Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors, we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.

Salvatore, C., Bianchi, A., Biffignandi, S. (2020). Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies. In 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020) (pp.269-277). CAMINO VERA S-N, VALENCIA, 46022, SPAIN : Editorial Universitat Politècnica de València [10.4995/CARMA2020.2020.11646].

Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies

Salvatore, Camilla;
2020

Abstract

Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors, we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.
abstract
Topic modelling; Structural Topic Model; Social media communication
English
International Conference on Advanced Research Methods and Analytics (CARMA2020)
2020
3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)
9788490488324
2020
269
277
none
Salvatore, C., Bianchi, A., Biffignandi, S. (2020). Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies. In 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020) (pp.269-277). CAMINO VERA S-N, VALENCIA, 46022, SPAIN : Editorial Universitat Politècnica de València [10.4995/CARMA2020.2020.11646].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/317611
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