Junk short message filter method

Abstract

The invention discloses a junk short message (SM) filter method, which comprises the following steps: step 10, deleting words which are not related with the SM texts in the SMs; step 20, calculating file fingerprints of the SMs in which the words not related with the SM texts are deleted; and step 30, if the number of the file fingerprints of the SMs in which the words not related with the SM texts are deleted exceeds a first preset threshold value, judging that the SMs are junk SMs. The method is a filter method based on the file fingerprints, which refers to not only similarity of the SMs in texts, but also mode of transmission of the junk SMs. The junk short message filter method of the invention has the advantages that identification and filter of the junk SMs are fast, thereby being suitable for occasions with higher requirements on instantaneity; and the filter method is not affected by change of nonessential texts in the junk SMs, thereby being capable of effectively dealing with continuous changes of the junk SMs.

Description

technical field [0001] The invention relates to the technical field of text information processing, in particular to a method for filtering spam short messages. Background technique [0002] Short message is one of the ways of information exchange that people often use. At the same time, spam text messages have also begun to flood gradually. Statistics show that among the huge number of short messages, about 30% belong to spam text messages. For ordinary users, spam text messages seriously interfere with daily life; for operators, spam text messages occupy a large amount of traffic space and reduce information transmission efficiency. [0003] The main contents of spam messages include advertising information, pornographic information, false winning information, fraudulent information and pranks, among which pornographic information and false winning information are the most common. [0004] In the prior art, the most common filtering methods include: [0005] 1. Spam SMS...

AI Technical Summary

Problems solved by technology

[0007] (1) The "manslaughter rate" is relatively high. Regardless of whether word segmentation is performed or not, spam SMS filtering based on matching will cause a lot of "manslaughter"

[0008] (2) Cannot flexibly respond to changes in spam messages

In order to build a training corpus of a certain scale, it takes a lot of money, and the training corpus must be updated continuously, otherwise it will be difficult to keep up with the pace of spam SMS changes

[0017] (2) Spam SMS filtering efficiency is low, not suitable for high real-time occasions

This method not only increases the computing pressure on the client, but also makes it easy to filter messages from strangers

[0028] To sum up, the spam SMS filtering methods in the prior art either filter only based on the content of the spam SMS, or filter only based on the propagation mode of the spam SMS, and cannot effectively identify and filter the spam SMS

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