TY - JOUR
T1 - Predicting new venture survival
T2 - A Twitter-based machine learning approach to measuring online legitimacy
AU - Antretter, Torben
AU - Blohm, Ivo
AU - Grichnik, Dietmar
AU - Wincent, Joakim
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures’ Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.
AB - Research indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures’ Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.
KW - 512 Business and Management
UR - http://www.scopus.com/inward/record.url?scp=85059509620&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/predicting-new-venture-survival-twitterbased-machine-learning-approach-measuring-online-legitimacy
U2 - 10.1016/j.jbvi.2018.e00109
DO - 10.1016/j.jbvi.2018.e00109
M3 - Article
SN - 2352-6734
VL - 11
SP - 1
EP - 8
JO - Journal of Business Venturing Insights
JF - Journal of Business Venturing Insights
IS - June
M1 - e00109
ER -