Assessing perceived organizational leadership styles through twitter text mining

TL;DR

This paper introduces a support vector machine-based text classification tool to evaluate perceived organizational leadership styles through Twitter data, enabling rapid social media-based leadership assessment.

Contribution

It presents a novel method for assessing leadership styles from Twitter interactions using a support vector machine classifier and a 10-factor leadership model.

Findings

Effective classification of leadership styles from Twitter data

Rapid assessment of perceived leadership capabilities

Insights into stakeholder reactions and company responses

Abstract

We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.