describe and Predict Social Dynamics of Web Users

Kristina Lerman, Tad Hogg

备注：: Submitted to ACM TIST Special Issue on Computational Models of Collective Intelligence in the Social Web

摘要：

社交媒体中的内容受欢迎程度分布不均，一些项目受到用户不成比例的关注。预测哪些新提交的项目将成为流行的社交媒体内容的主机和消费者都至关重要。准确和及时的预测将使主机能够通过对内容或广告投放的差异定价来最大限度地提高收入。预测还将为消费者提供一个重要的工具，用于过滤不断增长的内容。然而，由于内容质量与社交媒体网站如何选择突出内容之间的复杂相互作用，预测社交媒体中内容的受欢迎程度具有挑战性。此外，大多数社交媒体网站还选择性地呈现相似用户高度评价的内容，其相似性通过他们的行为或社交网络中的链接来暗示。虽然这些因素使得很难预测流行\emph{a priori}，我们表明，这些网站上的用户行为的随机模型，允许预测流行的基础上，早期用户对新内容的反应。通过整合网站显示内容的各种机制，这些模型改进了基于简单地从早期投票中推断的预测。使用数据从这样一个网站，新闻聚合器Digg，我们展示了如何随机模型的用户行为区分的影响，增加了知名度，由于网络的用户有兴趣的内容。我们发现了广泛的兴趣，从用户社区更普遍感兴趣的故事中识别出网络中用户主要感兴趣的故事（“利基兴趣”）。这种区分对于从用户对故事的早期反应预测故事的最终流行度很有用。

英文摘要：

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions between content quality and how the social media site chooses to highlight content. Moreover, most social media sites also selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions based on simply extrapolating from the early votes. Using data from one such site, the news aggregator Digg, we show how a stochastic model of user behavior distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, identifying stories primarily of interest to users in the network (``niche interests'') from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story.

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。

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英文摘要：

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