The Impact of Textual Online Harassment on the Performance of Projects in Crowdfunding

In the consequence-free and anonymous online environment, online harassment has become a serious problem. In many crowdfunding platforms, there exists offensive speech on the project pages, which might force potential funders to leave the discussion and to give up investment. The effect of online harassment on project performance remains unknown. This study attempts to investigate to what extent the textual online harassment score and the project creator’s attitude towards textual online harassment might affect project performance. We constructed a Kickstarter panel dataset consisting of 388,100 projects and designed a novel framework and an algorithm BiLSTM-CNN to extract the textual online harassment score from comments, which can reach column-wise mean ROC AUC of 0.9463. This study contributes to crowdfunding and online harassment literature and provides important implications for reputation management of projects and crowdfunding platform design.

[1]  Christopher D. Manning,et al.  Baselines and Bigrams: Simple, Good Sentiment and Topic Classification , 2012, ACL.

[2]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[3]  Hadar Gafni,et al.  Gender Dynamics in Crowdfunding (Kickstarter): Evidence on Entrepreneurs, Investors, Deals and Taste-Based Discrimination , 2016 .

[4]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[5]  Eric P. Xing,et al.  Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2014, ACL 2014.

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Armin Schwienbacher,et al.  Crowdfunding of Small Entrepreneurial Ventures , 2010 .

[8]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[9]  Stephen G. Donald,et al.  Inference with Difference-in-Differences and Other Panel Data , 2007, The Review of Economics and Statistics.

[10]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[11]  Chuang Wang,et al.  Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model , 2016, Inf. Syst. Res..

[12]  Ingmar Weber,et al.  Automated Hate Speech Detection and the Problem of Offensive Language , 2017, ICWSM.

[13]  Alok Gupta,et al.  Special Section Introduction - Ubiquitous IT and Digital Vulnerabilities , 2016, Inf. Syst. Res..

[14]  Eric Nichols,et al.  Named Entity Recognition with Bidirectional LSTM-CNNs , 2015, TACL.

[15]  Björn Gambäck,et al.  Using Convolutional Neural Networks to Classify Hate-Speech , 2017, ALW@ACL.

[16]  Ethan Mollick The Dynamics of Crowdfunding: An Exploratory Study , 2014 .

[17]  Eric Gilbert,et al.  The language that gets people to give: phrases that predict success on kickstarter , 2014, CSCW.

[18]  Brendan Maher,et al.  Can a video game company tame toxic behaviour? , 2016, Nature.

[19]  Vincent Etter,et al.  Launch hard or go home!: predicting the success of kickstarter campaigns , 2013, COSN '13.

[20]  Georgiana Dinu,et al.  Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.

[21]  R. Akers,et al.  Social Learning and Social Structure: A General Theory of Crime and Deviance. , 2000 .

[22]  Lucas Dixon,et al.  Ex Machina: Personal Attacks Seen at Scale , 2016, WWW.