Causal Effects of Brevity on Style and Success in Social Media
Not on the map. Why?
Abstract (arXiv)
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 on average between 10% and 20%. The observed effect is robust across different subpopulations 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 2.
4 papers.
- 2024Hypothesis Generation with Large Language Modelscited by 68
- 2022Current and Near-Term AI as a Potential Existential Risk Factorcited by 14
- 2024Estimating Causal Effects of Text Interventions Leveraging LLMscited by 4
- 2022Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisationcited by 1
- 2024Estimating Causal Effects of Text Interventions Leveraging LLMs28 Oct 2024
- 2024Hypothesis Generation with Large Language Models5 Apr 2024
- 2022Current and Near-Term AI as a Potential Existential Risk Factor21 Sep 2022
- 2022Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation7 Feb 2022