CN107180077A - A kind of social networks rumour detection method based on deep learning - Google Patents
A kind of social networks rumour detection method based on deep learning Download PDFInfo
- Publication number
- CN107180077A CN107180077A CN201710252179.3A CN201710252179A CN107180077A CN 107180077 A CN107180077 A CN 107180077A CN 201710252179 A CN201710252179 A CN 201710252179A CN 107180077 A CN107180077 A CN 107180077A
- Authority
- CN
- China
- Prior art keywords
- sample
- sentence
- detected
- data
- word
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013135 deep learning Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 title claims abstract description 28
- 239000011159 matrix material Substances 0.000 claims abstract description 39
- 238000000034 method Methods 0.000 claims abstract description 32
- 238000012549 training Methods 0.000 claims abstract description 21
- 238000012360 testing method Methods 0.000 claims abstract description 5
- 230000000694 effects Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 244000097202 Rathbunia alamosensis Species 0.000 description 3
- 235000009776 Rathbunia alamosensis Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000003058 natural language processing Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/355—Class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/374—Thesaurus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Economics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Machine Translation (AREA)
Abstract
The present invention discloses a kind of social networks rumour detection method based on deep learning, including：Collect social network data and be used as sample data；Sample data is marked and participle, builds dictionary and the sample term vector for the regular length for being element is numbered with numeral of the sample word in dictionary；The sample word number that sample sentence is included in sample data is defined to fixed value；Using Word2Vec methods build sample sentence matrix, sample sentence matrix row vector group for sample sentence in all sample words sample term vector；Sample sentence matrix is trained using deep learning method LSTM, multi-level training pattern is built；Sentence matrix to be detected is built using with building sample sentence matrix identical method；Classification and Detection is carried out to sentence matrix to be detected according to multi-level training pattern, the rumour testing result of social network data to be detected is obtained.The present invention can carry out effective detection to social networks rumour.
Description
Technical field
The present invention relates to natural language processing and machine learning techniques field.Depth is based on more particularly, to one kind
The social networks rumour detection method of habit.
Background technology
In recent years, propagation of the rumour in network social intercourse media grows in intensity.The Chinese Academy of Social Sciences's issue in 2016《China
New media development report》In, just microblogging and wechat rumour etc. are analyzed.The rumour propagated in social media causes society
Can be panic and unstable, also damage the image of country and government.Therefore how social networks rumour is effectively detected, it is quickly right
Its is handled, and is urgent problem to be solved.
At present, for the detection of rumour on social networks, mainly based on desk checking and keyword retrieval.It is micro- with Sina
Exemplified by rich, the mode of user's report, official investigation and artificial judgment is mainly taken in the processing to rumour at present, and which results in processing
Efficiency is low, time lag, tackle not in time the problems such as.How just classification or early warning are carried out to rumour to improving reply social network
The effect of network rumour has great significance.
Existing machine learning method, it is all generally one-dimensional feature that it, which is inputted, and this gives processing substantial amounts of text data band
Challenge is carried out.If the sentence in a file is all flattened as vector, dimension can be caused to explode and be difficult to handle.How to adopt
Vocabulary and sentence are expressed with the mode quantized, is a major issue in natural language processing.It is this expression contribute to by
Natural language is converted into the form for being available for computer or algorithm process.Word2Vector is a kind of method that Google puts forward,
Its meaning is can be with high-dimensional vector from multiple directions one word of sign, so as to improve numeral using binary form
Expression scope.On the basis of this, by calculating vectorial distance, it is possible to calculate the close degree of vocabulary.
