Database of digital media publications on maternal (family) capital in Russia in 2006–2019

The database contains data from publications of digital Russian-language media registered in the Russian Federation on the topic of maternity capital published in the period from May 10, 2006 to June 30, 2019. The database includes general data on publications on maternity capital in .csv formats (UTF-8 encoding). Full texts of publications are presented in .xml format. A specialized request was generated for the aggregator of publications of Russian-language digital mass media public.ru. In total, the database consists of 457,888 publications of 7,665 publishing houses from 1,251 settlements located in 85 regions of Russia. The database includes information about the date and type of publication, publisher, place of publication (municipality), texts about maternity capital, and numbers of unique positive, negative, and neutral words and phrases according to the RuSentiLex2017 dictionary, as well as full texts of publications

Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews

Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other

Identifying Reproductive Behavior Arguments in Social Media Content Users’ Opinions through Natural Language Processing Techniques

Big data provides researchers with valuable sources of information for studying demographic behavior in the population. One such source is the texts posted by social network users on various demographic issues. This study utilizes methods for automatically extracting user opinions from the “VKontakte” social network. The extracted texts are then classified using the Conversational RuBERT neural network model to investigate opinions related to reproductive behavior in the population. The classification process addresses two consecutive problems. Firstly, it aims to identify whether a user’s comment contains argumentation. Secondly, if an argument is present, it seeks to determine its type within the context of the “personal-public” dichotomy. To search for arguments and classify their types, six experiments were conducted, varying the dataset and the number of classes. The method employed for automatic extraction and classification of user opinions on the “VKontakte” social network has demonstrated the ability to accurately classify users’ comments, identifying the presence of argumentation and categorizing the arguments within the “personal-public” dichotomy. This enables the identification of personal and social attitudes, values, stories, and opinions, thus facilitating the study of reproductive behavior

The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks

Transverse momentum and pseudorapidity distributions of charged hadrons in pp collisions at (s)\sqrt(s) = 0.9 and 2.36 TeV

Measurements of inclusive charged-hadron transverse-momentum and pseudorapidity distributions are presented for proton-proton collisions at sqrt(s) = 0.9 and 2.36 TeV. The data were collected with the CMS detector during the LHC commissioning in December 2009. For non-single-diffractive interactions, the average charged-hadron transverse momentum is measured to be 0.46 +/- 0.01 (stat.) +/- 0.01 (syst.) GeV/c at 0.9 TeV and 0.50 +/- 0.01 (stat.) +/- 0.01 (syst.) GeV/c at 2.36 TeV, for pseudorapidities between -2.4 and +2.4. At these energies, the measured pseudorapidity densities in the central region, dN(charged)/d(eta) for |eta| < 0.5, are 3.48 +/- 0.02 (stat.) +/- 0.13 (syst.) and 4.47 +/- 0.04 (stat.) +/- 0.16 (syst.), respectively. The results at 0.9 TeV are in agreement with previous measurements and confirm the expectation of near equal hadron production in p-pbar and pp collisions. The results at 2.36 TeV represent the highest-energy measurements at a particle collider to date

Measurement of the charge ratio of atmospheric muons with the CMS detector

We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/ c to 1 TeV/ c . The surface flux ratio is measured to be 1.2766±0.0032(stat.)±0.0032(syst.) , independent of the muon momentum, below 100 GeV/ c . This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments.We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/c to 1 TeV/c. The surface flux ratio is measured to be 1.2766 \pm 0.0032(stat.) \pm 0.0032 (syst.), independent of the muon momentum, below 100 GeV/c. This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments

Transverse-momentum and pseudorapidity distributions of charged hadrons in pppp collisions at s\sqrt{s} = 7 TeV

Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at $\sqrt{s} = 7$~TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit-pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity \dnchdeta|_{|\eta| < 0.5} = 5.78\pm 0.01\stat\pm 0.23\syst for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from $\sqrt{s} = 0.9$ to 7~TeV is 66.1\%\pm 1.0\%\stat\pm 4.2\%\syst. The mean transverse momentum is measured to be 0.545\pm 0.005\stat\pm 0.015\syst\GeVc. The results are compared with similar measurements at lower energies.Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at sqrt(s) = 7 TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit-pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity, dN(charged)/d(eta), for |eta| < 0.5, of 5.78 +/- 0.01 (stat) +/- 0.23 (syst) for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from sqrt(s) = 0.9 to 7 TeV is 66.1% +/- 1.0% (stat) +/- 4.2% (syst). The mean transverse momentum is measured to be 0.545 +/- 0.005 (stat) +/- 0.015 (syst) GeV/c. The results are compared with similar measurements at lower energies

Search for Pair Production of Second-Generation Scalar Leptoquarks in pp Collisions at sqrt(s) = 7 TeV

A search for pair production of second-generation scalar leptoquarks in the final state with two muons and two jets is performed using proton-proton collision data at sqrt(s) = 7 TeV collected by the CMS detector at the LHC. The data sample used corresponds to an integrated luminosity of 34 inverse picobarns. The number of observed events is in good agreement with the predictions from the standard model processes. An upper limit is set on the second-generation leptoquark cross section times beta^2 as a function of the leptoquark mass, and leptoquarks with masses below 394 GeV are excluded at a 95% confidence level for beta = 1, where beta is the leptoquark branching fraction into a muon and a quark. These limits are the most stringent to date