Abstract
The openness of Internet and the use of big data often lead to reliability issues. The advancement of social networks necessitates the need for a more effective approach to security management, especially in crowdsourcing-based vehicular social networking systems. This paper discusses issues surrounding the practice of trust management in international crowdsourcing projects based on massive amounts of unscrupulous data. The study looks at Bellingcat's investigative projects to identify the working patterns of data verification. Data analysis was performed with the help of the grounded theory methodology. The results of the study suggest that functional algorithms for news screening should be integrated with intuition-based methods of data verification. The proposed approach is expected to reduce the number of factors affecting the accuracy of crowdsourced data analysis.
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