The Importance of Interactions Between Content Characteristics and Creator Characteristics for Studying Virality in Social Media

Author:

Han Yue1ORCID,Lappas Theodoros2ORCID,Sabnis Gaurav2ORCID

Affiliation:

1. Madden School of Business, Le Moyne College, Syracuse, New York 13214;

2. School of Business, Stevens Institute of Technology, Hoboken, New Jersey 07030

Abstract

Why does a social media post go viral? Two approaches to understand this mystery are content-based research and creator-based research. Both content characteristics and creator characteristics have been examined for their influence on virality. But the relationships between them are rarely discussed. We propose an extension to our existing conceptual framework to study the interactions between content and creator variables. And we demonstrate the significance of the interactions using data from 800,000 tweets. We find that by adding content-–creator interactions, the predictive power of the model improves significantly, which underlines the importance of the interactions for studying virality in social media. We also provide insights for managers on shaping their social media presence and strategy to use social media popularity for marketing and brand building.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

Reference44 articles.

1. Abbasi MA, Liu H (2013) Measuring user credibility in social media. Greenberg AM, Kennedy WG, Bos ND, eds. Internat. Conf. Soc. Comput. Behav.Cultural Model. Prediction (Springer, Berlin), 441–448.

2. Twitter for crisis communication: lessons learned from Japan's tsunami disaster

3. Asuncion A, Welling M, Smyth P, Teh YW (2009) On smoothing and inference for topic models. Bilmes J, Ng A, eds. Proc. 25th Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 27–34.

4. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: Quantifying influence on Twitter. Proc. 4th ACM Internat. Conf. Web Search Data Mining (ACM, New York), 65–74.

5. What Makes Online Content Viral?

Cited by 58 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3