During the past decade, social media has become an important channel for businesses to promote themselves, leading to new customer relations, competitive longevity, and an increase in customer markets. Although social media can have a positive influence to business reputation and global position, there are also negative aspects. Online portals such as Facebook and Twitter let negative word-of-mouth spread rapidly, damaging companies reputation and competitive advantage. Little is known about the implications these online firestorms have on businesses. Therefore, this thesis presents a connection between the rise in search volume, an increase in negative mentions as well as a decrease in stock prices through Volkswagens online scandal where they used car software to manipulate emission values in Europe and the United States. To show the impact negative word-of-mouth has on search volume, the VW scandal is presented in a Google Trends graph. Peaks throughout the graph distinguish dates with high search volume. Furthermore, IBMs artificial intelligence software, known as Watson Analytics, is used to investigate the implications online firestorms have with regards to the diesel affair on social media to show the bigger picture of the scandal. The “emission gate” is split into different countries, car brands and types. Watson is able to understand natural language and detect, then analyze posts with negative content about Volkswagen. A comparison is made between the VW share prices as shown at the American stock exchange NASDAQ, Google Trends and Watson graph. The main findings show that the search volume, number of negative posts, and stock prices appear to be strongly connected to each other. If the values for search volume and the amount of negative mentions on social media increase, the stock price is likely to drop right afterwards. However, findings do not point towards the ability to predict the exact price of these shares.