Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, the terrain of charity crowdfunding within the Social Web remains relatively unexplored, lacking comprehension of the motivations driving donations that often lack concrete reciprocation. Distinct from conventional crowdfunding that offers tangible returns, charity crowdfunding relies on intangible rewards like tax advantages, recognition posts, or advisory roles. Such details are often embedded within campaign narratives, yet, the analysis of textual content in charity crowdfunding is limited. This study introduces an inventive text analytics framework, utilizing Latent Dirichlet Allocation (LDA) to extract latent themes from textual descriptions of charity campaigns. The study has explored four different themes, two each in campaign and incentive descriptions. Campaign description themes are focused on child and elderly health mainly the ones who are diagnosed with terminal diseases. Incentive description themes are based on tax benefits, certificates, and appreciation posts. These themes, combined with numerical parameters, predict campaign success. The study was successful in using Random Forest Classifier to predict success of the campaign using both thematic and numerical parameters. The study distinguishes thematic categories, particularly medical need-based charity and general causes, based on project and incentive descriptions. In conclusion, this research bridges the gap by showcasing topic modelling utility in uncharted charity crowdfunding domains.

翻译：社交网络领域的众筹已受到广泛关注，先前的研究考察了活动的多方面要素，包括项目目标、持续时间以及成功筹款的关键项目类别。这些因素对于寻求捐助者支持的企业家至关重要。然而，社交网络中慈善众筹领域的研究仍相对不足，缺乏对驱动捐赠动机（这些捐赠往往缺乏实质性回报）的理解。与提供具体回报的传统众筹不同，慈善众筹依赖无形奖励，如税收优惠、认可帖或顾问角色。此类细节通常嵌于活动叙事中，但对慈善众筹文本内容的分析仍十分有限。本研究提出了一种创新的文本分析框架，利用潜在狄利克雷分配（LDA）从慈善活动的文本描述中提取潜在主题。研究探索了四个不同主题，其中两个来自活动描述，两个来自激励描述。活动描述主题聚焦于儿童与老年人健康，尤其针对被诊断患有绝症的人群。激励描述主题则基于税收优惠、证书及感谢帖。这些主题与数值参数相结合，共同预测活动成功与否。研究成功运用随机森林分类器，结合主题与数值参数预测活动结果。研究根据项目与激励描述，区分了主题类别，特别是基于医疗需求的慈善与一般性公益。总之，本研究通过展示主题建模在尚未探索的慈善众筹领域的应用价值，填补了研究空白。