An increasing amount of social networks users-generated data is the most remarkable research challenge nowadays. Despite the progress in the field of semistructured data processing algorithms creation, even initial data collection could not be treated as issues that have been optimally solved. The paper covers a high-level overview of the automated social media content search system. The proposed structure enables to implement instruments for multisource content extraction tasks as well as supporting of identification processes of new patterns, which describe a certain type of content. Issues of Search engine organization, logically unified extracted data repository and possible content classification techniques with the appropriate knowledge base’s application are considered. Under the work, existing approaches and automated web-data extraction methods have been analyzed; social media API’s functions and limits, as well as ways of semistructured data storage system organization, have been studied. The planned result’s application area is automation and informational support of sociological research based on the social media content analysis techniques namely a content propagation simulation in interconnected groups; social and personal anomy study; clarification of the weak linkage’s strength concept.

This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyse this data to solve problems such as forecasting and modelling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator.

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Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, β = 0.81. For the second sample, β = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm.

The article discusses process of decision support in oilfield development. The algorithm of geological and engineering operations planning, based on the principal stages of the decision-making process to perform GEO, is proposed.