Linguistic Markers of Influence in Informal Interactions

TL;DR

This paper introduces a novel linguistic feature-based method to measure social influence in online interactions, demonstrating that language characteristics can predict influence with improved accuracy.

Contribution

It presents a new approach to operationalize and identify linguistic markers of influence in online communities, with experimental validation showing enhanced classification performance.

Findings

Linguistic features correlate with influence in online posts

Operational scheme effectively labels influence indicators

Classification accuracy improves by 3.15% using identified features

Abstract

There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.