E. Artemova, Aleksandr Maksimenko, Dmitriy Ohrimenko
The paper attempts to classify the corruption-related media content of Russian-language and English-language Internet media using machine learning methods. The methodological approach proposed in the article is very relevant and promising, since, according to our earlier data, corruption monitoring mechanisms used in foreign publications based on the use of advanced information technologies have rather limited potential effectiveness and are not always adequately interpreted. The study shows the principles and grounds for identifying identification parameters, and also describes in detail the layout scheme of the collected news array. In the course of automatic text processing, which took place in 2 stages (vectorization of the text and the use of a learning model), it was possible to solve the main 4 tasks: highlighting a significant quote from a news article to identify a text on corruption topics, predicting the type of news message, predicting a relevant article of the Criminal Code of the Russian Federation, which is used to determine responsibility for the described corruption offense, as well as predicting the type of relationship in corruption offenses. The results obtained showed that modern methods of automatic text processing successfully cope with the tasks of identification and classification of corruption-related content in both Russian and English.
{"title":"Application of machine learning methods in the classification of corruption related content in Russian-speaking and English-speaking Internet media","authors":"E. Artemova, Aleksandr Maksimenko, Dmitriy Ohrimenko","doi":"10.19181/4m.2021.52.5","DOIUrl":"https://doi.org/10.19181/4m.2021.52.5","url":null,"abstract":"The paper attempts to classify the corruption-related media content of Russian-language and English-language Internet media using machine learning methods. The methodological approach proposed in the article is very relevant and promising, since, according to our earlier data, corruption monitoring mechanisms used in foreign publications based on the use of advanced information technologies have rather limited potential effectiveness and are not always adequately interpreted. The study shows the principles and grounds for identifying identification parameters, and also describes in detail the layout scheme of the collected news array. In the course of automatic text processing, which took place in 2 stages (vectorization of the text and the use of a learning model), it was possible to solve the main 4 tasks: highlighting a significant quote from a news article to identify a text on corruption topics, predicting the type of news message, predicting a relevant article of the Criminal Code of the Russian Federation, which is used to determine responsibility for the described corruption offense, as well as predicting the type of relationship in corruption offenses. The results obtained showed that modern methods of automatic text processing successfully cope with the tasks of identification and classification of corruption-related content in both Russian and English.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To our knowledge, this is the first paper which develops and validates the scale of political knowledge in Russia. The scale was validated in a telephone survey using random sampling (N = 3002). The scale includes 10 questions on the knowledge of political institutes, main political actors, substantial events, international politics, and history of the state. The scale showed sufficiently high internal consistency. Cronbach’s alpha was equal to 0.74. In addition, the criterion and convergent validity of the instrument was tested based on socio-demographic variables, interest in politics, news discussion, media consumption, and political behavior. We found weak and medium Pearson correlation coefficients between political knowledge and socio-demographic variables. Convergent validity shows weak and medium correlation coefficients between political knowledge and interest in politics, frequency of discussion of political news, and media consumption. The strongest correlation was found with interest in politics. In addition, we found a positive correlation between political knowledge and such forms of political behavior as turnout at State Duma elections, signing petitions, and participating in campaigning. Finally, the IRT (Item-Response Theory) results showed quite a high quality of the scale. Overall, we conclude that this scale can be used to measure political knowledge in Russia.
