ONLINE FIRESTORM

Although public media often discussing the online firestorm phenomenon, this study can only find a few studies that specifically research this topic. Fortunately, Pfeffer et al. (2014) define online firestorm explicitly: online firestorm is the sudden discharge of large quantities of messages containing negative WOM and complaint behavior against politicians, companies and their brands, government institutions and celebrities in social media networks. Social media users can create huge outrage in reaction to a questionable statement or activity within only a few hours. During an online firestorm, angry emotion is often expressed without targeting any specific criticism; most messages in an online firestorm are personal opinions instead of fact, thereby having highly effective influence.

Following Pfeffer et al. (2014), this study define the difference between NWOM and an online firestorm in the below chart. This study can see that negative WOM can trigger the generation of an online firestorm.

Table 2.6-1. Difference between NWOM and Online firestorm

Definition Difference

NWOM NWOM as a communication way that dissatisfied customer complaint or damage about the product, service or company to others.

Online firestorm as the sudden discharge of large quantities of messages containing negative WOM and complaint behavior against a politicians, company and their brands, government institutions and celebrities in social media networks.

A “phenomenon” characterized by sudden large quantities of NWOM being discussed on social media network. Sudden great amounts of NWOM can lead to an online firestorm.

Pfeffer, et al. (2014) discussed several famous online firestorm cases to illustrate the dangerous of an online firestorm.

Table 2.6-2. Online firestorm cases

Event Online firestorm

On 2012/1/18, McDonald launched an activity to encourage customers aware of the heritage of McDonald food by using

#meetthefarmers on Twitter, but later they changed the hash to #McDStories to encouraged followers shared their story related to McDonalds.

Within only 2 hours, McDonald’s withdrew the activity because many customers shared NWOM and insults on the Internet. Although McDonald’s switched back to #meetthefarmers 2 hours later, the damage had already been done. More than 1000 people had posted negative experiences that happened in McDonalds. This online firestorm received coverage from the traditional media, which led to broader propagation.

In January 2012, a German bank

ING-DiBa made an advertisement show a famous basketball player eating sausage in a butcher’s shop. Vegetarian accused that ING-DiBa promote an unethical (meat) industry on the bank Facebook page.

At the beginning of the protest, comments were posted on the bank’s Facebook page every 5 seconds. At first, ING-DiBa ignored the comments and allowed the heated discussion between vegetarians and meat eaters. The

traditional media instantly propagated the news. Two weeks later, ING-DiBa found the heated discussion had not yet faded, so they announced that from then on, all comments about meat would be deleted in the future. Thereby, the online firestorm finally ended.

At 2011/11/22, a Australian airline Qantas invited customers to attend an activity to win the First Class gift pack, and use the tag of #QantasLuxury.The prize was a pair of pajamas and an amenity kit.

Only 2 hours later, #QantasLuxury was listed in the Twitter Trending Topics list in Australia, with approximately 100-150 tweets every 10 min. Qantas did not consider that a few weeks previously, it had left thousands of passengers stranded at different airports. Therefore, the activity drew many negative comments

attacking Qantas. However, Qantas ignored all the comments and claimed it received a large amount of positive feedback. This behavior led to further propagation and gained Qantas more negative reputation outside Australia.

From the above examples, the most terrible phenomenon this study can observe in an online firestorm is that it can induce intense, huge waves of criticism and complaint without warning to harm the reputation of a company or brand. It could lead the company to lose money. Because to date there are few studies on online firestorms, this study can reasonably assume that companies fear huge volumes of NWOM comments appearing on the Internet and causing negative effects.

This study will discuss later how the online firestorm happened to candidates. It is important for us to recognize clearly the factors causing online firestorms. Pfeffer et al. (2014) summarized seven factors that affect opinion spreading in social media.

This study includes all of them because they bear on this research.

1. Speed and volume

The constant flow of real-time messages in social media causes fast information transportation. Compared to the traditional media need of approximately one day to transport information, an online firestorm can let highly attractive news reach many people within a short time through social media; a company should react in hours or even a few minutes. In an online firestorm, the news will temporally dominate what tremendous numbers of people are talking about, thereby causing huge volumes of communication.

2. Binary choices

Articulate and sophisticated opinions are not what appear on most social media sites.

People who use Social media such as Facebook or Twitter tend to ‘like’ or ‘+1’ the information; were they to want to write more, they would be restricted by technical length limitations of messages. Therefore, there are limits on forming gradual or complete opinions. The absence of comment interaction is an important factor for an online firestorm. Deciding whether to support or oppose the opinions is a binary choice because they are ’either-or situations’ (Schelling, 1973).

3. Network clusters and echo chambers

Interpersonal communication networks have obvious local clustering. Network clustering refers to transporting link creations(Heider, 1946). In other words, if user A is a friend of user B, and user B is a friend of user C, then user A and user C also have a high chance of being friends. The high intensity of connected friends and local clustering will lead to “echo chambers” (Key & Cummings, 1966). These “echo chambers” create a phenomenon in which information is received from different people and directions of their social network; it feels as though everyone is talking about the same topic or has the same opinion(Sunstein, 2001).

