In September 2015 the Environmental Protection Agency found that many Volkswagen cars sold in the United States were equipped with software that could falsely improve the performance of diesel engines on emissions tests. This cheating was subsequently acknowledged by the car maker.

Among the many issues at stake for the company was one of public perception. Anecdotal evidence at the time of the incident suggested irreparable harm to the Volkswagen brand. So could Volkswagen recover in the short term in this regard? And, the broader question, how can you measure brand perception in times of scandal, particularly in an era where social media can cause negative news to proliferate and reverberate over time?

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In the absence of direct empirical evidence, we wanted to find a way to tackle this important issue. We began our research with some key questions: How does social media sentiment change as a consequence of a public relations crisis? How does the public react to recovery efforts initiated by the company? How do topics of conversation shift as a consequence of a brand scandal and subsequent recovery efforts?

We examined more than 100,000 tweets to analyze how the public sentiment changed over time after the breakout of the scandal. Our approach to capturing themes in the evolving scandal involved sampling a few date windows; therefore, we did not examine data for every single day. The following periods were selected: September 29, 2015–October 7, 2015; October 18, 2015–October 27, 2015; January 1, 2016–January 7, 2016; and January 17, 2016–January 25, 2016. These periods align with some of the events relating to the scandal, and also represent periods during and following the scandal. We explored the daily tweets from these periods by considering all possible events that might have affected the public sentiment over Volkswagen. Entire sets of tweets including the word “Volkswagen” were in our initial data set. We made several observations about how the scandal unfolded in the public conversation, broken out into the following categories.

Frequency. The number of times the scandal was mentioned on Twitter varied dramatically day by day, and the mentions seemed to parallel specific actions taken by Volkswagen to issue apologies or by regulatory agencies to place responsibility or issue punishments.

For example, after an article in The Guardian on September 30 revealed that the scandal has affected 1.2 million Volkswagen diesel vehicles, the number of tweets increased for the next two days. Subsequently, we observed a decrease in the range of number of tweets, from 5,000–7,000 to 1,000–2,000, except around January 6, which coincided with the following headline: “U.S. Sues Volkswagen in Diesel Emissions Scandal.”

Another exceptional surge in the number of tweets was on October 19, which could be explained by articles regarding the governments of France and Spain pushing the scandal investigations. We conjecture that the amount of tweets reflect the level of public interest in the scandal.

Vocabulary. We also identified the most-frequent words in tweets for each day by mining Twitter for all mentions of the brand name “Volkswagen” during the aforementioned time periods, including retweets. We then conducted topic modeling on the tweets using the text-mining library within the statistical program, excluding words that were obvious, and thus less meaningful in our analysis (“vehicle,” “Volkswagen,” and “car,” among others). We narrowed the number of words down to the five most frequently mentioned on each day. In some cases, when there were multiple words with similar frequencies, we had more than five words per day.

During the first period we studied, the words “new,” “news,” and “cheat” appeared most often across every single day in the window. Over the next few weeks, however, the word “cheat” fell off the list. We interpret this to mean people were focusing less on the “cheating” action of Volkswagen.

As more time went by, specific car models, such as Beetle and Jetta, were mentioned more frequently. We speculate that people tended to view Volkswagen as a whole during initial stages of the brand scandal, but as more information became available, and the company itself attempted to limit the damage stemming from the scandal to specific makes and models, the conversation shifted to a greater focus on specific models that are implicated in the scandal rather than the overall brand.

Other key moments included the German prosecutors launching an investigation into former Volkswagen CEO Martin Winterkorn in early October — “German,” “Germany,” and “Merkel” appeared more frequently on Twitter. This potentially represented a significant shift in the social media conversation as the company, along with regulatory agencies, focused on identifying who may be responsible for the brand scandal.

“CEO,” “January,” “recall,” and “start” were terms that appeared the most, coinciding with an announcement on October 7 that the recall of the affected vehicles that would start in January. This phase marked a turning point as the company initiated recovery efforts and mitigated the impact of the brand scandal on its customers. However, on January 4 the U.S. Department of Justice filed a complaint against the company, which is reflected above in orange.

Sentiment. Although we had a sense of what people were talking about, the tone of the tweets wasn’t immediately clear. We used the Vader Sentiment Analysis software to calculate the sentiment values of each tweet. We counted the daily tweets that showed positive sentiment values, negative sentiment values, and neutral sentiment values, respectively, and derived the percentage of positive tweets, negative tweets, and neutral tweets relative to the total tweets of each day. As a result, we concluded that the daily percentage of negative tweets decreased as time went by. This overall trend is consistent with our previous results from using topic modeling on word-of-mouth communications.

We also calculated the average sentiment values for each day, which showed a rise from negative to positive.

What does this all mean? Here are our takeaways:

The volume of social media conversation tends to attain a high point immediately following a scandal. There are brief spurts in conversation later as regulatory agencies launch investigations, but these do not match the volume that is attained in the early periods.

The valence of social media conversation shifts dramatically, with brand sentiment becoming extremely negative immediately after the scandal incident. Following that, the sentiment shifts as the company initiates recovery efforts (e.g., apology, recall) and regulatory agencies attempt to place responsibility for the scandal on the company itself. These actions make the sentiment itself quite volatile. Ultimately, if the company’s efforts at recovery are successful, the sentiment returns to a neutral state.

The topics that are discussed in social media change during the course of a brand scandal. Initially, there is a great deal of focus on the crisis itself, as conversations focus on the scope of the crisis. Following that, topics revolve around identifying who may be responsible. Different regulatory agencies become involved in the crisis, and their voices become prominent in social media conversations. This is followed by the company initiating recovery efforts, such as issuing apologies, initiating recalls, etc. In this stage, there is an attempt to limit the scope of the crisis incident to specific products within the brand’s portfolio. As the scandal itself dies out, the social media conversation shifts to the broader topic of the brand and its future prospects.

Our analysis of the Volkswagen scandal offers useful insights regarding the management of a crisis incident. By analyzing the topics most frequently discussed, managers can better understand what consumers are discussing and apply appropriate recovery strategies.

One issue with our analysis is that data is not available for a longer time period. In other words, we do not evaluate all the data prior to the start of the recall incident and compare with the events that occurred as the scandal unfolded. A longer time period would help generate deeper insights. We are still in the early stages of text mining and sentiment analysis, but we believe that early findings can help firms optimize the time and costs associated with a brand crisis.

The biggest takeaway is that managers should immediately focus on recovery strategies following an incident, and aim to neutralize the negative sentiment surrounding the brand. In this way, managers can accelerate the shift in conversation from the incident itself and limit the potential damage.