CHAPTER 3: METHODOLOGY
3.2 DATA COLLECTION
The data were primary selected from electronic news and celebrity Fan pages or websites. Most of the news can be accessed online, and most electronic news provides a forum for people to discuss. Therefore, through searching the news online, this study can obtain not only news of the candidates but also opinions from the viewers.
In addition, some candidates do not only respond to online firestorms through the public media; some have their own fan pages or websites to respond to the public.
Therefore, this can be one data resource.
2. Online firestorm events
This research focuses on the online firestorm events that happened during a nine-in-one political election launched in Taiwan; therefore, this study must collect data on the online firestorm events. However, this study initially wants to ensure that the event with NWOM actually triggered the online firestorm. Therefore, this study needs a measurement to help us determine which events are actually online firestorms.
For such a measurement, this study must clearly examine which factors cause an online firestorm. In a review, Pfeffer et al. (2014) summarized seven factors that cause opinions to spread: speed and volume, binary choices, network clusters and echo chambers, unrestrained information flow, lack of diversity, cross-media dynamics and network-triggered decision processes. Because ‘binary choices’ is a design characteristic of social platforms, it is not a helpful factor for our measurement.
In addition, ‘Network clusters and echo chambers’, ‘unrestrained information flow’,
‘lack of diversity’ and ‘network-triggered decision processes’ phenomena are seen only in people’s social platforms, they are also difficult to measure. Therefore, this study decided to use ‘cross-media dynamics’ and ‘speed and volume’ to design our measurement.
After experiencing the online firestorm event, if there were dissatisfied people spreading NWOM online, then an increasing number of people would encounter the information. As the network-triggered decision processes this study discussed occurred, people who encountered the news would want to ascertain what really happened and make up their mind. Many decision makers are not passive; they actively seek information (Haywood, 1989), and one of their primary information resources is the Internet. According to our research, INSIGHTXPLORER (2014) and Global Views Monthly (2013) both indicate that the primary Internet behavior in Taiwan is searching for information or news. Therefore, this study can believe that an event keyword being searched for many times provides the important characteristics
‘speed and volume’; thus, this study can consider this event the trigger of the online firestorm.
To determine the searching popularity of the specific keywords, this study use Google Trend to see the situation about how specific keywords be searched during the specific timespan and period. Google Trends is a service provided by Google to observe the volume of searches and news articles handled by their search engines.
Choi and Varian (2012) noted that the information Google presents is not the real searching volume; it is a relative number that provides a comparison with the other days a keyword has been searched. Google uses the numbers 1 to 100 to present keyword search volumes; the day that was searched the most frequently in a particular timespan is assigned a value of 100.
Based on the social sharing literature, this study know that people tend to share information shortly after an event happens—over 50% even on the same day (Rimé et al., 1992). Therefore, this study assumes that an online firestorm will form within no more than least five days. In addition, there are many news stories during the political election; therefore, to avoid news overlap altogether, five days is suitable for the measurement. This study analyzes Google Trend of the online firestorm cases mentioned in table 2.6-2. to make our measurements.
Table 3.2-1. Google Trend analyze on table 2.6-2. online firestorm cases
Event Keyword Average Google
Trend values five
ING-DiBa 53.6 25,30,100,67,46
2011/11/21
Qantas Twitter event
Qantas 74.6 82, 79,76,71,65
From the table, this study can see that the lowest average Google Trend value of the cases is 49.6, and the lowest Google Trend value happened during an online firestorm is 60. Therefore, this study wants to make a measurement to measure the online firestorm:
1. The Google Trend value of the keyword five days after the events should have at least one day up to 60.
2. The average Google Trend values five days after events should be at least 49.
This study assumes that if the event satisfies one of the conditions, then it can be considered an online firestorm. Therefore, this study selected some famous NWOM events that happened during the nine-in-one political election to check whether the NWOM triggered an online firestorm. The search area was set to Taiwan, and the search time limit was set to include the month of the event.
Table 3.2-2. Google Trend analyze of NWOM cases in Taiwan political election
Events Date of
The Taipei candidate, 2014/9/6 Ko 37.2 25,29,60,46,26
Wen-Je Ko (Ko), who was previously a doctor, was accused of a tendency toward sexism.
Ko was accused of money laundering (MG149 so the behavior is illegal.
2014/10/
12
Ko 58.6 59,50,62,60,62
Ko said that rejecting company “Ting-Shin” was similar to witch burning in medieval times. The witch-burning choice of words was attacked as being a poor analogy that did not express people’s
2014/7/15 Lien 35.8 28,54,38,37,22
prosperous. accused of not being able to understand living with hardship.
