An Examination of the Key Components of Social Capital and Its Measurement Based on the Principles of Computational Social Science
Abstract
One of the most important indicators for assessing the success or failure of political systems around the world is the state of their social capital. Social capital lacks a fixed and universally accepted definition, and its components are constantly being reconstructed and redefined. In Iran, the measurement of social capital fundamentally differs from the European and American models used to monitor it. It is not possible to gain an accurate understanding of social capital—especially that of the governing system—merely by analyzing social networks within society. Given the extensive responsibilities and central role of the state in Iran, along with the significant weakness of the middle class, civil organizations, associations, and reference groups, it appears that evaluating government performance has consistently taken higher priority.
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Key Components of Social Capital
Social capital consists of trust, networks, and participation, divided into government-related and public-related components. In Iran, due to the dominant role of the state and weak civil institutions, measuring trust in governance and state-society relations is especially important.
Methodology for Measuring Social Capital
This report introduces a model for measuring governance-related social capital through analysis of social media content, focusing on trust in governance, state-society relations, and social participation. Using 8 indicators and 40 components, data is collected from Telegram, Instagram, and Twitter posts and comments, based on tailored keyword queries. Each component is analyzed using sentiment analysis, and the results are weighted and compared to a 2018 national survey as a baseline. The rate of change in sentiment determines the current level of social capital, offering a context-sensitive alternative to traditional surveys.
Analytical Frameworks
The social capital of the governing system is assessed using validated indicators like trust, state-society relations, and participation, with weights based on expert input and online data. Sentiment analysis in virtual spaces enables ongoing, unbiased monitoring.
This study was conducted at Computational Social Science Center and Ditak company in collaboration with Alireza Haddadi , Rasoul Mohsenzadeh, Moein Kazemi and Ahmad Yazdaniyan commissioned by Islamic Parliament Research Center Of The Islamic Republic Of IRAN in 2022.
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