Assessing perceived organizational leadership styles through twitter text mining

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.

Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage

“…Dior has also the highest sentiment. It is important to notice that the approach we adopted here strongly differs from an analysis of tweets carried out without the identification of tribes; a limitation that characterizes many studies (e.g., La Bella, Fronzetti Colladon, Battistoni, Castellan, & Francucci, 2018). Generally speaking, tweets about a company may be about very different topicssuch as its financial performance, its products, or the latest news about the board members.…”

What is work engagement? A text mining approach using employees' self‐narratives

“…This study indicated that one can use text mining to measure the character strengths of large populations. Similarly, La Bella et al (2018) used text mining to track perceived organizational leadership styles almost real-time with Twitter messages. Examples in clinical settings include the screening of posttraumatic stress disorder in self-narratives (He et al, 2012) and the identification of trauma patients (Day et al, 2007).…”