2014/8/7 Lien 34.2 34,46,40,28,23
Lien posted a video asking that if we were a rich man, what would we want to do?
The video mentioned that some people would want to have fun every day, and mentioned if we were as rich as Lien we would not want to run for Taipei mayor. However, this video caused him to be accused of not understanding citizens at all.
2014/9/11 Lien 57.8 63,74,45,55,52
Lien performed a working activity in the night-market.
He was accused of showing off.
2014/9/18 Lien 76.4 69,66,87,62,98
Lien said that Buddha was a prince before, but he chose to liberate all sentient beings. This statement resulted in him being
2014/11/2 Lien 23.8 22,41,19,18,19
accused of being shameless by describing himself as Buddha.
Lien was accused of eavesdrop on Ko’s office.
2014/11/5 Lien 58.2 24,23,97,89,58
Lien’s father criticize Ko’s firestorms. In the next section, this study will focus on the events that conformed to the online firestorm measurement.
3.3 Data analysis
1. Image restoration strategies
This study would use the image restoration strategies from Benoit (1997) to analyze the respond strategy of candidates.
Table 3.3-1. The categorizes of Image restoration strategies
Strategy Variant
Mortification None
2. Using Opview insight clowd service to collect the online comments
Opview social WOM database service launched by eLand technologies corporate from Taiwan, this software can collect the comments from the several social platforms including Facebook, Plurk, news, blogs, BBS, and analysis the comments or news to judge the EWOM is positive or negative. This software can help us analyze the negative and positive WOM patterns of specific key word everyday, and show the data in graphic form. This software service has been used by several public media in Taiwan (Opview, 2013), therefore this study can believe the credibility.
3. Using Opview data to the formula to analyze the online firestorm
Rimé et al.(1992) mentioned that over 50% people tend to share information shortly after an event happens, some even spread the news on the same day. Since there are many news stories during the political election, in order to avoid news overlap altogether, five days is suitable for the measurement. Therefore, this study assumes that an online firestorm will form within no more than least five days.
This study assumes that some online firestorm cases need a “boiling period” to cause the online firestorm; later, events come to a peak that this study calls the “explode period”. After the “explode period”, events gradually receive little interest from the public; this study called this time the “faded period”. However, some online firestorm cases are too intense; they skipped the “boiling period” and jumped directly to the
“explode period”. This study develops a formula to describe the developing pattern of an online firestorm.
Given specific keyword, Opview would collect amounts of NWOM and PWOM from several social platforms and calculate the quantity. This study use “Wen-Je Ko” as a keyword in the cases of Ko; and use ”Sheng-Wen Lien” as a keyword in the cases of Lien. This study assumes that NWOM amounts of the dayi is di, assume i is a positive integer. The average NWOM comments value of the first five days after events is A .
A = d1+ d2+ d3+ d4+ d5 5
If d1< A , then day one is “boiling period”.
If di< A and di−1to d1< A, then i is “boiling period”.
If di ≥ A,
then i is “explode period”.
Otherwise, i is ”faded period”.
The effectiveness of responses would be judged by the exploded days, this study assumes that an effective response strategy can help the events exploded for only several days and faded soon. However, the inappropriate response would deteriorate the situation and exploded for long time, causing the online firestorm be more severe and harm the brand of candidate.
Chapter 4: Data Analysis
Online political election firestorms can result from three factors: fault, defect, and counterforce slander. To conduct the analysis, this study thus select six cases, which are presented in table 3.2-2, that correspond to the online firestorm standard. These six cases include: Ko was accused of a tendency toward sexism, Ko said that rejecting company “Ting-Shin” was similar to witch burning and Ko was accused of involving in human organs trading; Lien’s father criticized Ko’s grandfather is a japanization person, Lien donated one hundred thousand NT dollars to Kaohsiung gas explosion and Lien was accused of eavesdrop on Ko’s office. This study separates these six cases in three situations: fault, defect and counterforce slander. Faults occur when candidates lie, break the law or do something that society deems unacceptable.
Defects occur when candidates do or say something inappropriate and offend the public, and such situations are shaped by candidate personalities and opinions.
Counterforce slander is defined as accusatory remarks from a competitor. In this section, this study describes online firestorm events, candidate responses and use a formula to show how long each event exploded to determine which responses are most effective. In addition, this study also examines the cases to identify which features differ from online conventional business firestorms.
4.1. Fault
In this part, this study selected two cases both candidates say something society unacceptable to see which responses is more effective in this situation.
