# Assessing perceived organizational leadership styles through twitter text mining

arXiv: https://arxiv.org/abs/2105.11276 · HTML: https://mlsanity.com/paper/2105.11276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 67 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 1 of 1)

- [Using four different online media sources to forecast the crude oil price](https://mlsanity.com/paper/2105.09154/) (2021)

## Built on by (most cited first, 1 of 1)

- [Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage](https://mlsanity.com/paper/2105.13036/) (2021)

## Abstract (arXiv)

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.