Data and Text Interpretation in Social Media: Urban Planning Conflicts

Maria Pilgun () and Nailia Gabdrakhmanova ()
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Maria Pilgun: Russian State Social University
Nailia Gabdrakhmanova: Peoples’ Friendship University of Russia

A chapter in Data Analysis and Optimization, 2023, pp 271-289 from Springer

Abstract: Abstract The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts on the example of data and text interpretation in social media. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the Big circle metro line (Southern section) in Moscow (RF). Data collection: 1 October 2020–10 June 2021. Number of tokens: 62 657 289. To analyze the content of social media, a multi-modal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on the user generated content and on digital footprints of users. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts.

Date: 2023
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DOI: 10.1007/978-3-031-31654-8_18

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