基于主题模型的微博转发行为预测

在全部微博内容中,由用户转发而产生的信息占有非常大的比例。同时,内容的转发也是微博中信息传播的主要途径。因此,用户的转发行为有着重要的研究价值,可应用于社交营销、微博检索、热点事件预测等领域中。该文中,我们通过分析所收集的大量真实的新浪微博数据,发现影响用户转发行为的一些因素: 微博作者、用户兴趣以及微博热度。基于这些发现,该文提出了一种新颖的基于LDA模型的方法,综合利用以上3个特征预测用户转发行为。为了对该方法进行评价,我们利用收集的大量的微博数据及对应的社交网络结构模拟真实用户环境。实验表明,该方法的性能优于目前最好的方法,F值比其他基线方法高出35%—45%。

Retweet Behavior Prediction Using Topic Model

In the Microblogging service, retweeting is a key behavior for information diffusion. The task of predicting retweet behavior is an important step for various social network applications, such as social marketing, microblog retrieval, popular event prediction, and so on. We collect a large number of microblogs and the corresponding social networks from Sina Weibo, and discover several factors which affect users' retweet behavior: author of the tweet, user interest and popularity of the tweet. Then we propose a novel retweet behavior prediction method based on LDA model to combine structural, textual and author information. To evaluate the proposed method, we simulate the real user environment on the constructed dataset. Experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods. The relative improvement of the proposed over the baseline method is more than 35%—45% in terms of F1-Score.