4. Unrestrained information flow

Granovetter (1973) mentions the strength of a tie as a combination of time, emotional intensity, intimacy and reciprocal service. In general, weak ties play a bridge role for transporting information within different groups; strong ties play a role in effecting message communication in a social network (Brown & Reingen, 1987). One can be a friend or follower of a large number of people in social media. The intense number of social network neighbors can amplify and echo information and messages during transmission.

5. Lack of diversity and a filter bubble

Pariser (2011) refers to a concept of a filter bubble, which posits that people on social media tend to overemphasize a specific topic. The filter bubble can function in two ways. One way is connection via social media based on homogeny (McPherson et al., 2001); people tend to connect with others with similar characteristics such as age, gender and socioeconomic status, so that they are likely to have similar interests and opinions. The second way is that social media always present information based on previous user experience or information that also interests a user’s friends. Therefore, people become limited and receive filtered information based on their specific preferences.

6. Cross media dynamics

Social media have gradually become an important information source for traditional media (Diakopoulos et al., 2012). Traditional media use social media such as Twitter and Facebook as a type of ‘radar’ to seek new news or information. This phenomenon

also happened in Taiwan; television or new publishers often find a story on the largest bulletin board system, PPT. Traditional media then broadcasting such a story would cause much more online activity. Myers et al., (2012) found that approximately one-third of the information on social media is caused by external events or factors outside the network.

7. Network triggered decision processes

The process of a network-triggered decision has four separate parts. 1) ‘Knowledge’

refers to the moment people encounter the first information, but the filter bubble affects the information people receive. 2) ‘Persuasion’ implies that people receive positive and negative opinions of events, and the echo chamber causes people to feel as though everyone is talking about it. 3) ’Propagation’ indicates that people decide to support or decline the opinion, talk about the decision to others, and allow it to affect their own decision making. 4) ’Affirmation’ suggests that people would confirm their own opinion when meeting another with the same opinion; otherwise, their opinion may destabilize. This step would also be affected by the echo chamber.

Having explained the seven factors of opinion spreading, this study combine all the factors in a network-triggered decision model to illustrate the process of an online firestorm:

Figure 1. The forming process of online firestorm

As this study mentioned, a filter bubble can affect the first time people receive the information. After people know the news, the echo chamber effect would lead them to feel as though everyone is talking about it; therefore, they would seek more information to help them determine what is actually happening. After people in the

‘persuasion’ step receive a great deal of information from others, they would finally make a decision and try to persuade others of their opinion. Unrestrained information factors lead to the amplification effect when transporting information and messages to others. EWOM implies that people express their opinions to others online; therefore, this study can see that some people in “persuasion” steps would be affected by those people who have already made a decision in the “propagation” step. After these people finalize their opinion, some would also spread the information to influence others. Therefore, this study can observe a continuous virtual circle between the

‘persuasion’ and ‘propagation’ steps; this study names this a virtual circle.

An online firestorm is defined as the sudden discharge of large quantities of messages containing negative WOM and complaint behavior against a company in social media networks (Pfeffer, et al.,2014). Therefore, this study can model the situation. Suppose

knowledge

• Cross  media  dynamic  help  the  information  spreading  further.  This   step  affect  by  Eilter  bubble,  so  customer  would  only  attain  limited   information  online.

persuasion

• The  echo  chamber  would  made  user  feels  like  everyone  are  talking   about  the  news  so  they  would  Eind  more  informaiton  to  Eigure  out  the   accident.  Many  decision  makers  are  not  passive,  they  actively  seek   information(Haywood,  1989).

propagation

• In  this  step,  people  will  try  to  express  the  opinions  to  persuade   others,  this  action  would  effect  by  unrestrained  information  Elow  to   effect  other  people  online.

afEirmation

• The  affection  of  echo  chamber  would  still  effect  in  this  step  to  help   users  Eind  others  with  same  opinion  to  afEirm  their  mind.

that person A first encounters information online indicating that company B is responsible for the crisis. The echo chamber effect leads A to want more information to understand the event, but the filter bubble effect causes A to gather more opinions against company B. Gradually, A is affected by many people who feel company B is immoderate; therefore, A decides against company B. Moreover, A tries to spread negative information about company B to persuade others. According to unrestrained information flow factors, A can be a friend or follower of tremendous numbers of people in social media. Therefore, the negative information from A potentially can spread to many people. Those people affected by A will enter the virtual circle and later affect other people. Because of the fast transportation speed and great information volume characteristics of social media, many people engage in the virtual circle within a short time, finally resulting in an online firestorm.

Figure 2. The forecast model of online firestorm

As the model shows, this study can see information repeatedly spread by people on the Internet affecting many people’s potential decisions. Therefore, this study wants to determine what a company can do to manage online WOM or how to address the crisis after encountering an online firestorm.