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Detecting Online Firestorms in Social Media

Title data

Drasch, Benedict ; Huber, Johannes ; Panz, Sven ; Probst, Florian:
Detecting Online Firestorms in Social Media.
2015
Event: 36th International Conference on Information Systems (ICIS) , 13.-16. Dez. 2015 , Fort Worth, USA.
(Conference item: Conference , Paper )

Official URL: Volltext

Abstract in another language

As social media has increased the reach and speed of electronic word-of-mouth (eWOM), so it has intensified customers’ exposure to negative eWOM. Consequently, companies increasingly suffer from massive outbursts of negative eWOM, known as online firestorms. Because of their dynamics, it is nearly impossible to stop online firestorms if their emergence is not detected promptly. However, well-founded approaches that provide automated, real-time detection are missing. We design an Online Firestorm Detector that includes an algorithm inspired by epidemiological surveillance systems. Real-world data from a firestorm suffered by Coca-Cola is used to prove the utility and validity of the proposed approach. We show that online firestorms can be reliably detected shortly after the first piece of related negative eWOM has been generated, and that the number of false alarms is low. A comparison with competing artifacts shows that the Online Firestorm Detector is superior to approaches that could be alternatively used.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Word-of-mouth; social media; online firestorm; information diffusion; design science
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 25 Jan 2016 07:37
Last Modified: 19 Dec 2018 13:39
URI: https://eref.uni-bayreuth.de/id/eprint/29911