4.1.1. Ko was accused of a tendency toward sexism
On 6 September 2014, Ko stated that a female Jiayi candidate, Chen Yie-Chen, was beautiful enough to be a receptionist but not qualified to be a mayor. The next day, 7 September, during a speech, Ko mentioned that he did not want to be an obstetrician because these professionals only see “one hole.” The speech appalled numerous people, and he was criticized for disrespecting women and thus for being unqualified to be mayor (Liu, K. Y, 2014; Appendix 1-1). However, supporters argued that Ko was not ill-intentioned and that people must have a better sense of humor. During a major online firestorm, Ko made announcements to the public.
l On 9/7 Ko interviewed by the media clarified that his speaking want to emphasize the important characteristic to be a mayor is good ability not beautiful looks. He admitted that did not convey his thinking correctly, he needs to do self-reflection (Tang, S., 2014).
s Admitted that did not convey his thinking correctly àEvasion of responsibility: good intention
s He needs to do self-reflectionàCorrective action
l On 9/8 Ko post on FB admit that he made a mistake and will improve, emphasized most of his colleague are female, and the way he interactive with his wife can tell he never had sexism tendency (Appendix 1-2).
s Emphasized most of his colleague are female, and the way he interactive with his wife àReduce offensiveness: Bolstering
s Admit he made a mistake and will improveàCorrective action s He never had sexism tendency àDenial: Fully deny
l On 9/8 Ko’s wife, Chen Pei-Chi, also made a post on Facebook mentioned that Ko’s image be played up by somebody, and emphasized Ko is a person who work seriously and only have lowest requirements of life quality (Appendix 1-3).
s Ko’s image be played up by somebodyà Reduce offensiveness: Attack accusers
s Emphasized Ko is a person who work seriously and only have lowest requirements of life quality à Reduce offensiveness: Bolstering
Table 4.1.1-1. Response strategy of sexism tendency online firestorm
Time Image restoration discourse Sponsor
9/6 No No
9/7 s Evasion of responsibility: good intention s Corrective action
Ko
9/8 s Reduce offensiveness: Bolstering s Corrective action
s Denial: Fully deny
Ko
9/8 s Reduce offensiveness: Attack accusers Ko’wife
s Reduce offensiveness: Bolstering
Following this study use Opview insight software to see how many NWOM raised up after the event. In this case, this study uses “Wen-Je Ko” as the keyword to collect data and do the analysis. This study will mark the day when candidates respond to the events by yellow dot (Figure 3), and draw the yellow dotted line and yellow shadow to present the explode period (Figure 4).
Figure 3. The response time and NWOM pattern of sexism tendency online firestorm Data source: Opview insight software data
Following this study use the Opview insight data to the formula this study mentioned in chapter three to find out how many time the online firestorm needed to boil up and explode, and when it faded is related to the candidate’s response.
Opview Insight data : A= (348+788+672+772+750)/5=666
Table 4.1.1-2. NWOM amounts of sexism tendency online firestorm
Date 9/6 9/7 9/8 9/9 9/10 9/11 9/12 9/13
NWOM amounts
348 788 672 772 750 905 894 700
Date 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21
NWOM amounts
692 884 701 777 1228 1020 861 743
0 200 400 600 800 1000 1200 1400
amount of comments
NWOM PWOM
Date 9/22 9/23 NWOM
amounts
699 617
The green part is boiling period; the orange part is exploding period; the blue part is the day start faded.
Data source: Opview insight software data.
Table 4.1.1-3. Boiling period and explode period of sexism tendency online firestorm
Figure 4. The explode period of sexism tendency online firestorm
Following this study summarize the online firestorm characteristic of this case in table 4.1.1-4.
Table 4.1.1-4. Online firestorm characteristic of sexism tendency online firestorm Online firestorm Target s Ko’s behavior: unsuitable speaking.
Cause s Fault: tendency toward sexism is society unacceptable.
Consumer Reaction
s Complaint: criticized Ko did not respect to women at all.
0 200 400 600 800 1000 1200 1400
amount of comments
NWOM PWOM
Period Boiling period Explode period
Days 1 day 16 days
s Damage: criticized Ko unqualified to be mayor.
s Malicious abuse: criticized Ko with malicious and irrational words.
Initiatives s Launched by registered and unregistered voters Effects s People refuse to support
s People would vote for others
s Some supporters remain(PWOM line in figure 3)
4.1.2. Lien’s father criticized Ko’s grandfather is a japanization person
Japanese troops occupied Taiwan from 1895 to 1945. During the Japanese occupation, the Japanese forced the Taiwanese change their names to Japanese names, to speak Japanese and to learn about Japanese culture. On 16 November 2014, Lien’s father, Lien-Jane, stated that Ko’s family was faithful to Japan during the Japanese occupation and was not patriotic. Lien-Jane also criticized Ko’s grandfather for previously changing his name to a Japanese name and cursed at Ko (Ling, U. Y., 2014). Because the Taiwanese were not willingly occupied by the Japanese, the speech offended the Taiwanese public. The speech roused anger among the public, and it was argued that Lien-Jane intended to divide the Taiwanese (Appendix 2).