Deep learning is one of study hotspot in machine learning field, and concept source is in artificial neural network.Depth
Habit usually contains multiple hidden layers, so as to represent attribute classification or feature from more abstract angle, to find data
Distributed nature is represented.Convolutional neural networks (CNN) are a kind of wide variety of deep learning algorithms, using local receptor field,
The technologies such as weights are shared, sub-sampling, to realize the consistency to displacement, proportional zoom and some other deformation operations.This
Outside, typical deep learning method also includes depth confidence network (DBN) and depth Boltzmann machine (DBM) etc..These depths
Degree learning method has a wide range of applications in terms of image procossing, speech recognition.Different from more paying close attention to position in image procossing
Relativeness, effectively integrating or reconstructing for adjacent position information is more paid close attention in text-processing field.Thus in this field
Recurrent neural network (RNN) is then using more.A kind of implementation method of recurrent neural network is exactly length Memory Neural Networks
(LSTM), a kind of simplest form is that a neuron has three door (Gate) compositions, using door control mechanism so that a god
The information of a period of time can be operationally kept through member, and the interference that inner gradient is not changed is kept in training, this makes
Relation of long standing relation must be learnt to be possibly realized, therefore LSTM is adapted to time series, the processing of elongated sequence, especially natural language.
Accordingly, it is desirable to provide a kind of social networks rumour detection method based on deep learning.
The content of the invention
It is an object of the invention to provide a kind of social networks rumour detection method based on deep learning, to pass through depth
Learning art carries out multi-level training, improves the accuracy rate detected to social networks rumour.
To reach above-mentioned purpose, the present invention uses following technical proposals：
A kind of social networks rumour detection method based on deep learning, comprises the following steps：
Collect social network data and be used as sample data；
Sample data is marked and participle, built with the dictionary of the digital numbering representative sample word of regular length, structure
Build each digital sample term vector for element in being numbered with numeral of the sample word in dictionary；
The sample word number that sample sentence is included in sample data is defined to fixed value；
Sample sentence matrix is built using Word2Vec methods, the row vector group of sample sentence matrix is all samples in sample sentence
The sample term vector of this word, obtains the sample sentence matrix of all sample sentences in sample data；
Sample sentence matrix is trained using deep learning method LSTM, multi-level training pattern is built；
Social network data to be detected is marked and participle, the numeral numbering of each word to be detected is searched in dictionary,
Each digital term vector to be detected for element in the numeral numbering with word to be detected is built, by treating that middle sample sentence to be detected is included
Detection word number is defined to fixed value, and sentence matrix to be detected, the row vector group of sentence matrix to be detected are built using Word2Vec methods
For the term vector to be detected of all words to be detected in sentence to be detected；
Classification and Detection is carried out to sentence matrix to be detected according to multi-level training pattern, social network data to be detected is obtained
Rumour testing result.
Preferably, the sample data includes normal sample data and rumour sample data.
Preferably, the sample word number that sample sentence is included in sample data is defined to fixed value by step also includes：If some
This sample word number included then gives up the word that rear portion has more in sample sentence more than the fixed value, if the sample that certain sample sentence is included
This word number then carries out completion to sample sentence less than the fixed value by adding specific word in sentence tail.
Beneficial effects of the present invention are as follows：
Depth learning technology is applied in the detection of the rumour of social networks by technical scheme of the present invention, can be by nature
Word and sentence in language are expressed as the data format for being available for deep learning to handle, and pass through multi-level training, effectively carry
The treatment effect of social networks rumour in high current natural language processing field, at the same can for social networks rumour point
Analysis provides a kind of feasible method.Technical scheme of the present invention can carry out just classifying to social networks rumour, to the danger of rumour
Dangerous degree is evaluated, so as to reduce the current main complexity handled manually, improves the efficiency of reply rumour,
Reduce the hysteresis quality of existing method.
Brief description of the drawings
The embodiment to the present invention is described in further detail below in conjunction with the accompanying drawings.
Fig. 1 shows the flow chart of the social networks rumour detection method based on deep learning.
Embodiment
In order to illustrate more clearly of the present invention, the present invention is done further with reference to preferred embodiments and drawings
It is bright.Similar part is indicated with identical reference in accompanying drawing.It will be appreciated by those skilled in the art that institute is specific below
The content of description is illustrative and be not restrictive, and should not be limited the scope of the invention with this.