{"title":"Measuring political knowledge: development and testing the scale in Russia","authors":"A. Klimova, Georgy Artamonov, K. Chmel","doi":"10.19181/4m.2021.52.3","DOIUrl":"https://doi.org/10.19181/4m.2021.52.3","url":null,"abstract":"To our knowledge, this is the first paper which develops and validates the scale of political knowledge in Russia. The scale was validated in a telephone survey using random sampling (N = 3002). The scale includes 10 questions on the knowledge of political institutes, main political actors, substantial events, international politics, and history of the state. The scale showed sufficiently high internal consistency. Cronbach’s alpha was equal to 0.74. In addition, the criterion and convergent validity of the instrument was tested based on socio-demographic variables, interest in politics, news discussion, media consumption, and political behavior. We found weak and medium Pearson correlation coefficients between political knowledge and socio-demographic variables. Convergent validity shows weak and medium correlation coefficients between political knowledge and interest in politics, frequency of discussion of political news, and media consumption. The strongest correlation was found with interest in politics. In addition, we found a positive correlation between political knowledge and such forms of political behavior as turnout at State Duma elections, signing petitions, and participating in campaigning. Finally, the IRT (Item-Response Theory) results showed quite a high quality of the scale. Overall, we conclude that this scale can be used to measure political knowledge in Russia.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114621326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In article we propose a method for the analysis of long time series data of the Monitoring of Labor Protests. This database has been accumulating data on labor protests in Russia since 2008. During 156 months data were collected on 3,951 protests by Russian workers. The article describes the need to control labor protests, the features of the current legal regulation of labor conflicts. The peculiarity of the Russian situation is that the state bodies register only “legal” strikes, ignoring the numerous protest actions of workers undertaken in other forms. Therefore, it is necessary to study their dynamics in order to identify trends that are not visible using conventional analysis. The main difficulty in identifying trends is the high variability of the source data. The article proposes a method for smoothing data and the rate of change of the smoothed function at each point, which makes it possible to find criteria for determining periods of growth and decline in the protest indicator. This makes it possible to calculate periods, their duration, intensity, average rate of growth or decline. In addition to analyzing the overall dynamics, the proposed method allows to study subsamples, for example, of different sectors, and compare them obtaining comparable estimates. The article compares three sectors – industry, transport and healthcare – that accounted for 75% of all protest actions of workers in 2020.
{"title":"Studying the dynamics of labor protests: the experience of using linear approximation method","authors":"P. Bizyukov, T. Burnysheva","doi":"10.19181/4m.2021.52.4","DOIUrl":"https://doi.org/10.19181/4m.2021.52.4","url":null,"abstract":"In article we propose a method for the analysis of long time series data of the Monitoring of Labor Protests. This database has been accumulating data on labor protests in Russia since 2008. During 156 months data were collected on 3,951 protests by Russian workers. The article describes the need to control labor protests, the features of the current legal regulation of labor conflicts. The peculiarity of the Russian situation is that the state bodies register only “legal” strikes, ignoring the numerous protest actions of workers undertaken in other forms. Therefore, it is necessary to study their dynamics in order to identify trends that are not visible using conventional analysis. The main difficulty in identifying trends is the high variability of the source data. The article proposes a method for smoothing data and the rate of change of the smoothed function at each point, which makes it possible to find criteria for determining periods of growth and decline in the protest indicator. This makes it possible to calculate periods, their duration, intensity, average rate of growth or decline. In addition to analyzing the overall dynamics, the proposed method allows to study subsamples, for example, of different sectors, and compare them obtaining comparable estimates. The article compares three sectors – industry, transport and healthcare – that accounted for 75% of all protest actions of workers in 2020.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124736361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article discusses the features of blockmodeling as a class of methods for clustering network data in the analysis of social structures. Blockmodeling is considered as an approach to the analysis of social structure, which combines network components into groups (clusters) based on their equivalent structural positions. The basic concepts of blockmodeling are described – matrix, matrix image, cluster, clustering, position, block, blockmodel; an illustrating example is given. The concept of equivalence is presented, and two types of equivalence, structural and regular, are described. The main approaches of blockmodeling – indirect and direct – and related methods and algorithms are presented. For each approach, examples of the practical application in social sciences are provided. Other methods of blockmodeling (stochastic blockmodeling) and similar methods of subgroups detection in networks are mentioned. It is shown that the methodology of blockmodeling has heuristic potential for analyzing social structures and is promising for identifying cohesive groups and determining the role and structural positions of individuals within them. In conclusion, the open questions and limitations of this research methodology are discussed.
