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A method and system for discovering causality among social network users combining behavioral sequence and text information

A technology of text information and causality, applied in unstructured text data retrieval, network data retrieval, text database query, etc., can solve problems such as misleading information transmission and ineffective data

Active Publication Date: 2019-01-25
GUANGDONG UNIV OF TECH
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Problems solved by technology

In addition, in the use of time series data, the existing technology basically divides the data into equal intervals. Due to the sparsity, some data will have little effect or even mislead the discovery of information transmission. Therefore, we can use a more appropriate method to find the optimal interval sequence, reconstructing the data

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  • A method and system for discovering causality among social network users combining behavioral sequence and text information
  • A method and system for discovering causality among social network users combining behavioral sequence and text information
  • A method and system for discovering causality among social network users combining behavioral sequence and text information

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Embodiment Construction

[0044] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0045] like figure 1 As shown, a method for discovering causal relationship among social network users that combines behavior sequence and text information includes the following steps:

[0046] S1), write a web crawler through the framework of python+scrapy, construct the target URL according to the user ID, grab the user information and the dynamic data released in the target social network; this implementation mode takes Sina Weibo as an example, by analyzing Sina Weibo The URL of the user’s Weibo page and its parameters are constructed, and the URL corresponding to the user is constructed to start capturing data; the original Weibo data is cleaned, and some data with incomplete information is removed. The cleaned data has two dimensions, namely user and time;

[0047] S2), according to the time information of the data, the data is first divi...

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Abstract

The invention provides a method and system for discovering causality among social network users combining a behavior sequence and text information, which comprises the following steps: S1) data acquisition; S2), preprocessing the data at equal intervals with the smallest time unit; 3) optimize that objective function to find the optimal interval by using the time sequence behavior data; S4) reconstructing the text data in the way of merging the text at the time of merging, and representing the text vectorization; S5) calculating the transfer entropy of the text vector sequences of the two users; 6) prune to obtain a user causality network; 7) store and deriving that us causal network; S8) user causality inquiry and visualization. The invention solves the problem of calculating the transferentropy caused by the sparse user activities; infer the user causality of the social network by using the text data, and the amount of information is more abundant than the pure behavior data; and aninteractive system for inferring, inquiring and deriving the user causality is provided.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a method and system for discovering causal relationships between social network users combined with behavior sequences and text information. Background technique [0002] On social networks, due to the participation of a large number of users, a series of user activity data and user text, audio, and video data are generated. Users can post messages or upload pictures and videos through various online channels. Users can write what they see, hear, and feel in a sentence, and share it with friends anytime, anywhere through a computer or mobile phone; they can also follow their friends' dynamics. [0003] As the number of social network users continues to increase, the user's hobbies and topics of concern can be mined from the user's dynamics. People in data mining and analysis are also increasingly focusing on social networks. There are many scholars at home and abroad who st...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F16/955
Inventor 蔡瑞初谢泳陈薇郝志峰陈炳丰
Owner GUANGDONG UNIV OF TECH
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