As shown in figure 1, the social networks rumour detection method disclosed by the invention based on deep learning, including following step
Suddenly：
Social network data is collected as sample data, sample data includes normal sample data and rumour sample data；
Sample data is marked and participle, built with the dictionary of the digital numbering representative sample word of regular length, structure
Build each digital sample term vector for element in being numbered with numeral of the sample word in dictionary；
The sample word number that sample sentence is included in sample data is defined to fixed value, if the sample word number that certain sample sentence is included
Then give up the word that rear portion has more in sample sentence more than the fixed value, if the sample word number that certain sample sentence is included is less than the fixed value
Completion then is carried out by adding specific word in sentence tail to sample sentence, specific word is the word without rumour tendency such as " ", " etc. "；
Sample sentence matrix is built using Word2Vec methods, the row vector group of sample sentence matrix is all samples in sample sentence
The sample term vector of this word, obtains the sample sentence matrix of all sample sentences in sample data, wherein, the row vector of sample sentence matrix
Group be sample sentence in all sample words sample term vector for example sample sentence matrix be m*n matrix, then, and m be sample sentence bag
The sample word number contained, n is the length of sample term vector, and the length of sample term vector is numeral numbering of the sample word in dictionary
Length；
Sample sentence matrix is trained using deep learning method LSTM, multi-level training pattern is built；
Social network data to be detected is marked and participle, the numeral numbering of each word to be detected is searched in dictionary,
Each digital term vector to be detected for element in the numeral numbering with word to be detected is built, by treating that middle sample sentence to be detected is included
Detection word number is defined to fixed value, and sentence matrix to be detected, the row vector group of sentence matrix to be detected are built using Word2Vec methods
For the term vector to be detected of all words to be detected in sentence to be detected；
Classification and Detection is carried out to sentence matrix to be detected according to multi-level training pattern, social network data to be detected is obtained
Rumour testing result.
Specific social network data is substituted into below to examine the social networks rumour disclosed by the invention based on deep learning
Survey method is further described.
Social networks rumour detection method based on deep learning, comprises the following steps：
Collect social network data and be used as sample data：Sample data uses the public data of Sina weibo, includes altogether
20228 datas.Wherein rumour sample data is in document《Chinese social media rumour statistical semantic analysis》Disclosed in, include altogether
9097.Normal sample packet as a comparison contains 11131, is collected from microblogging.In implementation process, upset at random
The distribution of the public data of Sina weibo, using first 8000 therein as sample data, remainder is all as to be detected
Social network data；
Sample data is marked and participle, built with the dictionary of the digital numbering representative sample word of regular length, structure
Build each digital sample term vector for element in being numbered with numeral of the sample word in dictionary：The participle instrument increased income is called to sample
Notebook data is marked and participle, is cut into the numeral numbering of each in single word, dictionary and represents a sample word；
The sample word number that sample sentence is included in sample data is defined to fixed value 50, when a sample sentence carries out participle
Afterwards, the quantity of its sample word included then gives up the sample word behind the 50th sample word in sample sentence more than 50；When one
Individual sample sentence is carried out after participle, and the quantity of its sample word included is less than 50, then is entered by adding specific word at sentence end
Row completion.
Sample sentence matrix is built using Word2Vec methods, the row vector group of sample sentence matrix is all samples in sample sentence
The sample term vector of this word, obtains the sample sentence matrix of all sample sentences in sample data, the square that sample sentence matrix is 50*256
Battle array；
Sample sentence matrix is trained using deep learning method LSTM, multi-level training pattern is built：Using opening
The platforms of Keras 1.0 in source are as environment is realized, using the training of sequence (sequence) model realization at many levels.Wherein, sequence
Row model is a kind of typical deep learning implementation method, and multiple training process is attached by it by the way of order, preceding
One layer of output is as the input of current layer, and the input of current layer is used as next layer of output.In this manner it is achieved that in theory
More multi-level training can be realized.In actual applications, it is contemplated that the factor such as situation and over-fitting of amount of training data, lead to
The suitable number of plies is often used according to expertise.In implementation process, employ one layer, two layers, three layers, four layers and five layers this five
Group experimental result is analyzed.Wherein, two layers of following (the output dimension (output_dim) of LSTM settings：128, abandon
Rate (dropout)：0.5)：
Model.add (LSTM (output_dim=128, return_sequences=True))
model.add(Dropout(0.5))
Model.add (LSTM (output_dim=128, return_sequences=True))
model.add(Dropout(0.5))
It is identical with building sample sentence matrix identical method, social network data to be detected is marked and participle,
The numeral numbering of each word to be detected is searched in dictionary, structure is with each numeral in the numeral numbering of word to be detected for the to be detected of element
Term vector, the word number to be detected that middle sample sentence to be detected is included is defined to fixed value 50, and 50* is built using Word2Vec methods
256 sentence matrix to be detected, the row vector group of sentence matrix to be detected is the word to be detected of all words to be detected in sentence to be detected
Vector；
Classification and Detection is carried out to sentence matrix to be detected according to multi-level training pattern, social network data to be detected is obtained
Rumour testing result.
Below the Detection results to rumour are weighed using three kinds of evaluation indexes：One is classification accuracy, refers to calculate just
The normal and malicious data quantity really detected and the ratio of all data bulks；The second is rate of false alarm, refers to normal number
According to the ratio for the quantity and all normal data quantity for being identified as malicious data；The third is verification and measurement ratio, refers to correctly identify
Malicious data quantity and all malicious data quantity ratio.
In implementation process, the effect of deep learning is verified using one to five layer of LSTM models.Wherein one layer LSTM's
As shown in table 1,10 groups of results represent that the iterations of experiment is 10,20 ..., 100 to experimental result respectively.Can from table 1
Go out, 10 groups of iteration can reach 90% to 98% classification accuracy.During each iteration of sample, its order is
Upset at random, therefore under different iterations, the accuracy rate of sample can be different.Wherein maximum can reach
98.72%, rate of false alarm now is 0.21%.
Table 1 one layer model, ten groups of iteration accuracies
The characteristics of in view of deep learning, it is compared using the different training numbers of plies, is respectively compared one to five layer of training
Under model, iterations is 10,30,50,70,90 five groups of situation.Table 2,3,4 presents accuracy rate, rate of false alarm and inspection respectively
Survey rate.In the case of same training number of plies difference iterations, three groups of evaluation indexes have certain difference.But come from average value
See, when iterations increases, accuracy rate and verification and measurement ratio substantially integrally present after first rising and gradually steadily omit downward trend.Five
Best result appears in three layers of training pattern in group, its rate of accuracy reached to 98.34%, and now rate of false alarm is 0.22%, detection
Rate is 96.59%.
2 five groups of embodiment accuracys rate of table
3 five groups of embodiment rate of false alarms of table
4 five groups of embodiment verification and measurement ratios of table
The above-mentioned set simply to a specific social network data carries out the citing of rumour detection, is specifically applying
In, under different data sets and application environment, it can simply apply the social network disclosed by the invention based on deep learning
Network rumour detection method, the algorithm parameter of selection obtains similar effect.The method of deep learning is applied to social activity by the present invention
In media information processing, by handling the initial data obtained in social media, enable to meet deep learning
Call format.The present invention can effectively improve the verification and measurement ratio of rumour information, in the work that can be applied to current public sentiment management, and
And information of the detection in addition to rumour, the information such as violence, pornographic, reaction can be expanded to.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention, for those of ordinary skill in the field, may be used also on the basis of the above description
To make other changes in different forms, all embodiments can not be exhaustive here, it is every to belong to this hair
Row of the obvious changes or variations that bright technical scheme is extended out still in protection scope of the present invention.
