Proceedings of the International scientific and practical conference ―Oxford International Science Forum‖ (April 17-19, 2026) / Publisher website: www.naukainfo.com. - Oxford, United Kingdom, 2026. - 367 p.

102 funding dynamics. Donation-based campaigns emphasize urgency, narrative credibility, emotional appeal, and network outreach. Share-based campaigns include additional investment-oriented and regulatory attributes that are less prominent in other models [2, 7-11]. According to the data structure, CD can be divided into structured, semi- structured and unstructured categories. Structured data includes target amount, pledged amount, number of backers, campaign duration, country and category. Semi- structured data includes API responses, tagged campaign metadata, update records or export reports. Unstructured data includes project descriptions, comments, FAQ sections, narrative updates and external text mentions [4-5, 9]. From a time perspective, CD can be divided into static data, quasi-static data, and dynamic data. Static data includes initial campaign parameters, such as title, category, target amount, and country. Quasi-static data includes edited descriptions, reward adjustments, or modified campaign elements. Dynamic data includes fund tracking, comment campaigns, update frequency, and daily or hourly support behavior [1, 5-6]. According to the data, CD can be divided into platform native data, user- generated data, external signals and analysis-derived metrics. Platform native data comes directly from the crowdfunding page, platform backend or official export mechanism. User-generated data includes comments, likes and creator responses. External signals may come from social media, forums or media reports. Derivative metrics are calculated through analysis, such as sentiment score, fund acceleration, momentum index or creator activity score [2, 4, 12-13]. Based on its analytical function, CD can serve as target variables, predictive variables, contextual variables, or explanatory signals. Typical target variables include success/failure, fundraising success rate, time required to reach the goal, expected oversubscription amount, or outlier ratings. Predictive variables include numerical, categorical, linguistic, temporal, and behavioral attributes. Contextual variables describe platform type, project category, country, or fundraising model.