{"title":"Blockmodeling for analysis of social structures: theoretical and methodological foundations","authors":"T. Shcheglova, D. Maltseva, Aryuna Kim","doi":"10.19181/4m.2021.52.1","DOIUrl":"https://doi.org/10.19181/4m.2021.52.1","url":null,"abstract":"The article discusses the features of blockmodeling as a class of methods for clustering network data in the analysis of social structures. Blockmodeling is considered as an approach to the analysis of social structure, which combines network components into groups (clusters) based on their equivalent structural positions. The basic concepts of blockmodeling are described – matrix, matrix image, cluster, clustering, position, block, blockmodel; an illustrating example is given. The concept of equivalence is presented, and two types of equivalence, structural and regular, are described. The main approaches of blockmodeling – indirect and direct – and related methods and algorithms are presented. For each approach, examples of the practical application in social sciences are provided. Other methods of blockmodeling (stochastic blockmodeling) and similar methods of subgroups detection in networks are mentioned. It is shown that the methodology of blockmodeling has heuristic potential for analyzing social structures and is promising for identifying cohesive groups and determining the role and structural positions of individuals within them. In conclusion, the open questions and limitations of this research methodology are discussed.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article is devoted to the description of the original method of generational differences analysis. This method is called the maximum differentiation scheme. With the help of the scheme we investigated unique trends in the millennial generation (born 1982–2000), which were observed neither in the entire sample nor in the other two generations for three types of data collection methods (cross-sectional analysis, analysis of temporal changes and long-term analysis). To satisfy the mechanics of the scheme, in addition to millennials, the Soviet generation (born 1947–1967) and the reform generation (born 1968–1981) were used for systematic comparisons. The research was conducted using data from the Russian Target Group Index. Thanks to the scheme, the author showed that only the statement “I prefer vegetarian food” can be considered a truly characteristic attitude of millennials. However, the scheme has two important limitations: 1) it is not sensitive to trends that are not unique to a particular generation; 2) the scheme cannot be used as the only type of analysis and needs additional methodological procedures. Taking into account these limitations, the maximum differentiation scheme can be useful both as a unique type of preliminary analysis of generational differences, and included in a broader approach to working with age and period effects.
本文对代际差异分析的原始方法进行了描述。这种方法称为最大微分格式。在该方案的帮助下，我们研究了千禧一代(1982-2000年出生)的独特趋势，这些趋势既没有在整个样本中观察到，也没有在其他两代人中观察到，采用了三种类型的数据收集方法(横断面分析、时间变化分析和长期分析)。为了满足该方案的机制，除了千禧一代，苏联一代(生于1947-1967年)和改革一代(生于1968-1981年)被用于系统比较。这项研究使用了俄罗斯目标群体指数(Russian Target Group Index)的数据。通过这个方案，作者证明了只有“我更喜欢素食”这句话才能被认为是千禧一代真正具有特色的态度。然而，该方案有两个重要的局限性:1)它对非特定世代独有的趋势不敏感;2)该方案不能作为唯一的分析类型，需要额外的方法学程序。考虑到这些限制，最大差异方案既可以作为代际差异的一种独特的初步分析，也可以纳入研究年龄和时期影响的更广泛的方法。
{"title":"Methodological aspects of generational differentiation: on the example of determining nutritional attitudes of Russian millennials","authors":"E. Tkachenko","doi":"10.19181/4m.2021.52.2","DOIUrl":"https://doi.org/10.19181/4m.2021.52.2","url":null,"abstract":"The article is devoted to the description of the original method of generational differences analysis. This method is called the maximum differentiation scheme. With the help of the scheme we investigated unique trends in the millennial generation (born 1982–2000), which were observed neither in the entire sample nor in the other two generations for three types of data collection methods (cross-sectional analysis, analysis of temporal changes and long-term analysis). To satisfy the mechanics of the scheme, in addition to millennials, the Soviet generation (born 1947–1967) and the reform generation (born 1968–1981) were used for systematic comparisons. The research was conducted using data from the Russian Target Group Index. Thanks to the scheme, the author showed that only the statement “I prefer vegetarian food” can be considered a truly characteristic attitude of millennials. However, the scheme has two important limitations: 1) it is not sensitive to trends that are not unique to a particular generation; 2) the scheme cannot be used as the only type of analysis and needs additional methodological procedures. Taking into account these limitations, the maximum differentiation scheme can be useful both as a unique type of preliminary analysis of generational differences, and included in a broader approach to working with age and period effects.","PeriodicalId":271863,"journal":{"name":"Sociology: methodology, methods, mathematical modeling (Sociology: 4M)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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