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- W2890723352 abstract "In recent years, one of the most extensive research topics in social media analysis has been event detection. Most of the approaches use fixed temporal and spatial resolutions to detect events. In this paper, we employ a procedure for the detection of economic and financial events using news analytics data. We use an algorithm to compute a data similarity graph at chosen scales and detect economic and financial events simultaneously by a single graph-based clustering process. Experimental results on real world data collected from news analytics providers demonstrate the effectiveness of the event detection procedure based on real-time news analytics data." @default.
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- W2890723352 date "2018-01-01" @default.
- W2890723352 modified "2023-09-27" @default.
- W2890723352 title "Graph-Based Clustering Approach for Economic and Financial Event Detection Using News Analytics Data" @default.
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- W2890723352 doi "https://doi.org/10.1007/978-3-030-01159-8_26" @default.
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