Claims (3)
1. a kind of social networks rumour detection method based on deep learning, it is characterised in that this method comprises the following steps：
Collect social network data and be used as sample data；
Sample data is marked and participle, built with the dictionary of the digital numbering representative sample word of regular length, structure with
Numeral is the sample term vector of element in numeral numbering of the sample word in dictionary；
The sample word number that sample sentence is included in sample data is defined to fixed value；
Sample sentence matrix is built using Word2Vec methods, the row vector group of sample sentence matrix is all sample words in sample sentence
Sample term vector, obtain the sample sentence matrix of all samples sentence in sample data；
Sample sentence matrix is trained using deep learning method LSTM, multi-level training pattern is built；
Social network data to be detected is marked and participle, the numeral numbering of each word to be detected is searched in dictionary, is built
With to be detected term vector of each numeral in the numeral numbering of word to be detected for element, by middle sample sentence to be detected include it is to be detected
Word number is defined to fixed value, and sentence matrix to be detected is built using Word2Vec methods, and the row vector group of sentence matrix to be detected is to treat
Detect the term vector to be detected of all words to be detected in sentence；
Classification and Detection is carried out to sentence matrix to be detected according to multi-level training pattern, the ballad of social network data to be detected is obtained
Say testing result.
2. the social networks rumour detection method according to claim 1 based on deep learning, it is characterised in that the sample
Notebook data includes normal sample data and rumour sample data.
3. the social networks rumour detection method according to claim 1 based on deep learning, it is characterised in that step will
The sample word number that sample sentence is included in sample data, which is defined to fixed value, also to be included：If the sample word number that certain sample sentence is included is more than
The fixed value then gives up the word that rear portion has more in sample sentence, right if the sample word number that certain sample sentence is included is less than the fixed value
Sample sentence carries out completion by adding specific word in sentence tail.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710252179.3A CN107180077A (en) | 2017-04-18 | 2017-04-18 | A kind of social networks rumour detection method based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710252179.3A CN107180077A (en) | 2017-04-18 | 2017-04-18 | A kind of social networks rumour detection method based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107180077A true CN107180077A (en) | 2017-09-19 |
Family
ID=59830874
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710252179.3A Pending CN107180077A (en) | 2017-04-18 | 2017-04-18 | A kind of social networks rumour detection method based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107180077A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992469A (en) * | 2017-10-13 | 2018-05-04 | 中国科学院信息工程研究所 | A kind of fishing URL detection methods and system based on word sequence |
| CN108038240A (en) * | 2017-12-26 | 2018-05-15 | 武汉大学 | Based on content, the social networks rumour detection method of user's multiplicity |
| CN108280057A (en) * | 2017-12-26 | 2018-07-13 | 厦门大学 | A kind of microblogging rumour detection method based on BLSTM |
| CN108319666A (en) * | 2018-01-19 | 2018-07-24 | 国网浙江省电力有限公司电力科学研究院 | A kind of electric service appraisal procedure based on multi-modal the analysis of public opinion |
| CN108491480A (en) * | 2018-03-12 | 2018-09-04 | 义语智能科技（上海）有限公司 | Rumour detection method and equipment |
| CN108614855A (en) * | 2018-03-19 | 2018-10-02 | 众安信息技术服务有限公司 | A kind of rumour recognition methods |
