Time series analysis method of social network events

A technology of time series relationship and analysis method, applied in text database clustering/classification, special data processing applications, instruments, etc., can solve problems such as unobvious text distribution characteristics, and achieve the effect of improving recognition accuracy

Active Publication Date: 2019-01-04
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, this method needs to estimate the distribution of event text streams in advance. In practice, the text distribution characteristics of short text events in social networks may not be obvious, which brings great challenges to the establishment of distribution models.

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  • Time series analysis method of social network events
  • Time series analysis method of social network events
  • Time series analysis method of social network events

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[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0038] figure 1 It is a schematic flowchart of the method for analyzing the temporal relationship of social network events in the present invention. A method for analyzing the temporal relationship of social network events, comprising the following steps:

[0039] A. Obtain event detection result data, which is a collection of event short text clusters;

[0040] B. Perform event short text cluster time series extraction on the event short text cluster set according to the number of short text words and the number of short texts in the event detection result data;

[0041] C. Traverse the event s...

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Abstract

The invention discloses a social network event timing relation analysis method, which comprises the following steps: obtaining event detection result data, extracting event short text cluster time series, dynamically adjusting time series, and constructing a quantile-Quantile diagram to analyze the temporal relationship of events. At first, the invention extract the event short text cluster time series from the event short text cluster set, matches the time series of the event with a dynamic time regularization algorithm, then quantitatively calculates the time sequence distance of the time sequence correspondence relationship between the event short text clusters according to the matched result, and quantiles the time sequence distance of the event short text clusters according to the time sequence distance of the event short text clusters. Quantile graph visualization method qualitatively analyzes the temporal relationships among event short text clusters, which can significantly improve the recognition accuracy of event time series relationships in social networks.

Description

technical field [0001] The invention belongs to the technical field of event detection and tracking, and in particular relates to a method for analyzing the temporal relationship of social network events. Background technique [0002] Topic Detection and Tracking (TDT) technology is derived from the earlier Event Detection and Tracking (EDT) technology. The original TDT research defined topics as events. Events were originally described as things that happened at specific times and places. With the development of TDT technology, the definition of topic becomes more extensive. A topic includes not only the subsequent events caused or caused by the original event, but also other events or activities related to it. TDT defines a topic as: a topic consists of a seed event or activity and events or activities directly related to it. The tasks of TDT include segmentation tasks for news reports, tracking tasks for known topics, detection tasks for unknown topics, detection task...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F16/35
Inventor 费高雷周磊胡光岷
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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