The determinants of crowdfunding success: A semantic text analytics approach

Yuan, Hui; Lau, Raymond Y. K.; Xu, Wei

doi:10.1016/j.dss.2016.08.001

Cited by 181 publications

(86 citation statements)

References 21 publications

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“…Related studies also find a positive correlation between project success and the dialogue between fundraisers and pledgers(Beaulieu and Sarker 2013;Kromidha and Robson 2016;Allison et al 2017;Clauss et al 2018;Wang et al 2018), project descriptions' language(Frydrych et al 2016;Yuan et al 2016;Gafni et al 2019;Parhankangas and Renko 2017), or social media usage(Borst et al 2017). …”

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confidence: 88%

Crowdfunding a monthly income: an analysis of the membership platform Patreon

Regner¹

2020

J Cult Econ

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Membership platforms allow creators to crowdfund a monthly income, while campaigns on conventional reward crowdfunding portals aim to reach a specified funding target within a preset period. We study transaction-level data from Patreon, the biggest membership platform, to gain insights about behavioral patterns at this emerging type of crowdfunding. Our analysis shows that hundreds of creators crowdfund a sizable income (more than $2500 monthly). We also find that measures of communication quality are determinants of project success, in line with the related literature. Funding dynamics-pledges as well as deletions-are heterogeneous across campaigns. Our analysis suggests that the option to delete the monthly pledge to a creator at any time serves as a feedback mechanism. We conclude that crowdfunding a monthly income offers the creative class a viable alternative to advertising-based business models.

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mentioning

confidence: 88%

Crowdfunding a monthly income: an analysis of the membership platform Patreon

Regner¹

2020

J Cult Econ

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“…The differences in distance diffusion between categories reflect the inconsistency in investors' evaluation criteria. Previous studies have shown that the specialization of food crowdfunding projects, such as environment and food security [46], along with the description relating to environmental protection and food safety, are very useful in pinpointing fund raising success [47]. However, previous studies did not pay attention to the differences among the sources of investors.…”

Section: Contribution To Theorymentioning

confidence: 99%

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

et al. 2018

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As the information communication and investment of crowdfunding campaigns are usually accomplished online, online crowdfunding theoretically breaks the limitation of space. However, investors' behaviors still show significant home bias; furthermore, from a dynamic view, the changes in home bias during the funding duration reflect the investment preference. This paper studies the dynamics of home bias in crowdfunding investment as well as the distance diffusion of home bias in the crowdfunding market through data mining and econometric models. The distance between investors and founders gradually increases from 3605 km to 4229 km as the funding progresses, and home bias shows different diffusion patterns between categories. For most of the campaigns, the distance between backers and founders of the successful campaigns is always greater than that of the failed ones; thus, the distance diffusion impacts the pledge results. However, the study also finds that for some categories, home bias does not play a positive role in investment or that it even shows negative impacts, among which food and technology are two extreme categories. The study provides a theoretical basis from the perspective of user behavior to analyze online investment and to improve the promotion of crowdfunding campaigns.

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“…In [28], crowdfunding success prediction is estimated through a text analytics approach, where LDA is used to extract semantic features out of the text, along with feature selection, and data mining. In a similar work [29], crowdfunding updates are analyzed by using LDA to classify the updates into different topic categories.…”

Section: Predictions and Recommendations In Crowdfundingmentioning

confidence: 99%

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Shafqat

Byun

2019

Applied Sciences

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The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers' decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

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The determinants of crowdfunding success: A semantic text analytics approach

Cited by 181 publications

References 21 publications

Crowdfunding a monthly income: an analysis of the membership platform Patreon

Crowdfunding a monthly income: an analysis of the membership platform Patreon

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

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