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Implementing Artificial Neural Network to Identify Influencers for Crowdfunding Campaigns on Twitter

Authors:
Frank Yeong-Sung Lin
Evana Szu-Han Fang
Chiu-Han Hsiao
Hsin-Hong Lin

Keywords: social media analysis; influencer marketing; artificial neural network; sentiment analysis.

Abstract:
This study proposes an Artificial Neural Network (ANN) model for ranking potential influencers for crowdfunding campaigns on Twitter. Because influencers have a strong connection with their followers and are considered trustworthy key opinion leaders, identifying them provides opportunities for start-up companies to reach highly relevant audiences and promote their campaigns. In this study, the social authority value, a mechanism developed by Followerwonk, was employed to examine the influence strength of a Twitter user. Followerwonk is one of the most popular Twitter marketing platforms in the United States. A total of 20 influence factors of 1969 Twitter users were collected to train the ANN model. The results revealed that 13 of the 20 influence factors were significant for measuring influence strength, which improved the time efficiency of the process of evaluating potential influencers. This model can be effectively and cost- efficiently applied to support start-up companies, thus increasing the success rate of campaigns by utilizing influencer marketing.

Pages: 27 to 35

Copyright: Copyright (c) IARIA, 2019

Publication date: July 28, 2019

Published in: conference

ISSN: 2308-3972

ISBN: 978-1-61208-728-3

Location: Nice, France

Dates: from July 28, 2019 to August 2, 2019