Linguistic Markers of Influence in Informal Interactions
This work addresses the challenge of understanding social influence for researchers in computational linguistics and social sciences, but it is incremental as it builds on existing principles with a specific domain application.
The paper tackled the problem of measuring interpersonal influence in daily interactions by identifying linguistic features in posts from an online knitting community, resulting in a 3.15% improvement in classification accuracy for influence.
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.