This paper examined a dataset of more than 45,000 Kickstarter projects to determine what properties make a successful Kickstarter campaign (in this case, defining success as driving sufficient crowd financial interest to meet the project\u2019s funding goal).\u00a0 More specifically, the authors used both quantitative control variables (e.g., project duration, video present, number of updates) as predictors, as well as scraping the language in each project\u2019s home page for common phrases.\u00a0 By combining both components, the authors created a penalized logistic regression model that could predict whether or not a project would be successfully funded with only a 2.24% error rate.\u00a0 The authors extended their discussion of the phrases to common persuasive techniques from literature such as reciprocity and scarcity to better explain the persuasive power of some phrases.<\/p>\n
I thought that one of the most useful part of this paper relative to the upcoming course project was the collection of descriptions and uses of the tools used by the authors.\u00a0 Should my group\u2019s course project attempt something similar, it is nice to know about the existence of tools such as Beautiful Soup, cv.glmnet, LIWC, and Many Eyes for data collection, preprocessing, analysis, and presentation.\u00a0 Other techniques such as Bonferroni correction and data repositories like the Google 1T corpus could also come in handy, and it is nice to know that they exist.\u00a0 Has anyone else in the class ever used any of these tools?\u00a0 Are the straightforward and user-friendly, or a nightmare to work with?<\/strong><\/p>\n The authors aimed to find phrases that were common across all Kickstarter projects, and so they eliminated phrases that did not appear in all 13 project categories.\u00a0 As a result, phrases such as \u201cgame credits\u201d and \u201cour menu\u201d were removed from the Games and Food categories respectively.\u00a0 I can certainly understand this approach for an initial study into Kickstarter funding phraseology, but I would be curious to see if any of these specific phrases (or lack of them) were strong predictors of funding within each category.\u00a0 I would speculate that a lack of phrases related to menus would be harmful to a funding goal in the Food category.\u00a0 There might even be some common predictors that are shared across a subset of the 13 project categories; it would be interesting to see if phrases in the Film & Video and Photography categories were shared, or Music and Dance for another example.\u00a0 How do you think some of the results from this study might have changed if the filtering steps were more or less restrictive?<\/strong><\/p>\n Even after taking machine learning and data analytics classes, I still treat the outputs of many machine learning models as computational magic.\u00a0 As I glanced through Tables 3 and 4, a number of phrases surprised me in each group.\u00a0 For example, the phrase \u201ctrash\u201d was an indicator that a project was more likely to be funded, while \u201chand made by\u201d was an indicator that a project would not be funded.\u00a0 I would have expected each of these phrases to fall into the other group.\u00a0 Further, I noted that very similar phrases also existed across categories:\u00a0 \u201cfunding will help\u201d indicated funding, whereas \u201ccampaign will help\u201d indicated non-funding.\u00a0 Did anyone else notice unexpected phrases that intuitively felt like they were placed in the wrong group?\u00a0 Does the principle of keeping data in context that we discussed last week come into play here?\u00a0 <\/strong>Similarly, I thought that the Authority persuasive argument went counter to my own feelings.\u00a0 I would tend to view phrases like \u201cproject will be\u201d as cocky and therefore would have a negative reaction to them, rather than treating them as expert opinions.\u00a0 Of course, that\u2019s just my own view, and I\u2019d have to read the referenced works to better understand the argument in the other direction.<\/p>\n I suspect that this paper didn\u2019t get as much attention as Google Flu Trends (no offense, professor), but I\u2019m curious to know if the phrasing in Kickstarter projects changed after this work was published.\u00a0 Perhaps this could be an interesting follow-up study; have Kickstarter creators become more likely to use phrases that indicated funding and less likely to use phrases that indicated non-funding after the paper and datasets were released?\u00a0 <\/strong>Another interesting follow-up study was hinted at in the Future Work section.\u00a0 Since Kickstarter projects can be tied to Facebook, and because \u201cFacebook Connected\u201d was a positive predictor of a project being funded, a researcher could explore the methods by which these Kickstarter projects are disseminated via social media.\u00a0 Are projects more likely to be funded based on number of posts?\u00a0 Quality of posts (or phrasing in posts)?\u00a0 The number of Facebook profiles that see a post related to the project?\u00a0 That interact with a post related to the project?<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" This paper examined a dataset of more than 45,000 Kickstarter projects to determine what properties make a successful Kickstarter campaign (in this case, defining success as driving sufficient crowd financial interest to meet the project\u2019s funding goal).\u00a0 More specifically, the authors used both quantitative control variables (e.g., project duration, video present, number of updates) as […]<\/p>\n","protected":false},"author":133,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-105","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/105","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/users\/133"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/comments?post=105"}],"version-history":[{"count":1,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/105\/revisions"}],"predecessor-version":[{"id":106,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/posts\/105\/revisions\/106"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/media?parent=105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/categories?post=105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724spring18\/wp-json\/wp\/v2\/tags?post=105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}