Abstract
This chapter presents a framework of detecting bursty topics of correlated news and twitter, and discusses how to integrate the framework into government services. Especially, as a specific application of the proposed framework of detecting bursty topics of correlated news and twitter, this chapter gives an example of collecting news and twitter that are related to “the 2012 London Olympic game” and applying the proposed framework.
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Notes
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In Kleinberg [1], \(\tau (i,j)\) is defined not as \((j - i)\gamma\), but as \((j - i)\gamma \ln m\), where \(m\) is the number of batches in the sequence \({\mathbf{B}} = (B_{1} , \ldots ,B_{m} )\). In this chapter, we omit the term \(\ln m\) in this definition for simplicity.
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Those evaluation results are still based on inside evaluation, which means that the two parameters \(s\) and \(\gamma\) are optimized with the news and tweets for evaluation we show in this chapter. However, we tune the two parameters across the 34 topics for evaluation, where we observed that the optimal values of the two parameters are mostly consistent across 34 topics for evaluation. Parameter optimization with held-out training data is one of our future work.
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Although Table 2 only shows the evaluation results for 34 topics that are relevant to “the London Olympic games”, even for the whole 50 topics, precision of the detected bursty topics is about 90 % per day/topic for both news articles and tweet texts.
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Utsuro, T., Inoue, Y., Imada, T., Yoshioka, M., Kando, N. (2015). Detecting Bursty Topics of Correlated News and Twitter for Government Services. In: Nepal, S., Paris, C., Georgakopoulos, D. (eds) Social Media for Government Services. Springer, Cham. https://doi.org/10.1007/978-3-319-27237-5_7
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