"St. Petersburg National Research University of Information Technologies, Mechanics and Optics"
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Identification of the authors of short messages portals on the Internet using the methods of mathematical linguistics. Postgraduate: Sukhoparov M.E. Supervisor: doctor of engineering science, Lebedev I.S. "St. Petersburg National Research University of Information Technologies, Mechanics and Optics" Department of "Secure Information Technology" Specialty 05.13.19 "Methods and systems of information protection, information security"
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"St. Petersburg National Research University of Information Technologies, Mechanics and Optics" Department of "Secure Information Technology". Identification of the authors of short messages portals on the Internet using the methods of mathematical linguistics. Specialty 05.13.19 - PowerPoint PPT Presentation
Transcript of "St. Petersburg National Research University of Information Technologies, Mechanics and Optics"
Identification of the authors of short messages portals on the Internet using
the methods of mathematical linguistics.
Postgraduate: Sukhoparov M.E.Supervisor: doctor of engineering science,
Lebedev I.S.
"St. Petersburg National Research University of Information Technologies, Mechanics and Optics"
Department of "Secure Information Technology"
Specialty 05.13.19"Methods and systems of information protection, information security"
Purpose and objectivesThe goal - a study of methods to identificate users.
Objectives:study and development of scientific-methodical system
of identification of authorship of textual informationcreation of the program layout, based on the proposed
approachassessment of the performance and efficiency of the
developed prototyping implementation
Prospective directions of research
The use of naive Bayes classifierAnalysis based on the N - gramsAnalysis based on latent Dirichlet allocation
Architecture of the proposed software
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Vocabulary
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Naive Bayes classifier
Bayes theorem:
- probability that document belongs to the class ; - probability of finding document of any documents class ; - unconditional probability of finding a document of class in the case of documents; - unconditional probability of a document in the case of documents.
Naive Bayes classifier
Maximum a posteriori estimation:
Naive Bayes classifierThe problem of arithmetic overflow:
Estimation of parameters of the Bayes model:• , where - number of documents belong to class , - total number of
documents in the training set;• , where - number of times as the i-th word appears in the documents
of class , - dictionary of a set of documents (a list of all unique words).
Naive Bayes classifierThe problem of unknown words:
The final view of the formula:
Naive Bayes classifierStatistics used in the classification stage:
relative frequencies of the classes in the case of documents;total number of words in each document class;the relative frequencies of words within each class;dictionary size (amount of unique words in training set).
- number of documents belong to class - total number of documents in the training set; - dictionary of a set of documents (a list of all unique words);- the total number of words in documents of class c in the training set; - number of times as the i-th word appears in the documents of class ; - set of words of classified document (including repeats).
Results
75 100 125 150 175 2000.00
0.20
0.40
0.60
0.80
1.00
0.54
0.64
0.720.76
0.790.81
Amount of training set
𝑃 (𝑐|𝑑 )
ConclusionsThe implementation of the proposed solutions will identify the authors of short message forums and blogs on the Internet at various PR - actions to combat and control the formation and manipulation of public opinion and other manifestations of astroterfing.
