## 4 Discussion  
The paper presents a solution to the problem of discovering what the residents think of a subway line construction project from their social media. The problem was split into subproblems: (1) filter the data; (2) build a mathematical model; and (3) make a managerial decision. These subproblems were solved by artificial neural networks. Solving the subproblems one by one is essentially an algorithm in which an AI automates solving a managerial problem. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(t\) & \(x\) & \(X_{1}\) & \(E_{1}\) & \(X_{2}\) & \(E_{2}\) \\ \hline
1 & 0.2500 & 0.2500 & 0.0000 & 0.3454 & 0.0091 \\
2 & 0.2000 & 0.2511 & 0.0026 & 0.2832 & 0.0069 \\
3 & 0.1058 & 0.2556 & 0.0225 & 0.0964 & 0.0001 \\
4 & 0.1039 & 0.2629 & 0.0253 & 0.1395 & 0.0013 \\
5 & 0.1882 & 0.2724 & 0.0071 & 0.2365 & 0.0023 \\
6 & 0.2069 & 0.2835 & 0.0059 & 0.2558 & 0.0024 \\
7 & 0.2000 & 0.2958 & 0.0092 & 0.2515 & 0.0026 \\
8 & 0.2833 & 0.3091 & 0.0007 & 0.1022 & 0.0328 \\ MSE & & & 0.0091 & & 0.0072 \\ NMSE & & & 0.0956 & & 0.0848 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of model accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(t\) & \(X_{3}\) & \(X_{1}\) & \(absd\) & \(D\) \\ \hline
0 & 0.29 & 0.29 & 0 & 0 \\
0.1 & 0.32 & 0.33 & 0.00 & 0.02 \\
0.2 & 0.35 & 0.37 & 0.01 & 0.04 \\
0.3 & 0.4 & 0.42 & 0.02 & 0.05 \\
0.4 & 0.44 & 0.47 Visual analytics and statistical modeling were used to verify the proposed approaches to mathematical modeling. The authors' comparative analysis showed the solutions produced by visual analytics, mathematical statistics and differential equations matched: the actors have negative attitudes toward the project. Discrepancies in the analyses made by visual analytics and by mathematical models may stem from complex relations that simple methods cannot grasp. The system of differential equations was parametrized by Nelder-Mead optimization and by artificial neural networks. Although the solutions by the two models were virtually identical at and near the initial conditions, we believe ANN-based models have several advantages. They are adaptive, resistant to noise, easier to use, and have high computational performance. Equally important is the fact that the structure of the ODE 2 models was selected from a class, and the class was selected on the basis of a priori data. However, the model structure was finalized by the neural network from data. The analysis of messages shows that most of the media content covering the implementation of the NEC project consists of official sources and traditional media. The rating of texts is topped by the mayor's tweet (17 May 2017) about the completion of the construction of a NEC section from Shchelkovskoye highway to Losinoostrovskaya street (1 031 548). The analysis of official messages shows the general representation of the media content related to the NEC construction. Meanwhile, the residents' perception of the project progress and their attitude towards the NEC construction is shown by the analysis of the content generated by users and actors' digital footprints in social networks. The dynamics of messaging with various sentiment types show a sharp drop in the number of texts with a neutral sentiment during the analyzed period, while the number of negative and positive messages increases. The analysis of the dynamics of messages shows that neutral messages forming a large part of the media content regarding the NEC construction, and positive references do not in any way correlate with the negative content. Thus, the official neutral and positive messages do not correlate and do not affect the perception of users who generate the negative content dedicated to the NEC implementation project, and expressed the negative perception of the project by the residents (Figures 12 and 13). The peak of negative reactions was recorded on August 21, 2020 (Figure 13) and was associated with the trial of Moscow City Duma deputy, [PERSON], who campaigned against the NEC construction and the destruction of Kuskovo Park.