English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Studies across many disciplines have shown that lexical choice can affectaudience perception. For example, how users describe themselves in a socialmedia profile can affect their perceived socio-economic status. However, welack general methods for estimating the causal effect of lexical choice on theperception of a specific sentence. While randomized controlled trials mayprovide good estimates, they do not scale to the potentially millions ofcomparisons necessary to consider all lexical choices. Instead, in this paper,we first offer two classes of methods to estimate the effect on perception ofchanging one word to another in a given sentence. The first class of algorithmsbuilds upon quasi-experimental designs to estimate individual treatment effectsfrom observational data. The second class treats treatment effect estimation asa classification problem. We conduct experiments with three data sources (Yelp,Twitter, and Airbnb), finding that the algorithmic estimates align well withthose produced by randomized-control trials. Additionally, we find that it ispossible to transfer treatment effect classifiers across domains and stillmaintain high accuracy.

When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation