Мысль для меня не новая. Время от времени я присупал к тому, чтобы самостоятельно сделать какой-нибудь бложек. У меня, был, например, блог на wordpress по адресу ihun.tk (на этом домене можно получить бесплатный адрес), но всё потом заглухало. По...
Эссе для конкурса на участие в Весенные школе ОРФ в Сочи.
Эссе для конкурса на получение стипендии ОРФ 2017г.
A semester course on python for data analysis for students of programme “Big Data Analysis for Business, Economy, and Society”, HSE.
The ability of social media to rapidly disseminate judgements on ethnicity and to influence offline ethnic relations creates demand for the methods of automatic monitoring of ethnicity-related online content. In this study we seek to measure the overall volume of ethnicity-related discussion in the Russian-language social media and to develop an approach that would automatically detect various aspects of attitudes to those ethnic groups. We develop a comprehensive list of ethnonyms and related bigrams that embrace 97 Post-Soviet ethnic groups and obtain all messages containing one of those words from a two-year period from all Russian-language social media (N=2,660,222 texts). We hand-code 7,181 messages where rare ethnicities are over-represented and train a number of classifiers to recognize different aspects of authors’ attitudes and other text features. After calculating a number of standard quality metrics, we find that we reach good quality in detecting intergroup conflict, positive intergroup contact, and overall negative and positive sentiment. Relevance to the topic of ethnicity and general attitude to an ethnic group are least well predicted, while some aspects such as calls for violence against an ethnic group are not sufficiently present in the data to be predicted.
An important role of digital inequality for hindering the development of civil society is being increasingly acknowledged. Simultaneously, differences in availability and the practices of use of social network sites (SNS) may be considered as major manifestations of such digital divide. While SNS are in principle highly convenient spaces for public discussion, lack of access or domination by socially insignificant small talk may indicate underdevelopment of the public sphere. At the same time, agenda differences between regions may signal about local problems. In this study we seek to find out whether regional digital divide exists in such a large country as Russia. We start from a theory of uneven modernization of Russia and use the data from its most popular SNS “VK.com” as a proxy for measuring digital inequality. By analyzing user activity data from a sample of 77,000 users and texts from a carefully selected subsample of 36,000 users we conclude that regional level explains an extremely small share of variance in the overall variation of behavioral user data. A notable exception is attention to the topics of Islam and Ukraine. However, our data reveal that historically geographical penetration of “VK.com” proceeded from the regions considered the most modernized to those considered the most traditional. This finding supports the theory of uneven modernization, but it also shows that digital inequality is subject to change with time.
Internet Studies is an interdisciplinary and multidisciplinary field of fundamental and applied research that integrate different research disciplines with a common object, that is the Internet. This review article gives a definition and a brief description of the structure of Internet Studies as part of the social sciences and introduces research agenda of this field, including most cutting edge research issues. The agenda of Internet Studies related to classical sociological issues are analyzed in more detail: inequality, online communities and social capital as well as topics related to the study of transformations in different spheres of society - politics, public health and medicine, education. Two main theoretical approaches are briefly described, within which the influence of the Internet on society is interpreted: the network society theory and critical theory of the Internet and society. We conclude that the present directions of Internet research have many intersections with each other, and the perspective of a more complete study of the mechanisms, that mediate social changes related to the Internet and connect online and offline sociality into a single space, opens at these intersections.
Media audiences that represent a significant part of a county’s public may hold opinions on media-generated definitions of social problems different from those of media professionals. The proliferation of user-generated content makes such opinions available, but simultaneously demands new automatic methods of analysis that media scholars still have to master. In this paper, we show how topics regarded as problematic by media consumers may be revealed and analyzed by social scientists with a combination of data mining methods. Our dataset consists of 33,877 news items and 258,121 comments from a sample of regional newspapers. With a number of new, but simple indices we find that issue salience in media texts and its popularity with audience diverge. We conclude that our approach can help communication scholars effectively detect both popular and negatively perceived topics as good proxies of social problems.
The paper presents the results of a study based on the Big Data paradigm analysis. The study aims at defining the features of the ethnic discourse in the Russian-speaking social media and the place of the North Caucasus ethnicities in this discourse. The informational basis for the study is 2,659,849 social media publications containing ethnonyms. The author concludes that the ethnic discourse is full of problematic topics mainly discussed by male participants. The study shows that the ethnonyms related to the North Caucasus peoples are often used in the context of crime and terrorism.
Эссе для конкурса на получение стипендии ОРФ 2016 года.
Эссе для конкурса на участие в Зимней школе ОРФ в Сочи.
Об одном недостатке социологического образования в Пиетрской Вышке.
The availability of large urban social media data creates new opportunities for studying cities. In our paper we propose a new direction for this research: a joint analysis of geolocations of shared images and their content as determined by computer vision. To test our ideas, we use a dataset of 47,410 Instagram images shared in the city of St.Petersburg over one year. We show how a combination of semantic clustering, image recognition and geospatial analysis can detect important patterns related to both how people use a city and how they represent in social media.
Эссе для конкурса на получение стипендии ОРФ 2015 года.
Современная частная собственность на средства производства выступает главным образом в акционерной форме, а в число крупнейших держателей акций на Западе входят пенсионные фонды, распоряжающиеся частью сбережений миллионов рабочих, которые таким...
В порядке бреда
Почему социологу сложно определиться с политическими предпочтениями.
Последние несколько дней не отрываясь наблюдал за революцией на Украине. Все события транслировались в реальном времени с нескольких камер. И тут я подумал, что для социального учёного наблюдение за революцией – то же самое, что наблюдение за...