Abstract

"This paper presents an empirical test of disputants' settlement behavior using online copyright notices. The Section 512(c) notice-and-takedown regime provides a natural setting to study the signaling aspect of pretrial bargaining. We apply text analysis to quantify the attributes of notice as a pretrial signal, and we use the text data to evaluate how different factors help to close the information gap and improve the settlement rate. The three primary determinants that help settlement are found to be text features of the complaints, legal representation, and platform mediation. A strong signal is short, easy to read, and more specific. Legal representation improves the credibility of the signal. Platform mediation, on the other hand, adds commitment to the signal. Interestingly, how the lawyers draft a notice compromises the positive effect of legal representation. Lawyers prefer long sentences, big words, and more terminology, whereas an effective notice is much more concise. Most of our empirical findings support theoretical predictions, but we also discuss some discrepancies between the two."

About the Authors

Pengfei Zhang (pictured) is an assistant professor of public policy, economics, and cybersecurity at the University of Texas at Dallas. He earned his Ph.D. in economics at Cornell in 2022. He studies the regulation and governance of digital platforms, including content moderation, intermediate liability, copyright, cybersecurity, and censorship.

Ji Li is a Ph.D. candidate in economics at the University of Texas at Dallas. He works on the application of textual analysis in law and economics, as well as asset pricing, focusing on extracting valuable insights from copyright notices, financial statements, and government reports.

Publication Details

Year: 2024