%0 Conference Paper %1 bergmann2016tweeting %A Bergmann, Jens %A Hadgu, Asmelash Teka %A Jäschke, Robert %B Proceedings of the Workshop on Natural Language Processing and Computational Social Science %C Hannover, Germany %D 2016 %K %T Tweeting in times of exposure: A mixed-methods approach for exploring patterns of communication related to business scandals on Twitter %X Currently, three trends mutually influence each other and can be observed using social media: (a) the growing use of social media, in particular Twitter, by organizations, (b) increased expectations of transparency towards organizations, and (c) massive public response to organizational crises via social media. Getting an understanding on how customers and organizations react to crises and crises responses as well as identifying different communication strategies is difficult, since the large amount of actors and the abundance of messages can not be handled by traditional methods from the Social Sciences. These often rely on manual work, for instance, interviews, qualitative studies, or questionnaires. Even large parts of content analysis using computer-assisted qualitative data analysis software have to be supported by manual work. At the same time, the availability and accessibility of large volumes of messages on Twitter also opens up possibilities for mixed-methods approaches to analyze this data. In particular, natural language processing can support the analysis of large sets of tweets. In this work we present first steps towards a large-scale analysis of Twitter communication during corporate crises by leveraging a mixed-methods approach. Such analyses can improve our understanding of organizational crises and their communication and can also prove beneficial to provide recommendation for successful reactions and interactions.

@inproceedings{bergmann2016tweeting, abstract = {Currently, three trends mutually influence each other and can be observed using social media: (a) the growing use of social media, in particular Twitter, by organizations, (b) increased expectations of transparency towards organizations, and (c) massive public response to organizational crises via social media. Getting an understanding on how customers and organizations react to crises and crises responses as well as identifying different communication strategies is difficult, since the large amount of actors and the abundance of messages can not be handled by traditional methods from the Social Sciences. These often rely on manual work, for instance, interviews, qualitative studies, or questionnaires. Even large parts of content analysis using computer-assisted qualitative data analysis software have to be supported by manual work. At the same time, the availability and accessibility of large volumes of messages on Twitter also opens up possibilities for mixed-methods approaches to analyze this data. In particular, natural language processing can support the analysis of large sets of tweets. In this work we present first steps towards a large-scale analysis of Twitter communication during corporate crises by leveraging a mixed-methods approach. Such analyses can improve our understanding of organizational crises and their communication and can also prove beneficial to provide recommendation for successful reactions and interactions.}, added-at = {2023-12-13T09:19:25.000+0100}, address = {Hannover, Germany}, author = {Bergmann, Jens and Hadgu, Asmelash Teka and Jäschke, Robert}, biburl = {https://www.bibsonomy.org/bibtex/2b83671f47d763cc9bd4f1574c11fea36/admin}, booktitle = {Proceedings of the Workshop on Natural Language Processing and Computational Social Science}, interhash = {ccfe21d71b22abc8e33935cb58228145}, intrahash = {b83671f47d763cc9bd4f1574c11fea36}, keywords = {}, month = may, series = {NLP+CSS at WebSci}, timestamp = {2023-12-13T09:19:25.000+0100}, title = {Tweeting in times of exposure: A mixed-methods approach for exploring patterns of communication related to business scandals on Twitter}, year = 2016 }