Lien-Jane finally responded to the firestorm on 11/21.
l On 11/21, Lien-Jane apologized that he should not say swear words, and he emphasized that he did not pick on any specific individual, the swear word was used to describe the political election. However, he did not reply to the most severe issue about Japanese people (Guo, A, J., 2014).
s Apologized that he should not say swear wordsà Mortification s He did not pick on any specific individualàReduce offensiveness:
Minimize
s Did not reply to the most severe issue about Japanese peopleà There are no correspond response in image restoration discourse. Therefore this study define it as “ignore”.
l On 11/22, Lien mentioned that his father did not tell him what he would say on 11/16, and he respected his father’s apology (Wang, D. J.; Ling. S.T.).
s His father did not tell him what he would sayà Evasion of responsibility:
Defeasibility.
Table 4.1.2-1. Response strategy of Japanization person online firestorm
Time Image restoration discourse Sponsor
11/16 No No
11/21 s Mortification
s Reduce offensiveness: Minimize s Ignore
Lien’s father
11/22 s Evasion of responsibility: Defeasibility. Lien
In this case, this study uses both “Sheng-Wen Lien” and ”Lien-Jane” as the keyword to collect data and do the analysis.
Figure 5. The response time and NWOM pattern of Japanization person online firestorm
Data source: Opview insight software data
Opview Insight data(Lien) : A= (3712+6358+4416+4115+3567)/5= 4433.6
Opview Insight data(Lien’s father) : A= (1737+7152+2728+1212+1055)/5=2776.8
0 1000 2000 3000 4000 5000 6000 7000 8000
amoun of comments
NWOM PWOM
NWOM of Lien's father
Table 4.1.2-2. NWOM amounts of Japanization person online firestorm
Date 11/16 11/17 11/18
NWOM amounts(Lien) 3712 6358 4416
NWOM amounts(Lien’s father) 1737 7152 2728 Data source: Opview insight software data
In this case, both of Lien and his father’s online firestorm only boiled one day and exploded one day.
Table 4.1.2-3. Boiling period and explode period of Japanization person online firestorm
Figure 6. The explode period of Japanization person online firestorm
Table 4.1.2-4. Online firestorm characteristic of Japanization person online firestorm Online firestorm Target s Lien’s father behavior: unsuitable speaking.
s Lien’s image: Lien involved in the online
firestorm that he be considered should responsible for, though he did not do the offensive behavior.
0 1000 2000 3000 4000 5000 6000 7000 8000
amoun of comments
NWOM PWOM
NWOM of Lien's father
Period Boiling period Explode period
Days 1 day 1 days
Cause s Fault: accusing Taiwanese is a Japanization people is society unacceptable.
Consumer Reaction
s Complaint: criticized Lien’s family offended the Taiwanese public, they should apologize.
s Damage: criticized Lien’s family intended to divide the Taiwanese.
s Malicious abuse: criticized Lien with malicious and irrational words.
Initiatives s Launched by registered and unregistered voters Effects s People refuse to support
s People would vote for others
s Some supporters remain (PWOM line in figure 5)
4.1.3. Case analysis
In these two cases, both of them are doing or speaking some society unacceptable thing to induce the online firestorm: Ko’s unsuitable speaking to the women and Lien-Jane’s speaking about japanization person both appealed great criticism in a short time. This study will compare the explode time and respond strategy to decide which responding is better for the fault situation.
Table 4.1.3. Fault cases analysis
People Image restoration discourse Boiling period
Lien’s
Lien Evasion of responsibility:
Defeasibility
(1) The background of the cases
Taiwan is a country pursue for the gender equality, therefore Ko’s speaking caused lots of criticism is not surprising at all. His speaking attacks most of the female and some male, and caused sisterhood and some female member of parliament to against him. Though there still have loyal supporters to support him, but the online firestorm did not faded by their support.
Japanese occupation is the misery history memory for Taiwanese, Lien’s father use this to attack Ko is not fair to most Taiwanese. His speaking not only attacked most of the Taiwanese but also involve Lien into online firestorm. The speaking induces the attack form public, professors, politician and even the ex-president.
(2) The analysis of responding strategy
(2) The analysis of responding strategy