 Assessing perceived organizational leadership styles through twitter text miningAgostino La Bella, Andrea Fronzetti Colladon, Elisa Battistoni, Silvia Castellan and Matteo Francucci Journal of the Association for Information Science & Technology, 2018, vol. 69, issue 1, 21-31 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. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jinfst:v:69:y:2018:i:1:p:21-31 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the Association for Information Science & Technology from Association for Information Science & Technology |
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