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The Application Programming Interface as a Method for Monitoring Social Network Data for Research in Behavioral Economics
Lutsenko R. R.

Lutsenko, Rostyslav R. (2024) “The Application Programming Interface as a Method for Monitoring Social Network Data for Research in Behavioral Economics.” Business Inform 8:133–141.
https://doi.org/10.32983/2222-4459-2024-8-133-141

Section: Information Technologies in the Economy

UDC 330.4

Abstract:
In today’s behavioral economics, research based on social media data is becoming increasingly popular. Interactive platforms such as TikTok, Facebook, Instagram, X (Twitter), LinkedIn, and others generate a wealth of data that can be used to analyze user behavior. Processing large amounts of information requires the development of effective tools for collection and analysis of such data. The application programming interface (API) is one of the key methods that provides real-time access to data. The application programming interface allows scientists to automate the process of collecting information, customize queries according to various criteria, and obtain structured data for further analysis. The article examines the use of application programming interfaces as methods for monitoring social network data for research in behavioral economics. The features of the use of social networks for scientific research are analyzed. A monitoring method has been developed for collecting and processing data using APIs to study the behavior of social media users. The parameters of data collection (keywords, hashtags, time intervals, etc.) are determined, scripts for processing and storing data are created, a mechanism for starting the data collection process and monitoring its effectiveness and stability are designed. The proposed monitoring algorithm allows you to collect a representative database, analyze the content and interactions of social media users. APIs provide the ability to obtain a large amount of up-to-date and relevant information about user behavior, preferences, social interactions, and reactions to economic events, which allows for effective real-time data collection and analysis and increases the accuracy of forecasts in behavioral economics. Prospects for further research include the analysis of social media data, which will identify the impact of social and information factors on investment decision-making in the context of the market of virtual assets.

Keywords: application programming interface, API, social networks, monitoring, behavioral economics, tonality.

Fig.: 4. Bibl.: 16.

Lutsenko Rostyslav R. – Postgraduate Student, Department of Economic Cybernetics and Applied Economics, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]

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