The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: Tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results against TREC2014 and our self collected tweetMarch datasets demonstrate that the proposed method is able to provide more than 30 percent improvement compared to other advanced topic derivation methods.

中文翻译：

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什么和谁？通过交互和文本识别 Twitter 中的主题

Twitter 环境中不断流动的大量信息使得主题推导变得必不可少。它确实在各种基于 Twitter 的应用程序中发挥着重要作用，包括内容推荐、新闻摘要、市场分析等。主题推导方法通常基于推文内容的语义特征。由于推文本质上很短，因此此类方法会受到数据稀疏性的影响。为了缓解这个问题，本文提出了一种结合推文文本相似性和交互措施的主题推导方法。除了推文内容之外，该方法还考虑了推文之间的多种交互类型：提及同一个人的推文、回复和转发。主题推导是通过两步矩阵分解过程完成的。我们对几个 Twitter 数据集进行了大量实验，以揭示所考虑的各种特征的个体和综合影响。我们针对 TREC2014 和我们自收集的 tweetMarch 数据集的实验结果表明，与其他高级主题推导方法相比，所提出的方法能够提供 30% 以上的改进。