Twitter enables users to browse and access the latest newsrelated content. However, given user's interest in a particular newsrelated tweet, searching for related content may be a tedious process. Formulating an effective search query is not a trivial task. And due to the often small size of smart phone screens, instead of typing, users always prefer click-based operations to retrieve related content. To address these issues, we introduce a new paradigm for news-related Twitter search called Search by Tweet(SbT). In this paradigm, a user submits a particular tweet which triggers a search task to retrieve further related tweets. In this paper, we formalize the SbT problem and propose an effective and efficient framework implementing such a functionality. At the core, we model the public Twitter stream as a dynamic graph-of-words, reflecting the importance of both words and word correlations. Given an input tweet, our framework utilizes the graph model to generate an implicit query. Our techniques demonstrate high efficiency and effectiveness as evaluated using a large-scale Twitter dataset and a user study.
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机译：Twitter使用户能够浏览和访问最新的新闻相关内容。但是，考虑到用户对特定新闻相关的推文的兴趣，搜索相关内容可能是一个繁琐的过程。制定有效的搜索查询并非易事。而且由于智能手机屏幕通常很小，而不是键入，用户始终喜欢基于单击的操作来检索相关内容。为了解决这些问题，我们为新闻相关的Twitter搜索引入了一个新的范式，称为Search by Tweet（SbT）。在此范例中，用户提交特定的tweet，该tweet触发搜索任务以检索其他相关tweet。在本文中，我们将SbT问题形式化，并提出一个有效且高效的框架来实现这种功能。从根本上讲，我们将公共Twitter流建模为动态词图，从而反映了单词和单词相关性的重要性。给定输入推文，我们的框架利用图模型生成隐式查询。通过使用大规模Twitter数据集和用户研究，我们的技术证明了高效率和有效性。
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