| CN109670164A (en) * | 2018-04-11 | 2019-04-23 | 东莞迪赛软件技术有限公司 | Healthy the analysis of public opinion method based on the more word insertion Bi-LSTM residual error networks of deep layer |
| TWI688903B (en) * | 2017-12-28 | 2020-03-21 | 香港商阿里巴巴集團服務有限公司 | Social content risk identification method, device and equipment |
| CN110909125A (en) * | 2019-10-30 | 2020-03-24 | 中山大学 | Media rumor detection method for shoji society |
| CN111160040A (en) * | 2019-12-26 | 2020-05-15 | 西安交通大学 | Information reliability evaluation system and method based on multi-scale gating equilibrium interaction fusion network |
| CN111553167A (en) * | 2020-04-28 | 2020-08-18 | 腾讯科技（深圳）有限公司 | Text type identification method and device and storage medium |
| CN111694955A (en) * | 2020-05-08 | 2020-09-22 | 中国科学院计算技术研究所 | Early dispute message detection method and system for social platform |
| CN112541081A (en) * | 2020-12-21 | 2021-03-23 | 中国人民解放军国防科技大学 | Migratory rumor detection method based on field self-adaptation |
| CN113377959A (en) * | 2021-07-07 | 2021-09-10 | 江南大学 | Few-sample social media rumor detection method based on meta learning and deep learning |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105045857A (en) * | 2015-07-09 | 2015-11-11 | 中国科学院计算技术研究所 | Social network rumor recognition method and system |
| CN105354305A (en) * | 2015-11-05 | 2016-02-24 | 北京邮电大学 | Online-rumor identification method and apparatus |
| CN106202330A (en) * | 2016-07-01 | 2016-12-07 | 北京小米移动软件有限公司 | The determination methods of junk information and device |
-
2017
- 2017-04-18 CN CN201710252179.3A patent/CN107180077A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105045857A (en) * | 2015-07-09 | 2015-11-11 | 中国科学院计算技术研究所 | Social network rumor recognition method and system |
| CN105354305A (en) * | 2015-11-05 | 2016-02-24 | 北京邮电大学 | Online-rumor identification method and apparatus |
| CN106202330A (en) * | 2016-07-01 | 2016-12-07 | 北京小米移动软件有限公司 | The determination methods of junk information and device |
Non-Patent Citations (6)
| Title |
|---|
| GANG LIANG ET AL: "Automatic Rumors Identification on Sina Weibo", 《2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY》 * |
| JING MA ET AL: "Detecting Rumors from Microblogs with Recurrent Neural Networks", 《PROCEEDINGS OF THE 25TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE》 * |
| 李大友 等: "《计算机等级考试教程 三级B 数据结构》", 29 February 1996, 北京：机械工业出版社 * |
| 皮琪 等: "基于深度学习的虚假评论识别", 《网络新媒体技术》 * |
| 程亮 等: "微博谣言检测方法研究", 《计算机应用与软件》 * |
| 路同强: "基于半监督学习的微博谣言检测研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992469A (en) * | 2017-10-13 | 2018-05-04 | 中国科学院信息工程研究所 | A kind of fishing URL detection methods and system based on word sequence |
| CN108038240A (en) * | 2017-12-26 | 2018-05-15 | 武汉大学 | Based on content, the social networks rumour detection method of user's multiplicity |
| CN108280057A (en) * | 2017-12-26 | 2018-07-13 | 厦门大学 | A kind of microblogging rumour detection method based on BLSTM |
| TWI688903B (en) * | 2017-12-28 | 2020-03-21 | 香港商阿里巴巴集團服務有限公司 | Social content risk identification method, device and equipment |
| US11200381B2 (en) | 2017-12-28 | 2021-12-14 | Advanced New Technologies Co., Ltd. | Social content risk identification |
| CN108319666A (en) * | 2018-01-19 | 2018-07-24 | 国网浙江省电力有限公司电力科学研究院 | A kind of electric service appraisal procedure based on multi-modal the analysis of public opinion |
| CN108491480B (en) * | 2018-03-12 | 2021-05-11 | 义语智能科技（上海）有限公司 | Rumor detection method and apparatus |
| CN108491480A (en) * | 2018-03-12 | 2018-09-04 | 义语智能科技（上海）有限公司 | Rumour detection method and equipment |
| CN108614855A (en) * | 2018-03-19 | 2018-10-02 | 众安信息技术服务有限公司 | A kind of rumour recognition methods |
| CN109670164A (en) * | 2018-04-11 | 2019-04-23 | 东莞迪赛软件技术有限公司 | Healthy the analysis of public opinion method based on the more word insertion Bi-LSTM residual error networks of deep layer |
| CN110909125A (en) * | 2019-10-30 | 2020-03-24 | 中山大学 | Media rumor detection method for shoji society |
