[1]. Mitra, T. and Gilbert, E. 2014. The language that gets people to give. Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing – CSCW ’14.
Summary :
In this paper, the authors aim to answer the research question ‘What are the factors that drive people to fund projects on crowdfunding sites?’. To this end, Tanushree Mitra et al. studied a corpus of 45K projects from Kickstarter, a popular crowdfunding site. They carried out filtering techniques to eliminate bias and finally analyzed 9M phrases and 59 control variables to identify their predictive power in a project getting funded by the “crowd”. The error rate of their cross-validated penalized logistic regression model is only 2.4%. The authors found that the chances of funding increase if : the pitch offers incentives and rewards(reciprocity), the pitch has opportunities which are rare or limited in supply(scarcity), the pitch’s wordings indicate that it is already pledged by others(social proof), the pitch is from a project creator that people like(liking), the pitch has a positive and confident language(LIWC and sentiment), and, the pitch is endorsed by experts(authority). By performing this research they have made available a ‘phrase and control variables’ dataset. This dataset contains phrases and control variables that can be put to further use by crowdfunding sites and other researchers.
Reflection:
‘The language that gets people to give’ is an engaging research paper. I admire the effort put by the authors in analyzing a corpus of 45K Kickstarter projects. The flowchart of the steps taken to extract the variables to be used in the model was helpful for understanding the process of obtaining the phrases and control variables finally analyzed. The fact that the control variables are not specific for the Kickstarter platform aids in making this research more useful for all crowdfunding platforms. I like the Word Tree visualizations that were provided by the author. The role that persuasion phrases and concepts like reciprocity, scarcity, authority, and, sentiment play in getting a project funded were fascinating to read about. Features like ‘Video present’, ‘number of comments’ and ‘facebook connected’ emphasize the social aspects of this analysis. Few of the top 100 phrases listed in the paper surprised me, however, I could definitely spot the patterns that the authors identified. It is indeed impressive to see that a quantitative analysis using machine learning techniques can validate reciprocity, liking, and, scarcity, etc. I was amazed by the ‘good karma’ phrase. This phrase and its mention with respect to reciprocity made me realize that it would be exciting to study the crowdfunding projects to answer the question, ‘Do religious and spiritual beliefs impact the decision a person makes in funding a project? Do these beliefs hold more importance than the incentive rewards in the reciprocity phenomenon?’. On observing the tables listing the control variables having non-zero coefficients, I found that many of the variables in the not funded table were related to the ‘music’ and ‘film’ categories. This gave rise to the question ‘Do some beliefs (e.g. projects in these categories may not be successful) influence the decision of funding the project? Do these beliefs way more than factors like reciprocity, authority, liking, etc.?’. I appreciate the ideas for future work that the authors have provided. I believe that implementing a feature like providing a recommendation to project creator while the pitch is being typed using the phrases and control variable dataset that the authors have released could be extremely interesting.
