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Identifying lexical change in negative word-of-mouth on social media

Title data

Strathern, Wienke ; Ghawi, Raji ; Schönfeld, Mirco ; Pfeffer, Jürgen:
Identifying lexical change in negative word-of-mouth on social media.
In: Social Network Analysis and Mining. Vol. 12 (2022) Issue 1 . - No. 59.
ISSN 1869-5469
DOI: https://doi.org/10.1007/s13278-022-00881-0

Abstract in another language

Negative word-of-mouth is a strong consumer and user response to dissatisfaction. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. In this work, we examine the change of vocabulary to explore the outbreak of online firestorms on Twitter. The sudden change of an emotional state can be captured in language. It reveals how people connect with each other to form outrage. We find that when users turn their outrage against somebody, the occurrence of self-referencing pronouns like ‘I’ and ‘me’ reduces significantly. Using data from Twitter, we derive such linguistic features together with features based on retweets and mention networks to use them as indicators for negative word-of-mouth dynamics in social media networks. Based on these features, we build three classification models that can predict the outbreak of a firestorm with high accuracy.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Languages and Literature
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung - Juniorprof. Dr. Mirco Schönfeld
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 08 Jun 2022 07:33
Last Modified: 08 Jun 2022 07:33
URI: https://eref.uni-bayreuth.de/id/eprint/69983