Enhancing local live tweet stream to detect news
Enhancing local live tweet stream to detect news
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DOI：
10.1007/s10707-019-00392-9
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发表时间：
2018-11
期刊：
影响因子：
2
通讯作者：
Hong Wei;Jagan Sankaranarayanan;H. Samet
中科院分区：
文献类型：
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作者：
Hong Wei;Jagan Sankaranarayanan;H. Samet
Twitter捕捉了关于真实世界新闻的宝贵信息，涵盖了从总统选举等大型国家/国际新闻到当地农贸市场等小型地方新闻的广泛范围。检测和提取本地的小新闻是一个具有挑战性的问题，也是这项工作的重点。主要的挑战在于识别这些与当地感兴趣的地区相对应的小故事，与全国性的故事相比，这些故事通常更难检测，因为可能只有少数关于当地故事的推文。提出了一种称为萤火虫的系统，该系统克服了数据稀疏性，并且每天从大都市地区捕获数千个本地故事（例如，波士顿）。关键的想法在于结合Twitter中本地实时推文流的增强，“本地感知”关键字的识别，并使用这些关键字来聚类推文。实验表明，该系统具有显着更高的召回率在一组有代表性的地方新闻机构，并在同一时间，优于基线方法TwitterStand。更重要的是，结果还表明，通过利用增强的本地实时推文流，我们的系统比仅在地理标记的推文上工作的方法发现了更多的本地新闻，即，那些嵌入了GPS坐标值的。
Twitter captures invaluable information about real-world news, spanning a wide scale from large national/international stories like a presidential election to small local stories such as a local farmers market. Detecting and extracting small news for a local place is a challenging problem and the focus of this work. The main challenge lies in identifying these small stories that correspond to a local area of interest, which are typically harder to detect compared to national stories in the sense that there may be just a handful of tweets about a local story. A system, called Firefly, is proposed that overcomes the data sparsity and captures thousands of local stories per day from a metropolitan area (e.g., Boston). The key idea lies in combining the enhancement of a local live tweet stream in Twitter, the identification of "locality-aware" keywords, and using these keywords to cluster tweets. Experiments show that the proposed system has a significantly higher recall over a set of representative local news agencies, and at the same time, outperforms the baseline approach TwitterStand. More importantly, the results also demonstrate that our system, by utilizing the enhanced local live tweet stream, discovers much more local news than the methods working only on geotagged tweets, i.e., those with embedded GPS coordinate values.