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Adaptive Topic Follow-Up on Twitter

机译:Twitter上的自适应主题跟进

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Twitter provides a strictly limited API that makes it difficult for a simple search using pre-defined textual patterns to provide satisfying coverage of the topic of interest. This paper discusses a tweet acquisition system, that queries Twitter API using a set of key phrases, then analyzes the retrieved tweets. In order to achieve better coverage of the searched topic, the system employs an adaptive query generation mechanism that iteratively enriches the set of textual relevant patterns based on the previously collected tweets using an explore-exploit strategy. The paper also demonstrates an application called Topic Follow-up on Twitter (TFT) that is built on top of the acquisition system and aims at linking tweets with online articles. It first extracts a set of key phrases from the submitted news article and then utilizes the acquisition and analysis components of the system. Using this application, we will show how the adaptive searching mechanism of the tweet acquisition system improves the coverage of the topic of interest. Video: http://bit.ly/2kqkikB.
机译:Twitter提供了一个严格限制的API,这使得使用预定义的文本模式进行简单搜索来提供令人满意的感兴趣主题的覆盖变得困难。本文讨论了一个推文获取系统,该系统使用一组关键短语查询Twitter API,然后分析检索到的推文。为了更好地覆盖搜索到的主题,系统采用了自适应查询生成机制,该机制使用探寻策略基于先前收集的推文迭代地丰富了文本相关模式的集合。本文还演示了一个名为“ Twitter上的主题跟进”(TFT)的应用程序,该应用程序建立在获取系统的基础上,旨在将推文与在线文章链接起来。它首先从提交的新闻文章中提取一组关键短语,然后利用系统的获取和分析组件。使用此应用程序,我们将展示推文获取系统的自适应搜索机制如何提高关注主题的覆盖范围。视频:http://bit.ly/2kqkikB。

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