| CN110909125B (en) * | 2019-10-30 | 2022-11-15 | 中山大学 | Detection method of media rumor of news-level society |
| CN111160040A (en) * | 2019-12-26 | 2020-05-15 | 西安交通大学 | Information reliability evaluation system and method based on multi-scale gating equilibrium interaction fusion network |
| CN111553167A (en) * | 2020-04-28 | 2020-08-18 | 腾讯科技（深圳）有限公司 | Text type identification method and device and storage medium |
| CN111694955A (en) * | 2020-05-08 | 2020-09-22 | 中国科学院计算技术研究所 | Early dispute message detection method and system for social platform |
| CN111694955B (en) * | 2020-05-08 | 2023-09-12 | 中国科学院计算技术研究所 | Early dispute message detection method and system for social platform |
| CN112541081A (en) * | 2020-12-21 | 2021-03-23 | 中国人民解放军国防科技大学 | Migratory rumor detection method based on field self-adaptation |
| CN112541081B (en) * | 2020-12-21 | 2022-09-16 | 中国人民解放军国防科技大学 | Migratory rumor detection method based on field self-adaptation |
| CN113377959A (en) * | 2021-07-07 | 2021-09-10 | 江南大学 | Few-sample social media rumor detection method based on meta learning and deep learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107180077A (en) | A kind of social networks rumour detection method based on deep learning | |
| CN108170736B (en) | Document rapid scanning qualitative method based on cyclic attention mechanism | |
| US11762990B2 (en) | Unstructured text classification | |
| CN104834747B (en) | Short text classification method based on convolutional neural networks | |
| CN110750640B (en) | Text data classification method and device based on neural network model and storage medium | |
| CN112019651B (en) | DGA domain name detection method using depth residual error network and character-level sliding window | |
| CN109558487A (en) | Document Classification Method based on the more attention networks of hierarchy | |
| CN109450845B (en) | Detection method for generating malicious domain name based on deep neural network algorithm | |
| WO2020247616A1 (en) | Linguistically rich cross-lingual text event embeddings | |
| CN111552855A (en) | Network threat information automatic extraction method based on deep learning | |
| CN110990564A (en) | Negative news identification method based on emotion calculation and multi-head attention mechanism | |
| KR20190063978A (en) | Automatic classification method of unstructured data | |
| Xu et al. | Detecting sensitive information of unstructured text using convolutional neural network | |
| Junnarkar et al. | E-mail spam classification via machine learning and natural language processing | |
| CN112073550B (en) | DGA domain name detection method fusing character-level sliding window and depth residual error network | |
| CN112073551B (en) | DGA domain name detection system based on character-level sliding window and depth residual error network | |
| CN109325125B (en) | Social network rumor detection method based on CNN optimization | |
| CN110134792A (en) | Text recognition method, device, electronic equipment and storage medium | |
| ALBayari et al. | Cyberbullying classification methods for Arabic: A systematic review | |
| CN115309897A (en) | Chinese multi-modal confrontation sample defense method based on confrontation training and contrast learning | |
| Remmide et al. | Detection of phishing URLs using temporal convolutional network | |
| CN116318845B (en) | DGA domain name detection method under unbalanced proportion condition of positive and negative samples | |
| Shirazi et al. | Towards performance of NLP transformers on URL-based phishing detection for mobile devices | |
| CN112966507A (en) | Method, device, equipment and storage medium for constructing recognition model and identifying attack | |
| CN111782811A (en) | E-government affair sensitive text detection method based on convolutional neural network and support vector machine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170919 |
|
| RJ01 | Rejection of invention patent application after publication |