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

Abstract 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, word, or action of person resulting in hateful speech. In this work, we examine the change vocabulary explore outbreak online firestorms on Twitter. The sudden emotional state be captured language. It reveals how people connect with each other form outrage. We find that when users turn their outrage somebody, occurrence self-referencing pronouns like ‘I’ ‘me’ reduces significantly. Using data from Twitter, derive such linguistic features together based retweets mention networks use them as indicators for negative dynamics social media networks. Based these features, build three classification models predict firestorm high accuracy.