Person’s Recognition: A Criminal Can Be Found By Linguistic Analysis Of Their Social Network Profile

Linguistic And Semantic Files

Each person possesses a number of distinguishing features, from fingerprints to the way they walk. After some observations, my machine learning colleagues and I have concluded that textual content, its stylistics, lexemes, and many other parameters are no exception. Today, the abundance of text materials, messages, posts, and comments, allows us to highlight the unique features of a person using neural networks algorithms.

Recognizing Users With 87% Accuracy

In order to illustrate the process, we have carried out an experiment. 500 users engaged in political disputes were selected, meaning all users were essentially talking about the same thing. The samples simulated a situation when a user logs out of the site, but after a while comes back and starts writing under a different nickname. That is, the user’s early messages were placed in the training set, and the later ones in the test set.

Troll Factories, Fake News, And Total Loss Of Anonymity

The technologies discussed can be applied to a variety of NLP tasks, at different scales. Linguistic and semantic files can be compiled collectively for the participants of the troll factory. The discussed models are also suitable for fake news recognition since facts have semantic characteristics by definition. The meanings of the phrases ‘vaccines kill’ and ‘vaccines save’ are much easier to distinguish than the meanings of comments made by 500 people about politics.

The Technical Side. Speech2Vec And Cascade Models

While dealing with the tasks of machine processing for natural language, a word or sentence is transformed into a set of figures — a vector, where each number encodes semantic and linguistic components.



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