Abstract:
© International Research Publication House This article solves the problem of intelligent models constructing and their accuracy evaluating for identifying bots in social networks. The relevance of solving this problem is noted. The construction and accuracy assessment of the neural network model, decision tree and linear regression are performed. The initial data source was Twitter social network. To collect the initial data, we used our own database, consisted of 3428 users, about half of which contained characteristic features of bots. The initial data were randomly divided into the training and testing sets, each of them included approximately 50% of the records. 15 attributes were used as the model’s input parameters, in particular, the number of symbols in the username, the user’s number of tweets, the number of readers, etc. The models construction and study was carried out on the Deductor analytical platform base. Each model was tested on data set consisted of 1719 records. For all models, the corresponding classification matrices were constructed and the first, second kind errors and the general model’s error were calculated. In terms of minimizing these errors, the neural network model showed the best results, and the linear regression model showed the worst. This allowed us to conclude, that in order to minimize classification errors, it is advisable to use a neural network model. This indicates its effectiveness and the possibility of practical use in intelligent decision-making support systems for bots identifying in social networks.
