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WIREs Data Mining Knowl Discov
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Discovering the influence of sarcasm in social media responses

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Abstract Sarcasm in verbal and nonverbal communication is known to attract higher attention and create deeper influence than other negative responses. Many people are adept at including sarcasm in written communication thus sarcastic comments have the potential to stimulate the virality of social media content. Although diverse computational approaches have been used to detect sarcasm in social media, the use of text mining to explore the influential role of sarcasm in spreading negative content is limited. Using tweets during a service disruption of a leading Australian organization as a case study, we explore this phenomenon using a text mining framework with a combination of statistical modeling and natural language processing (NLP) techniques. Our work targets two main outcomes: the quantification of the influence of sarcasm and the exploration of the change in topical relationships in the conversations over time. We found that sarcastic expressions during the service disruption are higher than on regular days and negative sarcastic tweets attract significantly higher social media responses when compared to literal negative expressions. The content analysis showed that consumers initially complaining sarcastically about the outage tended to eventually widen the negative sarcasm in a cascading effect towards the organization's internal issues and strategies. Organizations could utilize such insights to enable proactive decision‐making during crisis situations. Moreover, detailed exploration of these impacts would elevate the current text mining applications, to better understand the impact of sarcasm by stakeholders expressed in a social media environment, which can significantly affect the reputation and goodwill of an organization. This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Text Mining Application Areas > Industry Specific Applications Fundamental Concepts of Data and Knowledge > Big Data Mining
The text mining framework for quantifying and exploring the impact of sarcasm
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The shared/unique topics from sarcastic tweets for the two outages and the distribution of sarcastic tweets related to external issues (government policies related to telecommunications and organization strategies) and the cascading effect of the topics in sarcastic tweets during an outage
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The hourly trend of tweet measurements on the day of an outage
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Knowledge extraction process using NLP and topic modeling
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Fundamental Concepts of Data and Knowledge > Big Data Mining
Application Areas > Industry Specific Applications
Algorithmic Development > Text Mining
Technologies > Data Preprocessing

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