Archive / INF Seminars / INF_2019_12_02_Robert_West
USI - Email

Causal Effects of Brevity on Style and Success in Social Media


Host: Prof. Fabio Crestani




USI Lugano Campus, room SI-007, Informatics building

Robert West
EPFL, Switzerland
In online communities, where billions of people strive to propagate their messages, understanding how wording affects success is of primary importance. In this work, we are interested in one particularly salient aspect of wording: brevity. What is the causal effect of brevity on message success? What are the linguistic traits of brevity? When is brevity beneficial, and when is it not?
Whereas most prior work has studied the effect of wording on style and success in observational setups, we conduct a controlled experiment, in which crowd workers shorten social media posts to prescribed target lengths and other crowd workers subsequently rate the original and shortened versions. This allows us to isolate the causal effect of brevity on the success of a message. We find that concise messages are on average more successful than the original messages up to a length reduction of 30–40%. The optimal reduction is onaverage between 10% and 20%. The observed effect is robust across different sub populations of raters and is the strongest for raters who visit social media on a daily basis. Finally, we discover unique linguistic and content traits of brevity and correlate them with the measured probability of success in order to distinguish effective from ineffective shortening strategies. Overall, our findings are important for developing a better understanding of the effect of brevity on the success of messages in online social media.

Robert West is a tenure-track assistant professor of Computer Science at EPFL (the Swiss Federal Institute of Technology, Lausanne), where he heads the Data Science Lab. He received his PhD in Computer Science from Stanford University, his MSc from McGill University, Canada, and his undergraduate degree from Technische Universität München, Germany. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in computational social science, social network analysis, machine learning, and natural language processing. Bob also collaborates closely with the Wikimedia Foundation, in his role as a Wikimedia Research Fellow. He is a co-founder of the Wiki Workshop and the Applied Machine Learning Days.