Private and Public Online Groups in Apartment Buildings of St. Petersburg
This paper is concerned with online communication of apartment buildings' residents on general purpose social networking site (SNS) VKontakte (VK), focusing on how groups' participants use instruments of SNS to separate place-based discussions and participation in wider community initiatives. With the help of topic modeling algorithm LDA, we analyzed posts collected from online groups related to apartment complexes in Saint-Petersburg to reveal differences of communication in open groups and restricted access groups. We also looked at overlaps between local groups of apartment buildings and city-wide movements. Our study shows that inside SNS there is a functional differentiation between restricted access groups and open groups, which have different audiences and communicative strategies. Restricted access (private) groups play an important role in the formation of neighbors' communities of trust and, supposedly, can be useful substitutes of face-to-face interaction for people moving into new buildings. Open (public) groups function as public forums for fostering neighbors' cooperation and attracting attention of broader public to local issues and conflicts.
Through the example of the U Street block in Washington, D.C., the noted American urbanist shows that urban “contact zones” in which people disunited by racial, ethic, confessional and class conflicts are living side by side, serve as generators of new adaptive strategies. The inexhaustible source of viability and flexibility of these communities lies in the need for survival in the conditions of “deliberate social complexity”. It is precisely this experience that enables such communities effectively to adapt to the aftermaths of natural calamities and social conflicts.
The United Nations estimates that by 2030, more than two-thirds of the total world population will live in urban areas. Most of this increase will take place not in Europe or in the United States but in the megacities and newly emerging urban regions of what used to be called the developing world. Urban studies is an expansive and growing field, covering many disciplines and professional fields, each with its own schedule of conferences, journals, and publication series. These two volumes address the specific theories, key studies, and important figures that have influenced not just the individual discipline but also the field of urban studies more generally. The Encyclopedia of Urban Studies is intended to present an overview of current work in the field and to serve as a guide for further reading in the field.
An important text mining problem is to find, in a large collection of texts, documents related to specic topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to nd the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predened sets of keywords (that dene the topics researchers are interested in) are restricted to specic intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.
The article describes routs of visitors of museum-reserve Tsaritsyno (Moscow) after its reconstruction -- in the most popular and crowded "historical" part of the park and in the distant areas. In addition, we consider which type of visitors prefer certain routes, as well as how visitors experience space in different parts of the park (or different modes of perception). The article describes such modes as "consumption of public space", "romantic tourist gaze" and "existential" mode.
An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments.