• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Radomir Britkov
How to Identify Bots in Social Media: Motifs in Semantic Spaces
Data Science
(Master’s programme)
7
2019
In today's world, more and more people are using the Internet to make a decision, so now the most important problem is determining the “honesty” of comments and feedback left on the Internet. For example, you choose a hotel and it has a lot of positive reviews, you would like to know which of these reviews people left of their own will, and which of the reviews were paid to the owners of the hotel. The difficulty of the problem lies in the fact that there is usually no sample, where some users are marked as "bots" and others as real people. Therefore it is necessary to resort to machine learning methods without a teacher.

In this paper, an attempt is made to classify users into ordinary people and “bots” (people who write reviews and leave comments for money) using clustering based on the Wishart algorithm. For the presentation of comments in the form of numerical vectors, two models are used: Word2Vec and ELMO. Further, the comments are divided into clusters, the algorithm parameters are determined on the basis of various metrics of clustering quality such as the Dunn index, silhouette index, simplified silhouette index and SD index. All these metrics are implemented as part of this thesis. For fast clustering, a custom implementation of the Wishart algorithm is used, which uses special data structures to quickly find the nearest neighbors. Then, based on the same clustering quality indices and threshold rules, which cluster is related to bots and which to ordinary people is determined. The scientific novelty of the work lies in the fact that before that almost no one tried to solve this problem using machine-learning algorithms without a teacher. Therefore, the methodology developed during this work can be applied in the future to analyze user behavior in such popular Internet resources as “youtube”, “vk.com” and “facebook”.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses