An Analysis of the Tweets Posted After the Crash of Germanwings Flight 9525

Faculteit Letteren & Wijsbegeerte

Sarah Messiaen

Automatic detection of crisis situations on social media: an analysis of the tweets posted after the crash of Germanwings Flight 9525

Masterproef voorgedragen tot het behalen van de graad van

Master in de Meertalige Communicatie

2015

Promotor Prof. Dr. Véronique Hoste Vakgroep Vertalen Tolken Communicatie

PREFACE

First and foremost, I would like to show my sincerest gratitude to my supervisor Prof. dr. Véronique Hoste, who has guided me from the initial to the final level of this master’s dissertation. She was a valuable help to me and she always took her time to meet and review nearly anything. Furthermore, I thank Cynthia Van Hee for helping me to perform all experiments needed to complete this paper’s study. Third, I would like to thank dr. Klaar Vanopstal for correcting some parts of this master’s dissertation.

I would also like to thank my boyfriend and my friends for their moral support. And finally, I owe my deepest gratitude to my parents for supporting my throughout my studies over the past few years. 3

TABLE OF CONTENTS

LIST OF GRAPHS AND TABLES ...... 5

ABSTRACT ...... 7

1. INTRODUCTION ...... 7

2. THEORETICAL FRAMEWORK ...... 9

2.1 CRISIS COMMUNICATION ...... 9 2.1.1 Crisis defined ...... 9

2.1.2 The three-stage approach to crisis management ...... 9

2.1.2.1 Pre crisis ……………………………………………………………...10

2.1.2.2 Crisis event …………………………………………………………...10

2.1.2.3 Post crisis ……………………………………………………………..10

2.1.3 Stealing thunder ...... 11

2.1.4 Coombs’ Situational Crisis Communication Theory ...... 11

2.1.4.1 SCCT's attribution theory roots ………………………………………12

2.1.4.2 Factors that shape the reputational threat …………………………….13

2.1.4.3 Crisis types …………………………………………………………...14

2.1.4.4 Crisis response strategies ……………………………………………..15

2.1.5 Emotions in crisis communication ...... 17

2.2 SOCIAL MEDIA ...... 18 2.2.1 Social media defined ...... 18

2.2.2 The role of social media in organizational crisis communication ...... 19

2.2.3 The use of Twitter in organizational crisis communication ...... 20

2.2.3.1 The use of Twitter by corporations …………………………………..20

2.2.3.2 The use of Twitter by citizens ………………………………………..21

2.3 AUTOMATIC CLASSIFICATION OF SENTIMENT AND EMOTION IN CRISIS RELATED MICROPOSTS ...... 22 2.3.1 Automatic classification of sentiment ...... 22

2.3.2 Automatic classification of emotion ...... 23

3. RESEARCH METHODOLOGY ...... 25

3.1 EVENT OF STUDY ...... 25 3.2 DATA COLLECTION ...... 26 3.3 METHOD ...... 27 3.3.1 Sentiment detection ...... 27

3.3.2 Emotion detection ...... 28

4. RESULTS AND DISCUSSION ...... 29

4.1 MOST FREQUENTLY USED WORDS AND ANALYSIS OF THE ORGANISATION’S TWEETS ...... 29 4.2 SENTIMENT CLASSIFICATION ...... 32 4.2.1 Quantitative discussion of English results ...... 33

4.2.2 Qualitative discussion of English results ...... 34

4.2.3 Quantitative discussion of Dutch results ...... 36

4.2.4 Qualitative discussion of Dutch results ...... 37

4.3 EMOTION CLASSIFICATION ...... 40 4.3.1 Quantitative discussion of English results ...... 40

4.3.2 Quantitative discussion of Dutch results ...... 42

4.3.3 Qualitative discussion ...... 43

5. CONCLUSION ...... 45

6. BIBLIOGRAPHIC REFERENCES ...... 47

7. APPENDIX ...... 50

7.1 TWEETS POSTED BY THE ORGANIZATION ...... 50 7.1.1 Tweets posted by Germanwings ...... 50

7.1.2 Tweets posted by Lufthansa ...... 51

7.2 CORPUS OF 200 ENGLISH TWEETS ...... 52 7.3 CORPUS OF 200 DUTCH TWEETS ...... 62

LIST OF GRAPHS AND TABLES

GRAPHS

Graph 1: Word Cloud English tweets March 24-25 p. 29

Graph 2: Word Cloud English tweets March 26-31 p. 30

Graph 3: Word Cloud English tweets April 1-6 p. 30

Graph 4: Word Cloud Dutch tweets March 24-25 p. 31

Graph 5: Word Cloud Dutch March 26 – April 6 p. 31

TABLES

Table 1: Crisis types definitions p. 15

Table 2: SCCT crisis response strategies p. 16

Table 3: Social media categories p. 18

Table 4: Class distribution of the English corpus p. 33

Table 5: System accuracy based on 200 gold annotations p. 33

Table 6: Class distribution based on 200 gold annotations p. 34

Table 7: Tweets that received the same label both manually and automatically p. 34

Table 8: Mistake type 1 p. 35

Table 9: Mistake type 2 p. 35

Table 10: Mistake type 3 p. 35

Table 11: Mistake type 4 p. 36

Table 12: Class distribution of the Dutch corpus p. 36

Table 13: System accuracy based on 200 gold annotations p. 37

Table 14: Class distribution based on 200 gold annotations p. 37

Table 15: Tweets that received the same label both manually and automatically p. 38

Table 16: Mistake type 1 p. 38

Table 17: Mistake type 2 p. 39 6

Table 18: Mistake type 3 p. 39

Table 19: Mistake type 4 p. 39

Table 20: Occurrence emotion classes in English gold standard corpus p. 41

Table 21: Examples of English tweets expressing emotion p. 41

Table 22: Occurrence emotion classes in Dutch gold standard corpus p. 42

Table 23: Examples of Dutch tweets expressing emotion p. 43

Table 24: Elements that could help to determine emotional content p. 44

ABSTRACT

Social media, and in particular Twitter, are increasingly being utilized during crises. It has been shown that tweets offer valuable real-time information for decision-making. However, because of the vast amount of information, this information is not directly usable. Applying sentiment and emotion analysis is one option for distinguishing important information from unimportant information. In this study, we investigate the feasibility of automatically detecting crisis-related emotions on Twitter. Therefore, two corpora of respectively English and Dutch tweets posted after the crash of Germanwings Flight 9525 were built. For each corpus, 200 tweets were manually and automatically labelled as positive, negative or neutral. Moreover, we manually tagged the emotional content of these tweets. We found that the system used for the sentiment analysis of the English tweets performed better than the system used for the sentiment analysis of the Dutch tweets. Both systems performed well with regard to the negative class label, which is important in crisis situations. Furthermore, sympathy and anger were the most recurring emotions in the English gold standard corpus, whereas sadness and contempt were the most frequently expressed emotions in the Dutch gold standard corpus. It can be concluded that automatic systems can help to enable the automatic detection of emotions: first through sentiment analysis and then through a more fine-grained detection of emotions.

1. INTRODUCTION

The use of social media tools has increased over the past few years. As a consequence, the ways in which publics communicate during crisis situations have changed. Especially the microblogging service Twitter has become a very popular web application for seeking and defusing crisis-related information. However, this potentially valuable information often remains unused since it is unfeasible to manually analyze the sheer amount of information. One option to distinguish important information from unimportant information is applying sentiment analysis. However, determining whether a tweet is positive, negative or neutral is rather difficult since tweets are unstructured and often contain colloquial language. Within the crisis response and crisis management domain, the classification of vast amounts of tweets using machine learning algorithms has become a popular topic (Schulz et al., 2013; Taboada et al., 2011; Van Hee et al., 2014). Research to understand the emotional content in crisis- related tweets, on the other hand, has received much less attention. Yet, Schulz et al. (2013, p. 847) argue that differentiating emotions is very important, since it allows a better understanding of the situation. Whereas sentiment analysis classifies tweets into three classes (positive, negative or neutral), emotion analysis is a multiclass classification problem. As a consequence, emotion analysis is even more challenging than sentiment analysis (Brynielsson, Johansson, & Westling, 2013, p. 34). The emotional content of tweets is often 8 classified into emotion classes based on Ekman’s six basic emotions: anger, fear, sadness, enjoyment, disgust, and surprise (Ekman, 1992).

The aim of this study is to investigate the feasibility of automatically detecting crisis-related emotions on Twitter. Therefore, two corpora of crisis-related tweets posted during the two weeks after the crash of Germanwings Flight 9525 were created. The first corpus consists of 5490 English tweets containing hashtag ‘#GermanWingsCrash’ and the second corpus consists of 722 Dutch tweets containing hashtag ‘#GermanWingsCrash’. For each corpus, 200 tweets were manually and automatically labelled as positive, negative or neutral. Furthermore, the emotional content of the 200 English tweets and the 200 Dutch tweets was manually identified using the crisis emotion scale of Jin et al. (2014).

In addition, this master’s thesis will try to find an answer to the following questions: What are the most frequently used words in the tweets and do the moments on which Germanwings or Lufthansa communicated have an impact on the contents of the tweets? Are there differences between the system’s predictions and the gold standard annotations for sentiment and what did the system do right or wrong? And finally: What emotions did the tweets express and what elements occurring in the tweets in our corpus could help to automatically detect the emotional content of these tweets.

It was found that the system used for the sentiment analysis of the English tweets performed better than the system used for the sentiment analysis of the Dutch tweets. Both systems performed well with regard to the negative class label, which is a significant advantage in crisis situations. Furthermore, we found that in the 200 English tweets, sympathy was the most frequently expressed emotion, and in the 200 Dutch tweets, sadness was the most frequently conveyed emotion. In addition, we discovered that content words, hashtags, exclamation marks, question marks, capital letters, and emoticons are important elements for the automatic detection of emotions in crisis-related tweets.

This paper is organized as follows: chapter two provides the theoretical framework of this paper, in which crisis communication, social media and the automatic classification of emotion and sentiment in crisis-related tweets are elaborated upon. Chapter three covers the event of study, the data collection, and the method of this paper’s study. In chapter four, the results of this paper’s study are analyzed and discussed. And finally, chapter five will handle the conclusion. 9

2. THEORETICAL FRAMEWORK

2.1 CRISIS COMMUNICATION

2.1.1 Crisis defined

An organizational crisis is “the perception of an unpredictable event that threatens important expectancies of stakeholders and can seriously impact an organization’s performance and generate negative outcomes” (Coombs, 2012, p. 2). An event is partially defined as a crisis by the perceptions of stakeholders (Coombs, 2012, p. 2). Bryson (2004, p. 22) defines a stakeholder as a person or a group that is influenced by or has an influence on an organization. If stakeholders think an organization is in crisis, there is a crisis, and they will treat the organization as if it is in crisis (Coombs, 2012, p. 2).

Crises interfere with some stakeholder expectancies, which results in people becoming angry and upset. As a consequence, the organization is perceived less positively and its reputation is damaged. In addition, it is important to be conscious of the fact that there is a difference between an incident and a crisis. Whereas an incident is a minor disruption, a crisis affects the organization as a whole. The following example explains the difference between an incident and a crisis: imagine a water valve breaks and sprays water in de vending area of a factory. As a result, vending machines are out of action for a day. Since the replacement of the valve did not harm the larger organizational routine, it is an incident. However, if the factory has to be shut down due to the broken water valve, it becomes a crisis because the entire organization is disrupted. Moreover, crises can create negative or undesirable outcomes for organizations, their stakeholders, and their industries, such as financial loss, injuries or deaths to stakeholders, structural or property damage, reputational damage, and environmental harm (Coombs, 2012, pp. 3-4).

2.1.2 The three-stage approach to crisis management

The idea that crises have an identifiable life cycle is a theme that often returns in the crisis management literature. A crisis consists of different phases and each phase requires a different action. Furthermore, crisis management is more than merely developing a plan and executing it during a crisis. It is in fact an ongoing process that should be a part of many organization members’ everyday tasks (Coombs, 2012, pp. 6-7). 10

Coombs (2012, pp. 10-11) decided to use the three-stage approach as the framework for his book due to its ability to subsume other models used in crisis management. The three stages of Coomb’s approach to crisis management are discussed in the following overview.

2.1.2.1 Pre-crisis

The pre-crisis stage implies actions that should be performed before a crisis is encountered. However, as some crises cannot be prevented, organizations must prepare for crises as well. The pre-crisis stage can be divided into three substages: signal detection, prevention, and crisis preparation.

As most crises emit early warning signs, many of them can often be anticipated. Therefore it is important for crisis managers to develop a system for detecting signals of potential crises so that corrective action can be taken in order to prevent the crisis. In addition, an organization must be prepared for a crisis happening. Preparation consists of creating crisis teams, selecting spokespersons, compiling a list of the most likely crisis to befall an organization, etc (Coombs, 2012, pp. 11-12).

2.1.2.2 Crisis event

The crisis stage commences with a trigger event and terminates when the crisis is considered to be solved. This stage can be divided into two substages: crisis recognition and crisis containment. Members of an organization must realize that their organization is in a crisis. Crisis containment, on the other hand, involves the organization’s crisis response. Communicating with stakeholders is a critical dimension of this phase (Coombs, 2012, p. 12).

2.1.2.3 Post crisis

When a crisis has come to an end, it is essential that the crisis team does not think that its work is completed. First, crisis managers should evaluate their efforts. By learning what the organization did right or wrong during the crisis, the crisis management process can be improved (Coombs, 2012, p. 169). As a consequence, the organization will be better prepared for future crises (Coombs, 2012, p. 12). Second, the crisis team should keep monitoring the 11 crisis after it is resolved, which might involve conducting further research or providing necessary updated information to stakeholders (Coombs, 2012, p. 169).

2.1.3 Stealing thunder

When an organization is confronted with a crisis, it has several options to release information. These options entail the content of the crisis message as well as the timing of the information release. Crisis management researchers seem to agree about the fact that organizations should supply accurate information to stakeholders as quickly as possible. However, many factors might hamper the decision to release information quickly, such as the need to investigate the situation, and the need to generate a unified organizational message (Arpan & Roskos- Ewoldsen, 2005, pp. 425-426).

Arpan & Roskos-Ewoldsen (2005, p. 426) define stealing thunder as “the fastest and most proactive approach to crisis communication: the organization does not respond quickly to media inquiries concerning a crisis, rather, the organization initiates the crisis communication”. The researchers conducted a study in order to examine the effects of such rapid self-disclosure during an organizational crisis. Two mock newspaper articles were written that included information about a new preservative in Pepsi that seemed to be making consumers sick. In one article, Pepsi stole thunder, and in the other article, the Centers for Disease Control disclosed the information. It was found that stealing thunder can have a positive effect on how stakeholders evaluate the organization and the crisis. Organizations that stole thunder were attributed more positive credibility ratings. Moreover, the enhanced credibility ratings due to stealing thunder resulted in perceptions of the crisis as less severe. Furthermore, it was found that more involvement with the organization or the product, weaker perceptions of crisis severity, as well as higher credibility ratings lead to greater intent to purchase the product in the near future (Arpan & Roskos-Ewoldsen, 2005, pp. 427-431).

2.1.4 Coombs’ Situational Crisis Communication Theory

It is critical for organizations and public relations practitioners working in the field of crisis communication to have knowledge about how to shape the appropriate strategies in response to crises. Coombs’ Situational Crisis Communication Theory (SCCT) is a dominant theory on 12 crisis response strategies. It takes an audience-centred approach in order to understand stakeholders’ reactions in crisis situations by examining their attribution of crisis responsibility and their assessment of crisis types (Jin, 2010, pp. 522-523). Moreover, SCCT offers a set of guidelines for how crisis response strategies can be used in order to protect the reputation held by stakeholders from the damage of a crisis (Coombs, 2007, p. 163).

Crises threaten to damage organizational reputations since a crisis often results in people thinking badly of an organization. In that case, the advantages of a favourable reputation, such as attracting customers, improving financial performance, etc, may be lost. Over time, organizations collect reputational capital. As a result of a crisis, reputational capital is lost. A favourable pre-crisis reputation, however, acts as a buffer against the loss of reputational capital during a crisis. Organizations that have the advantage of a more favourable prior reputation will still have a stronger reputation after the crisis since they have a larger amount of reputational capital to spend than organizations with an unfavourable or neutral pre-crisis reputation. Therefore, organizations with a favourable prior reputation suffer less and rebound more quickly. Still, crisis managers must keep in mind that the first priority in a crisis is protecting stakeholders from harm, not protecting the reputation (Coombs, 2007, pp. 164- 165).

2.1.4.1 SCCT’s attribution theory roots

Attribution theory posits that people will make judgements about the causes of events, especially those that are unexpected and generate negative outcomes (Coombs, 2004, p. 267). He states that “people will attribute the cause of an event to an individual involved in the event (personal causality) or to some outside force (external causality)”. Since crises are unforeseen and negative, they are just the type of event that will produce attributions. If stakeholders think an organization should have been able to control a crisis, they will blame the organization for the crisis. Furthermore, greater attributions of responsibility result in stronger feelings of anger and more negative visions on people and organizations. However, crisis teams can utilize crisis response strategies in order to shape attributions of the crisis as well as perceptions of the organization itself. Attributions of responsibility are used in the Situational Crisis Communication Theory in order to establish a link between crisis response strategies and the crisis situation (Coombs, 2004, pp. 267-268). 13

2.1.4.2 Factors that shape the reputational threat

SCCT argues that understanding a crisis situation helps the crisis manager to choose the crisis response strategy or strategies that will maximize reputational protection. If no action is taken, a crisis will inflict harm on the reputation of the organization. There are three factors in a crisis situation that shape this reputational threat: initial crisis responsibility, crisis history and prior relational reputation (Coombs, 2007, p. 166).

Initial crisis responsibility, which is the centrepiece of SCCT, is the degree to which stakeholders believe an organization is responsible for a crisis. There is a direct relationship between attributions of crisis responsibility and the reputational threat posed by a crisis: if attributions of crisis responsibility to an organization intensify, a crisis becomes a greater threat to the organization’s reputation. In that case, crisis teams should apply strategies that indicate that they accept responsibility for the crisis and simultaneously give prove of concern for victims. When crisis managers evaluate the crisis threat, they also identify the crisis type (Coombs, 2004, pp. 268-269), which will be elaborated upon in section 2.1.4.3.

Crisis history refers to the fact whether or not an organization has had a similar crisis in the past (Coombs, 2007, p. 167). The fact that an organization has been confronted with one or more crises may indicate that the present crisis is part of a pattern rather than an isolated event. Hence, a history of past crises could result in stronger attributions of organizational crisis responsibility (Coombs, 2004, p. 272).

Prior relational reputation refers to how well or poorly an organization is considered to have treated stakeholders in other contexts. An unfavourable prior relational reputation implies that an organization shows little consideration for stakeholders across a number of fields, not just in the current crisis. Both crisis history and prior relational reputation have a direct and indirect effect on the reputational threat produced by the crisis (Coombs, 2007, p. 167). The direct effect posits that the two factors result in a greater reputational threat due to lowering perceptions of the organization’s reputation. Moreover, either a history of crises or an unfavourable prior relational reputation reinforces attributions of crisis responsibility, thereby indirectly affecting the organizational reputation (Coombs, 2004, p. 273).

2.1.4.3 Crisis types

SCCT distinguishes ten crisis types: natural disaster, rumour, product tampering, workplace violence, challenges, technical-error product recall, technical-error accident, human-error product recall, human-error accident, and organizational misdeed. The crisis types differ in the amount of crisis responsibility stakeholders attribute to the organization. These crisis responsibility attributions have been utilized to classify the various crisis types into three clusters: the victim cluster, the accidental cluster, and the intentional cluster.

The victim cluster consists of crisis types that cause weak attributions of crisis responsibility (natural disaster, rumours, workplace violence, and product tampering) and thus a mild reputational threat. In addition, the organization is considered a victim of the crisis. The earthquake in Nepal that occurred on April 25, 2015 is an example of a natural disaster.

The accidental cluster consists of crisis types that cause minimal attributions of crisis responsibility (challenges, technical-error accidents, and technical-error product recalls) and thus a moderate reputational threat. Furthermore, the incident is viewed as unintentional or uncontrollable by the organization. The 2010 BP oil spill in the Gulf of Mexico that was caused by a gas explosion is an example of an accidental crisis.

Finally, the intentional crisis cluster consists of crisis types that cause strong attributions of crisis responsibility (human-error product recalls, human-error accidents, and organizational misdeeds) and thus a severe reputational threat. In addition, the event is considered intentional since people think such errors could and should have been preventable (Coombs, 2004, pp. 269-270). The Germanwings crash that occurred on March 24, 2015 is an example of an intentional crisis. This crisis will be closely analyzed in this Master’s thesis.

Table 1 gives an overview and definitions of the various crisis types.

Table 1: Crisis types definitions (Coombs, 2004, p. 270)

Victim Crisis Cluster Natural disaster: Acts of nature that damage an organization such as an earthquake. Some environmental/weather event impacts the organization. Rumours: False and damaging information about an organization is being circulated. Evidence that the information is false. Workplace violence: Current or former employee attacks current employees onsite. An employee or former employee injures or attempts to injure current employees. Product tampering: External agent causes damage to an organization. Some actor outside of the organization has altered the product to make it dangerous. Accidental Crisis Cluster Challenges: Stakeholders claim an organization is operating in an inappropriate manner. There is a public challenge based on moral or ethical, not legal, grounds. Technical-error accidents: A technology or equipment failure causes an industrial accident. The cause of the accident is equipment/technology related. Technical-error recalls: A technology or equipment failure causes a product to be recalled. A product is deemed harmful to stakeholders. The cause of the recall is equipment or technology related. Intentional Crisis Cluster Human-error accidents: Human error causes an industrial accident. The cause of the accident is a person or people not performing job properly. Human-error recalls: Human error causes a product to be recalled. A product is deemed harmful to stakeholders. The cause of the recall is a person or people not performing job properly. Organizational misdeed: Laws or regulations are violated by management or stakeholders are placed at risk by management. Members of management knowingly violate laws/regulations or offer a product or service they know could injure stakeholders.

Furthermore, SCCT argues that, when there is an intensifying factor, such as a crisis history or an unfavourable prior relationship reputation, a victim crisis causes the same reputational threat as an accident crisis. Similarly, accident crises generate the same reputational threat as an intentional crisis (Coombs, 2007, pp. 168-169). Consequently, if an intensifier exists, crises in the victim cluster should be handled like crises in the accidental cluster, and crises in the accidental cluster should be handled like crises in the intentional cluster (Coombs, 2004, p. 272).

2.1.4.4 Crisis response strategies

The crisis response strategies can be described as the organization’s actions after a crisis. They are utilized in order to mend the reputation, to avert negative behavioural intentions, and to diminish negative affect. In SCCT, responsibility provides the conceptual link between crisis response strategies and the reputational threat of a crisis. Therefore, SCCT’s list of crisis response strategies is based upon the perceived acceptance of responsibility for a crisis 16 represented in the response. When crisis response strategies become increasingly accommodative, show more concern for victims, the organization is perceived by stakeholders as taking more responsibility for the crisis (Coombs, 2007, p. 170). Table 2 gives an overview of the primary and secondary crisis response strategies utilized in SCCT.

Table 2: SCCT crisis response strategies (Coombs, 2007, p. 170)

Primary crisis response strategies Deny crisis response strategies Attack the accuser: Crisis manager confronts the person or group claiming something is wrong with the organization. Denial: Crisis manager asserts that there is no crisis. Scapegoat: Crisis manager blames some person or group outside of the organization for the crisis. Diminish crisis response strategies Excuse: Crisis manager minimizes organizational responsibility by denying intent to do harm and/or claiming inability to control the events that triggered the crisis. Justification: Crisis manager minimizes the perceived damage caused by the crisis. Rebuild crisis response strategies Compensation: Crisis manager offers money or other gifts to victims. Apology: Crisis manager indicates the organization takes full responsibility for the crisis and asks stakeholders for forgiveness.

Secondary crisis response strategies Bolstering crisis response strategies Reminder: Tell stakeholders about the past good works of the organization. Ingratiation: Crisis manager praises stakeholders and/or reminds them of past good works by the organization. Victimage: Crisis managers remind stakeholders that the organization is a victim of the crisis too.

Organizations that use deny strategies to respond to a crisis seek to remove any connection between the organization and the crisis. If this frame of denial is accepted by stakeholders, the organization does not suffer from any reputational damage. By using diminish crisis response strategies, crisis managers argue that a crisis is not as severe as people think or that the organization had no control over the crisis. If this crisis response strategy is accepted by stakeholders, the crisis is less damaging to the organization. Furthermore, an organization uses rebuild strategies to respond to crises that cause a serious reputational threat, such as intentional crises or accidental crises, in combination with a history of crises or an unfavourable prior relationship reputation. In that case, the crisis team takes positive actions in order to compensate for the crisis. To conclude, bolstering strategies are preferably used in addition to the three primary strategies, since they only offer a minimal opportunity to enhance the organization’s reputation (Coombs, 2007, pp. 171-172).

2.1.5 Emotions in crisis communication

In order to handle a crisis effectively, it is crucial for crisis managers to understand that emotions and conflicts are closely related to each other. Therefore, crisis managers should understand how crisis situations are experienced, felt, and evaluated by stakeholders (Jin, 2010, p. 523).

In addition to being a reputational threat, crisis responsibility causes affective reactions as well. It was found that stronger attributions of crisis responsibility result in feelings of anger and in some extreme cases in schadenfreude (getting pleasure from the pain of others) toward the organization. Moreover, feelings of sympathy for the organization reduce. Due to negative emotions, stakeholders can decide to break off interactions with an organization or engage in negative word of mouth about the organization. It was also found that increased feelings of anger and schadenfreude lead to behavioural intentions, such as the intention to buy an organization’s products and support of community members, that were less supportive of an organization (Coombs, 2007, p. 169).

Jin et al. (2007, p. 88) argue that anger, fright, anxiety, and sadness are the four dominant emotions experienced by the publics in a crisis. Guilt and shame, on the other hand, are secondary emotions, especially since external publics are less subject to these emotions.

Ekman (1992, p. 170), an expert in the field of emotions, distinguishes six basic emotions, namely anger, fear, sadness, enjoyment, disgust, and surprise. Although these emotions represent the predominant scale for the analysis of emotion in text-based messages, this scale will not be used in this Master’s thesis. Instead, the scale of Jin et al. (2014, p. 509) will be used. They developed a scale for measuring public’s emotions in organizational crises, since they argue that the emotions experienced by publics in non-crisis situations differ from those felt in crisis situations. It was found that publics are likely to feel three types of emotions when confronted with organizational crises: (1) attribution-independent emotions, which consist of anxiety, fear, apprehension, and sympathy; (2) external-attribution-dependent emotions, which consist of disgust, contempt, anger, and sadness; and (3) internal-attribution- dependent emotions, which consist of embarrassment, guilt, and shame. Attribution- independent emotions are emotions without a clear direction of attribution or blame that publics feel toward a crisis situation. Attribution-dependent emotions, on the other hand, are most likely to occur when the outcome of a crisis is negative or unexpected, causing people to seek the cause of the negative outcome. Two types of attribution-dependent emotions can be 18 distinguished: external-attribution-dependent emotions and internal-attribution-dependent emotions. External-attribution-dependent emotions are emotions publics feel about an organization in a crisis, and internal-attribution-dependent emotions are emotions publics feel for themselves as publics involved in a crisis (Jin, Liu, Anagondahalli, & Austin, 2014, pp. 509-515).

2.2 SOCIAL MEDIA

2.2.1 Social media defined

Social media can be described as a collection of online communication channels or tools that have five characteristics in common:

 participation: anyone can provide feedback on content;  openness: people are allowed to post content and feedback on most social media;  conversation: social media facilitate two-way interaction;  communities: people with similar interests can quickly form groups;  connectedness: links to other content are heavily utilized.

Interactivity is the main factor connecting these five characteristics (Coombs, 2012, p. 21).

Coombs (2012, p. 21) states that social media are an evolutionary impetus, since the creation and distribution of information is now controlled by users rather than by the traditional news media or organizations. Furthermore, he argues that social media are responsible for the increasing connection between crisis communication and the online world.

Table 3 gives an overview of the social media categories that can be used in crisis communication.

Table 3: Social media categories (Coombs, 2012, p. 24) Social networks Individual Web pages from which people share content and communicate with friends (Example: Facebook)

Blogs Online journals where people post content and others can comment on it

Wikis Web pages where people work together to create and edit content

Podcast Audio and video content created and distributed through a subscription based service 19

Forums Online discussions revolving around specific interests and topics

Content communities Places where people organize themselves around specific content that they create and comment on

Microblogs Sites on which people share small amounts of information through posts (Example: Twitter)

Aggregators Tools that collect content (e.g., news stories, blogs) from different sites on one site; content is frequently ranked by popularity and can include comments from users.

Social bookmarking Tool with which people share and rate content they have found online

2.2.2 The role of social media in organizational crisis communication

During crises, publics increasingly use social media (Jin, Liu, & Austin, 2011, p. 1). Therefore, social networking sites are regarded as important forums to track spontaneous reactions of publics during crises (Schwarz, 2012, p. 431). Veil et al. (2011, p. 118) state that the principal reason for crisis managers to use social media is that the organization’s stakeholders are already using social media to communicate about crises. When crisis communicators decide not to use the online forum, this does not mean that the conversation on the crisis through social media will come to an end. Instead, the conversation will continue without the organization’s voice being heard. However, when crisis managers decide to embrace social media, this does not mean that they should discontinue using mainstream media (Veil, Buehner, & Palenchar, 2011, pp. 118-119). Social media are thus an excellent scanning tool for organizations to locate emerging trends on the Internet that can possibly develop into crises (Coombs, 2012, p. 25). As a consequence, some crises can be anticipated, thus protecting the organization’s reputation (Veil, Buehner, & Palenchar, 2011, p. 114).

In addition to the fact that social media are an inviting means of communication for those who have experienced a crisis, it is also an ideal way for crisis managers to demonstrate their compassion, concern, and empathy to stakeholders. Moreover, social media allow organizations to speak directly to their stakeholders without being filtered (Veil, Buehner, & Palenchar, 2011, p. 116). The rapid growth of social media results in a viral spread of information. This could be considered an advantage to crisis managers, since they must reach the public as soon as possible. On the other hand, it has become more and more difficult for authoritative voices to be heard, since everyone can now spread information on social media 20

(Freberg, 2012, p. 416). Freberg (2012, pp. 416-417) carried out research into the intention to comply with a food crisis message communicated via social media. First, the intention to comply with a message coming from an organizational source was compared to the intention to comply with a message coming from a user-generated source. Next, it was analyzed whether messages containing confirmed information had the same impact on intention to comply as messages containing unconfirmed information. The results of the study indicated a stronger intent to comply with organizational messages than with user-generated messages. Message reliability, on the other hand, did not have an impact on intention to comply with the message. It was also found that younger people make less distinction between organizational and user-generated sources than older people (Freberg, 2012, pp. 416-420).

The following section focuses on one particular form of social media that is often utilized in crisis communication, namely Twitter.

2.2.3 The use of Twitter in organizational crisis communication

Veil et al. (2011, p. 113) define Twitter as “a microblog social networking platform through which individuals can post or ‘tweet’ comments to those who subscribe or ‘follow’ the blogger”. Citizens, news media organizations, and other types of organizations can use Twitter to share crisis-related information (Heverin & Zach, 2010, p. 5). They can send messages of maximum 140 characters and tweets about a particular topic can be categorized by using the hashtag-symbol (Heverin & Zach, 2010, p. 2).

Smith (2010, p. 330) states that due to its character-count limits and real-time updates, Twitter is a place for ongoing and immediate interaction. Furthermore, it facilitates dialogue in ways that other social media forms, such as blogs, do not.

In the two sections that follow, a distinction is made between the use of Twitter by corporations and the use of Twitter by citizens in a crisis situation.

2.2.3.1 The use of Twitter by corporations

Many businesses in the United States believe it is appropriate to invest in communication with their followers via Twitter. Whereas some corporations employ specialized Twitter 21 communication teams to operate their corporate Twitter accounts, there are also CEOs who use Twitter themselves to directly post brief updates and closely communicate with their followers (Hwang, 2012, p. 159). Tim Cook, the CEO of Apple, is an example of a corporate CEO in the Twitter world (Fortune 500 CEOs on Twitter, 2015).

Hwang (2012, p. 159) conducted a study in which he investigated the effects of microblogging services on forming perceptions about the leadership abilities of a CEO, the attitudes toward CEOs who use Twitter, and the attitudes toward corporations. The responses of young consumers who participated in this study showed that most respondents positively assessed the use of Twitter by CEOs. Even though there was no direct association between the use of Twitter and the evaluation of organizations, there was a direct effect between the use of the social medium and the perception of desirable leadership and evaluation of CEOs. This in turn positively affected attitudes toward organizations (Hwang, 2012, p. 160).

Therefore, it can be concluded that an active use of Twitter, and thus an open two-way communication with costumers, can help corporate leaders to develop effective personal public relations and to promote a positive image of the organization (Hwang, 2012, p. 161). This in turn will also be favourable when the organization is confronted with a crisis.

2.2.3.2 The use of Twitter by citizens

The ways in which citizens communicate in times of emergencies have changed owing to the rapid growth of social media. Emergency agencies are no longer the only resource of crisis- related information. Instead, citizens can now actively create, disseminate and share crisis- related information with a large audience using social media sites (Heverin & Zach, 2010, p. 1).

In their research-in-progress paper, Heverin et al. (2010, pp. 2-5) reported on how microblogging was used as a communication and information sharing resource during a violent crisis in Washington in 2009. They found that the majority of the messages transmitted were information-related tweets that were produced by citizens. Furthermore, the microblogging service was also used to share opinions, emotions, and other types of content (Heverin & Zach, 2010, p. 5). 22

While microblogs have recently been studied extensively in the field of crisis communication, limited research has been done in order to understand how users utilize the large quantity of real-time information available on microblogs during crises (Sreenivasan, Lee, & Goh, 2011, pp. 1-3). Therefore, Sreenivasan et al. (2011, pp. 4-8) conducted a study in which the information use categories in user tweets about the 2010 Icelandic volcanic ash eruption were examined. The results indicated that sharing contextual information, providing personal updates, explaining problems and reporting of factual data were the principal themes of information use. Furthermore, it was found that in order to reduce stress or frustration during a crisis, users often have recourse to humour (Sreenivasan, Lee, & Goh, 2011, pp. 6-8).

2.3 AUTOMATIC CLASSIFICATION OF SENTIMENT AND EMOTION IN CRISIS- RELATED MICROPOSTS

2.3.1 Automatic classification of sentiment

It has been shown that tweets supply useful real-time information for decision-making during crises. Due to the vast amount of both factual and emotional content being posted continuously, however, it has become unfeasible for both users and organisations to quickly exploit and react to this information (Schulz, Thanh, Paulheim, & Schweizer, 2013, p. 846). By applying information extraction and sentiment analysis, important information can be differentiated from unimportant information. While information extraction allows to extract factual information, such as the number of victims of a plane crash, sentiment analysis allows to detect human emotions in crisis-related tweets.

Esuli et al. (2006, p. 417) define sentiment classification, also known as opinion mining, as “a recent subdiscipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a text is about, but with the opinion it expresses”. Several subtasks can be identified within opinion mining (Esuli & Sebastiani, 2006, p. 417):

 determining text SO-polarity: determining whether a given text is objective (i.e. has a factual nature) or subjective (i.e. conveys a positive or a negative opinion);  determining text PN-polarity: determining whether a given subjective text conveys a positive or a negative opinion;  determining the strength of text PN-polarity: determining if the positive or negative opinion conveyed by a text is weakly, mildly or strongly positive or negative. 23

Through sentiment analysis, tweets with negative emotions can be selected and analyzed over tweets with positive emotions, which could, on the one hand, help to detect people in danger during crisis situations. On the other hand, this could also help to detect tweets that contribute to situational awareness. However, due to the fact that tweets are unstructured and often contain colloquial language, slang and abbreviations, it is rather challenging to detect sentiments in tweets (Schulz, Thanh, Paulheim, & Schweizer, 2013, p. 846).

Several researchers have attempted to create a system that can automatically determine whether a given text is positive, negative or objective. There are two approaches to this problem of extracting sentiment automatically: a lexicon-based approach and a learning-based approach (Taboada, Brooke, Tofiloski, Voll, & Stede, 2011, p. 268). Lexicon-based systems are systems that determine the polarity of a tweet through the help of a lexicon consisting of positive and negative words. An example of such a lexicon is SentiWordNet, which can be defined as “a lexical resource in which each WordNet synset s is associated to three numerical scores Obj(s), Pos(s) and Neg(s), describing how objective, positive, and negative the terms contained in the synset are” (Esuli & Sebastiani, 2006, p. 417). Machine learning systems, on the other hand, are systems that are trained using a corpus of tweets that were manually labelled as positive, negative or neutral. Subsequently, on the basis of these tweets, classifiers are built for the polarity classification of unseen data. An example of such a machine learning system is the contribution to the SemEval-2014 Task 9: Sentiment Analysis in Twitter (Van Hee, Van de Kauter, De Clercq, Lefever, & Hoste, 2014), which will be elaborated upon in the research methodology of this paper’s study.

2.3.2 Automatic classification of emotion

Contrary to sentiment analysis, which classifies crisis-related tweets as positive, negative or neutral, affect analysis or emotion recognition classifies crisis-related tweets as belonging to an emotional state. Since it is a multinomial classification problem, emotion analysis is even more challenging than sentiment analysis (Brynielsson, Johansson, & Westling, 2013, p. 34). Torkildson et al. (2014, p. 64) state that emotion analysis in text-based communication helps to understand how people communicate during crises.

Most systems for automatic analysis of emotions are based on the six basic emotions of Ekman (1992): anger, fear, sadness, enjoyment, disgust, and surprise. Strapparava and 24

Mihalcea (2008, p. 1556) constructed a large data set annotated for these basic emotions. By using a knowledge-based and a corpus-based approach, they effectuated five different systems for emotion analysis. The systems were then evaluated on a data set consisting of 1,000 newspaper headlines. As expected, each system had its own strengths. It was found that the system based on the presence of words from the WordNet Affect lexicon had the highest precision, but a low recall. The Latent Semantic Analysis system using all the emotion words, on the other hand, had the largest recall, but a significantly lower precision. Moreover, it was discovered that the system based on blogs provided the best results for anger and joy (Strapparava & Mihalcea, 2008, pp. 1557-1560). Torkildson et al. (2014) and Schulz et al. (2013) contributed an approach to identify Ekman’s six basic emotions in crisis-related tweets. Schulz et al. (2013, p. 850) state that more tweets that are relevant for decision- making in emergency management can be detected using their 7-class sentiment classifier (anger, disgust, fear, happiness, sadness, surprise, and neutral) than using a traditional 3-class sentiment classifier (positive, negative, and neutral).

Brynielsson et al. (2013, p. 33) manually tagged the emotional content of tweets sent during the Sandy hurricane as one of the classes positive, anger, fear, or other. The class other contained non-emotional content as well as emotions not belonging to any of the other classes. The objective was creating a classifier that could discriminate between the different classes. For that purpose, experiments with supervised learning algorithms were conducted using the tweets that had obtained a good inter-annotator agreement. It was found that the classifications were good enough to estimate the citizens’ emotions and attitudes toward a crisis on a more aggregated level. However, the machine learning algorithms did not perform well enough to be trusted on the level of individual postings (Brynielsson, Johansson, & Westling, 2013, pp. 33-36).

As yet, the task of detecting emotions in crisis-related tweets has received limited attention. Therefore, in this master’s paper, we want to investigate the feasibility of automatically detecting crisis-related emotions on Twitter.

3. RESEARCH METHODOLOGY

This chapter elaborates upon the research methodology of this Master’s thesis. In the first section, the event of study, namely the crash of Germanwings Flight 9525, is discussed. The second section explains how the data for this study were gathered. In the final section of this chapter, section three, the methods used for detecting the sentiment and the emotion expressed in each tweet are discussed.

3.1 EVENT OF STUDY

On Tuesday, March 24, 2015, around 10:41 Central European Time, an Airbus A320-200 crashed in the French Alps, 100 kilometres northwest of Nice. It concerned Flight 9525, an international passenger flight from Barcelona-El Prat Airport in Spain to Düsseldorf Airport in Germany. The flight was operated by Germanwings, a low-cost airline owned by Lufthansa. First, the crash was assumed to be an accident. On March, 26, however, the French Bureau d’Enquêtes et d’Analyses pour la Sécurité de l’Aviation Civile discovered after analyzing the aircraft’s flight data recorder that co-pilot Andreas Lubitz deliberately crashed the aircraft. Brice Robin, the public prosecutor of Marseille, declared that Lubitz had locked the cockpit door when captain Patrick Sondenheimer took a toilet break and that he refused to open the door when the captain returned. Next, the co-pilot initiated a descent causing the aircraft to crash into a mountain.

Two pilots, four cabin crew members, and 144 passengers were on board of the aircraft. No one survived the crash. There were eighteen different nationalities among the victims, but most people came from Spain and Germany. Sixteen students and two teachers from the Joseph-König-Gymnasium of Haltern am See, who came back from a student exchange with a school in Barcelona, were among the passengers. Furthermore, opera singers Oleg Bryjak and Maria Radner were on the flight. There is also one Belgian victim.

When German detectives searched Andreas Lubitz’s house three days after the crash, no suicide note was found. Moreover, no evidence was discovered that he crashed the plane due to political or religious motives. What they did find, however, was a note in a dustbin indicating that Lubitz had been declared unfit to work by a doctor. The following day, evidence was discovered that Lubitz suffered from a psychosomatic illness and that he was taking prescription drugs. 26

In response to the crash, Lufthansa and other airlines introduced new regulations that require the presence of two crew members in the cockpit at all times (Germanwings Flight 9525, 2015).

3.2 DATA COLLECTION

For this paper’s study, two corpora were created: a corpus of English tweets containing hashtag ‘#GermanWingsCrash’ and a corpus of Dutch tweets containing hashtag ‘#GermanWingsCrash’. These two corpora can be consulted on Minerva.

The Twitter search facility was used in order to find all English posts, made by any Twitter user, that contained hashtag ‘#GermanWingsCrash’. At first, we tried to gather all English tweets containing hashtag ‘#GermanWingsCrash’, but due to the vast amount of tweets, it soon became clear that this would be impossible, since all tweets had to be copied and pasted manually in an Excel file. Therefore, it was decided to make a selection: we would gather up to a maximum of 25 tweets per hour. In order to make sure that the selected tweets would be representative for the total amount of tweets, every two to three minutes a tweet was copied and pasted in the Excel file. The collection dates for user tweets posted during the crisis were from March 24, 2015 up to and including April 6, 2015. We decided to terminate the collection of tweets after April 6, since the number of tweets per day had strongly decreased. For the entire data collection, a total of 5490 English tweets were harvested. In addition to the tweets, the Twitter user who posted each message and the time at which each tweet was posted, were also included in the corpus.

The Dutch tweets containing hashtag ‘#GermanWingsCrash’ were harvested automatically. Next, the automatically harvested Dutch tweets were imported into an Excel file. However, since the tweets were gathered automatically, a number of tweets in other languages had crept into the data. These tweets were manually deleted from the Dutch corpus. The data collection period was equal to the data collection period of the English tweets, namely from March 24, 2015 up to and including April 6, 2015. For the entire data collection, a total of 722 Dutch tweets were gathered. Similar to the corpus of English tweets, the corpus of Dutch tweets also contains information about the users who posted the tweets and about the time at which each tweet was posted. 27

Furthermore, since Germanwings and Lufthansa, the organizations affected by the crisis, did not use hashtag ‘#GermanWingsCrash’ in their tweets, our corpora of English and Dutch tweets do not contain tweets from neither Germanwings nor Lufthansa. Therefore, we decided to gather the tweets of both organizations in a separate file. The queries ‘Germanwings’ and ‘Lufthansa’ were submitted to the Twitter search facility and the organizations’ tweets posted from March 24, 2015 up to and including April 6, 2015 were then copied and pasted in a separate Excel file. In total, 51 tweets posted by both Lufthansa and Germanwings were gathered. The tweets posted by Germanwings and Lufthansa can be found in the appendix of this paper.

3.3 METHOD

3.3.1 Sentiment detection

After the corpus of English tweets was created, it was determined for each tweet whether it was objective or subjective, and if it was subjective it was determined whether the tweet conveyed a positive or a negative opinion. The system developed by Van Hee et al. (2014) in the framework of the SemEval-2014 Task 9: Sentiment Analysis in Twitter was used for the classification of the English tweets. The following paragraph gives an overview of the pipeline that was developed and of the features that were implemented.

First, linguistic preprocessing was performed on the datasets. Initially, the possible polarity labels were positive, negative, objective, and neutral, which is a combination of a positive and a negative sentiment that outweigh each other and become a neutral sentiment. The label objective-OR-neutral was added, since in some cases, it was too difficult to draw a distinction between these two labels. However, as a final preprocessing step, it was decided that the labels objective, objective-OR-neutral, and neutral would be combined, Then, a number of lexical and syntactic features were implemented: n-gram features, word shape features (e.g. the number of capitalized words), lexicon features, syntactic features (e.g. Part-of-Speech), named entity features and PMI features (PMI values indicate the association of a word with positive and negative sentiment). After performing feature selection experiments, it was discovered that features based on n-grams, sentiment lexicons, and Part-of-Speech tags were most contributive for labelling a message or an instance of that message as positive, negative 28 or neutral. Dependency features, on the other hand, did not contribute to the classification performance (Van Hee, Van de Kauter, De Clercq, Lefever, & Hoste, 2014, pp. 406-409).

Lacking a labelled Dutch training corpus on the basis of which a sentiment classifier could be developed, we relied on a lexicon-based approach for the classification of the Dutch tweets. The predictions for the tweets in the Dutch corpus are based on the Dutch Duoman (Jijkoun & Hofmann, 2009) and Pattern (De Smedt & Daelemans, 2012) sentiment lexicons.

In addition to the automatic sentiment analysis, a gold standard annotation of both corpora was also provided. In both the corpus of English tweets and the corpus of Dutch tweets, 200 tweets were manually labelled as being positive, negative or neutral. These two corpora of 200 tweets can be found in the appendix of this paper.

3.3.2 Emotion detection

The 200 Dutch tweets and the 200 English tweets of the gold standard annotation for sentiment were also tagged manually for emotions. For that purpose, the scale of Jin et al. (2014, p. 509) was used, since it was specifically developed for measuring public’s emotions in organizational crises. This crisis emotion scale consists of thirteen discrete emotions: anger, anxiety, apprehension, confusion, contempt, disgust, embarrassment, fear, guilt, sadness, shame, surprise, and sympathy (Jin, Liu, Anagondahalli, & Austin, 2014, p. 513). However, a number of emotions were considered as one emotion, since the difference between the emotions is very little, more precisely anger and fear, and shame and embarrassment. Tweets that conveyed an emotion that did not occur in Jin et al.’s crisis emotion scale were labelled as other. The emotion labels given to the 200 English tweets and the 200 Dutch tweets can be consulted in the appendix of this paper.

The tweets were manually tagged for emotions, since systems that are able to distinguish crisis-related emotions for both English and Dutch are lacking. As already mentioned in section 2.3.2 of this paper, the detection of emotions in crisis-related tweets is a challenging task and has received limited attention up to now.

4. RESULTS AND DISCUSSION

4.1 MOST FREQUENTLY USED WORDS AND ANALYSIS OF THE ORGANISATION’S TWEETS

In order to find out what the Twitter users were mostly talking about in the tweets in our two corpora, different word clouds were created using the online tagcrowd program (http://tagcrowd.com/). In total, five word clouds were created. As expected, in each of these five word clouds, ‘germanwingscrash’ and ‘germanwings’ were the most frequently used words.

Three word clouds were created using the tweets in our English corpus: one word cloud of the tweets posted before it was discovered that the co-pilot deliberately crashed the plane, and two word clouds of the tweets posted after it was discovered that the co-pilot deliberately crashed the plane. Two separate word clouds had to be made, since there were too much data for one word cloud. For the first word cloud, tweets of the day of the crash and the first day after the crash were used as input. Words like ‘families’, ‘crash’, ‘prayers’, ‘thoughts’, ‘victims’, ‘condolences’, ‘passengers’, ‘pilot’ and ‘plane’ seemed to be frequently used words in the English tweets written on March 24 and March 25.

Graph 1: Word Cloud English tweets March 24-25

The second word cloud was made using the English tweets posted from March 26 until March 31. The tweets posted from April 1 until April 6 served as input for the third word cloud. In these two word clouds, ‘pilot’, ‘depression’, ‘suicide’, ‘cockpit’, ‘Andreas Lubitz’, ‘co-pilot’, ‘kill’, ‘victims’, ‘families’, ‘black box’ and ‘terrorism’ were frequently used words. 30

Graph 2: Word Cloud English tweets March 26-31

Graph 3: Word Cloud English tweets April 1-6

As expected, there is a clear difference between the first word cloud and the second and the third word cloud. It can thus be concluded that, in the course of March 26, the discovery and the announcement of the fact that the co-pilot deliberately crashed the aircraft had an impact on the word usage in the English tweets.

On the basis of the data in the Dutch corpus, another two word clouds were created. The Dutch tweets posted on March 24 and March 25 served as input for the fourth word cloud, in which ‘slachtoffers’, ‘sterkte’, ‘crash’, ‘vliegtuig’, ‘nabestaanden’, ‘nieuws’, ‘vliegramp’, ‘verschrikkelijk’ and ‘dood’ were frequently used words.

Graph 4: Word Cloud Dutch tweets March 24-25

The final word cloud was made using the Dutch tweets posted from March 26, when it was communicated that the co-pilot deliberately crashed the aircraft, until April 6. Words like ‘copiloot’, ‘piloot’, ‘mensen’, ‘cockpit’, ‘vliegtuig’, ‘zelfmoord’, ‘nieuws’, ‘crashen’, ‘dood’ and ‘bewust’ seemed to be frequently used words in this period of time.

Graph 5: Word Cloud Dutch March 26 – April 6

Although the difference is slightly less obvious than between the English word clouds, the discovery of the fact that the co-pilot deliberately crashed the plane resulted in a difference in frequently used words between the two Dutch word clouds. In the following paragraph, we will analyze the tweets posted by both Germanwings and Lufthansa in response to the crash. 32

In their tweets posted in response to the crash, Germanwings and Lufthansa focussed mainly on providing assistance to the families and friends of the victims of the plane crash. On March 24, a telephone hotline was established and a press release was issued. The next day, Lufthansa and Germanwings called for a minute’s silence to commemorate the victims. On March 26, the day on which it was discovered that the co-pilot deliberately crashed the aircraft, Germanwings held a press conference. The following day, Germanwings set up a Family Assistance Centre in Marseille, and Lufthansa published its employee magazine in black in a show of solidarity for those lost. In addition, Lufthansa group refined its safety structures by adopting the “rule of two” in the cockpit at all times. The tweets posted by Germanwings and Lufthansa in response to the crash can be found in the appendix of this paper.

While examining our two corpora of English and Dutch tweets, it soon became clear that the negative emotions of the Twitter users related to the co-pilot, and not to the organization. In the light of section 2.1.4 of this paper, it can be said that the responsibility for the crisis was not attributed to Germanwings nor Lufthansa. As a result, the reputational damage for Germanwings and Lufthansa caused by the crash is rather small.

In addition to the moment on which Germanwings and Lufthansa communicated that the co- pilot deliberately crashed the aircraft, the other moments on which Germanwings and Lufthansa communicated did not seem to have an impact on the word usage in the tweets.

4.2 SENTIMENT CLASSIFICATION

As already mentioned in section 2.1 of this paper, it is extremely important for a company to know when stakeholders are thinking badly of their company, since negative emotions can result in stakeholders breaking off interactions with the company or engaging in negative word of mouth about the organization, which in turn damages the organizational reputation. It is thus of the utmost importance for a company to be able to detect negative messages about itself on the Internet. The key stakeholders affected by this paper’s event of study, the crash of Germanwings Flight 9525, are employees, media, customers, and victims’ relatives. Through the help of sentiment analysis, we were able to detect the tweets with positive emotions, and more importantly the tweets with negative emotions in our two corpora.

4.2.1 Quantitative discussion of English results

The SemEval pipeline determined for each tweet in our corpus of 5490 English tweets whether the tweet was positive, negative or neutral. As can be seen in table 4, 676 tweets were labelled as positive, 2815 tweets were labelled as negative, and 1999 tweets were labelled as neutral. It is remarkable that the number of tweets that are labelled as positive is much smaller than the number of tweets that are labelled as negative or as neutral. However, this should not be surprising, since our corpus consisted of crisis-related tweets.

Table 4: Class distribution of the English corpus Class label Number of tweets Positive 676 Negative 2815 Neutral 1999 Total 5490

In order to determine how accurate the automatic system was in its predictions, we manually labelled 200 tweets and calculated the system accuracy on these gold standard annotations. Table 5 indicates that the accuracy of the system for the positive class label was 30%, for the negative class label 73%, and for the neutral class label 65%. The total system accuracy amounted to 63%. It can be concluded that the system particularly made mistakes with regard to the positive class label. This could be explained by the fact that the system has been trained with Twitter messages on a variety of topics and not with crisis-related tweets. As a result the training datasets probably contained more positive tweets, whereas our English corpus only contained 676 positive tweets, which represents only 12.3% of the entire corpus. Moreover, it can be concluded that the system performed best with regard to the negative class label. As already mentioned in section 2.3.1 of this paper, this is a significant advantage in crisis situations, in which the detection of negative emotions is highly important.

Table 5: System accuracy based on 200 gold annotations Class label System accuracy Positive 30% Negative 73% Neutral 65% Total system accuracy 63%

Table 6 gives an overview of the number of tweets that were manually labelled as positive, negative or neutral, as well as of the number of tweets that were automatically labelled as 34 positive, negative or neutral. 126 out of the 200 selected tweets were given the same label manually as well as automatically.

Table 6: Class distribution based on 200 gold annotations Class label Number of Number of gold predictions annotations Positive 20 26 Negative 99 82 Neutral 81 92 Total 200 200

4.2.2 Qualitative discussion of English results

In this section, the performance of the classification system used for the English tweets will be discussed. For that purpose, the system’s predictions were compared with the 200 gold standard annotations. We found that 126 out of 200 tweets received the same label both manually and automatically. A few examples of such tweets are represented in table 7.

Table 7: Tweets that received the same label both manually and automatically Tweet Prediction Gold annotation My heart goes out to everyone who is even remotely affected Positive Positive by this #GermanWingsCrash Sending love and support to all people struggling with Positive Positive depression. This must be such a difficult time 4U. #GermanWingsCrash #mentalillness I feel really sad for the 150 families who are suffering as a Negative Negative result of the #GermanWingsCrash. Beyond tragic. Reading new reports of the #GermanWingsCrash like wtf Negative Negative man.that sick bastard to crash the plane full of innocent ppl and kids. #MyheartBreaks Voice recordings show one pilot locked out cockpit Neutral Neutral http://bit.ly/18Zec4M #Germanwings #GermanWingsCrash #blackbox Francois Hollande and Angela Merkel arrive at Le Vernet, Neutral Neutral close to crash site. #GermanWingsCrash @Independent

While analyzing the tweets that were given a different label, we found that the mistakes made by the system could be divided into four types of mistakes. These types will now be discussed and a few examples will be given. Moreover, the elements in the tweets that supposedly misled the system, will be indicated in bold. 35

Type 1: Due to a number of interjections or not specifically sentiment-related words in the tweet, a sentiment is expressed, but the system labelled the tweet as neutral.

Table 8: Mistake type 1 Tweet Prediction Gold annotation Jan Cocheret, Dutch Emirates pilot, predicted a few weeks ago Neutral Negative that something like this could happen. Eerie. #GermanWingsCrash @TODAYshow Saturday Nite Live went to far with the Neutral Negative #GermanWingsCrash I'm appalled!!! Blown away. Pilot locked out of the #Germanwings Neutral Negative cockpit?!? I thought I heard it all. #GermanWingsCrash

Type 2: Due to a number of negative words, the system labelled the tweet as negative. However, the person posting the tweet did not experience the sentiment himself. As a consequence, the tweet is in fact neutral.

Table 9: Mistake type 2 Tweet Prediction Gold annotation #Germanwingscrash Spanish students in shock at Barcelona, Negative Neutral Families and friends were coming to terms with their grief http://ind.pn/1N5Dk94 Germans tend to go to work when sick as they fear getting Negative Neutral axed-research today by Boris Hirsch at Royal Economic Society #GermanWingsCrash Weather doesn't look great for search efforts Wednesday. Negative Neutral Rain to snow and even fog. #Germanwings #GermanWingsCrash

Type 3: Due to a number of positive words in the tweet, the system labelled the tweet as positive. But in fact, the tweet expresses a negative sentiment.

Table 10: Mistake type 3 Tweet Prediction Gold annotation I'm a big @onedirection fan but It's mind boggling to see Positive Negative #ZaynMalik leaving the band getting more coverage than the #GermanWingsCrash The man responsible for #GermanWingsCrash gets his wish to Positive Negative be in headlines and we forget Capt Sondenheimer heroism. http://edition.cnn.com/2015/03/30/europe/germanwings- captain-patrick-sondenheimer/index.htmlÂ â€¦

Type 4: Due to a number of negative words in the tweet, the system labelled the tweet as negative. But in fact, the tweet conveys a positive sentiment.

Table 11: Mistake type 4 Tweet Prediction Gold annotation #PatrickSonderheimer You're name should go down in history Negative Positive for being a hero in an ever so devastating situation. #GermanWingsCrash My sympathies to those who lost loved ones on Negative Positive #GermanWingsCrash. A320 is still one of the safest aircraft around. Won't stop me from flying.

4.2.3 Quantitative discussion of Dutch results

Through the help of the Dutch Duoman and Pattern sentiment lexicons, it was determined for each tweet in our corpus of 722 Dutch tweets, whether the tweet was positive, negative or neutral. Table 12 indicates that 253 tweets were labelled as positive, 297 tweets as negative, and 172 tweets as neutral. It is remarkable that, in comparison with the class distribution of the English corpus, a significantly bigger amount of tweets was labelled as positive. However, this result can be questioned, since the amount of crisis-related tweets conveying a positive opinion is usually much smaller than the amount of crisis-related tweets conveying a negative opinion, whereas in this case, the difference between the number of tweets labelled as positive and the number of tweets labelled as negative is rather small. This could possibly be explained by the fact that the system used for the classification of the Dutch results is a lexicon-based system and not a machine learning system. In other words, a tweet that contains positive words, but in fact is neutral or expresses a negative opinion, has more chance to be labelled as positive by a lexicon-based system.

Table 12: Class distribution of the Dutch corpus Class label Number of tweets Positive 253 Negative 297 Neutral 172 Total 722

In addition, the system accuracy based on 200 gold annotations was calculated. As can be seen in table 13, the system accuracy for the positive class label was 85%, which is a very 37 good result. The system accuracy for the negative class label was 62 %. The system accuracy for the neutral class label, on the other hand, was rather low, namely 31%. It can thus be concluded that the system particularly made mistakes with regard to the neutral class label. The total system accuracy amounted to 54%. When comparing this with the accuracy of the English system, we see that the system used for the classification of English tweets performed better than the system used for the classification of Dutch tweets.

Table 13: System accuracy based on 200 gold annotations Class label System accuracy Positive 85% Negative 62% Neutral 31% Total system accuracy 54%

Table 14 gives an overview of the amount of tweets that were automatically labelled as positive, negative or neutral, as well as of the amount of tweets that were manually labelled as positive, negative or neutral. It was also found that 107 out of 200 tweets received the same label both manually and automatically. Based on this result, it can be said that the SemEval pipeline again performed better than the system using the Dutch Duoman and Pattern sentiment lexicons. This should not be surprising, since the system used in order to label the Dutch tweets is less fine-grained than the system used for the classification of the English tweets.

Table 14: Class distribution based on 200 gold annotations Class label Number of Number of gold predictions annotations Positive 62 20 Negative 100 110 Neutral 38 70 Total 200 200

4.2.4 Qualitative discussion of Dutch results

After the quantitative discussion of the Dutch results, we will now discuss the performance of the classification system used for the Dutch tweets. It was found that 107 out of the 200 selected tweets were given the same label manually as well as automatically. Six examples of such tweets are illustrated in table 15. 38

Table 15: Tweets that received the same label both manually and automatically Tweet Prediction Gold annotation Bijzondere helden , die RI collega's ; al 150 unieke DNA Positive Positive profielen , zo kort na de #Germanwingscrash Maar wat een verdrietig contrast met #MH17 veel kracht voor de familieleden die de Alpen gaan bezoeken Positive Positive #GermanWingsCrash Wat een afschuwelijk , verschrikkelijk , schokkend verhaal... Negative Negative #GermanWingsCrash Waarom in godsnaam zoveel mensen meenemen in je Negative Negative wanhoopsdaad ? Dit is geen zelfmoord meer , maar een aanslag . #Germanwings #GermanWingsCrash #GermanWingsCrash #4U9525 - Belgisch slachtoffer is Neutral Neutral Christian Driessens ( 59 ) woonachtig in Barcelona http://www.hln.be/hln/nl/33222/Vliegtuigcrash- Germanwings/article/detail/2263145/2015/03/24/Minstens-een- Belgisch-slachtoffer-aan-boord-van-rampvlucht.dhtml ... De zoektocht naar de lichamen van de 150 slachtoffers van de Neutral Neutral vliegramp in Zuid-Frankrijk is gestaakt . #GermanWingsCrash #A320Crash

When the tweets that were given a different label were analyzed, we found that the mistakes made by the system could be divided into four types of mistakes. These types will now be discussed and a few examples will be given. Moreover, if there are elements in the tweets that supposedly misled the system, they will be indicated in bold. The first three types of mistakes did also occur in the sentiment predictions of the English tweets.

Type 1: Due to a number of interjections or not specifically sentiment-related words in the tweet, a sentiment is expressed, but the system labelled the tweet as neutral.

Table 16: Mistake type 1 Tweet Prediction Gold annotation Bah , je zou haast vliegangst gaan krijgen ! Neutral Negative #GermanWingsCrash Waarom met jezelf nog 149 anderen.. ? #GermanWingsCrash Neutral Negative Stil van...|#GermanWingsCrash Neutral Negative

Table 17: Mistake type 2 Tweet Prediction Gold annotation Andreas Lubitz zag zijn droom van piloot in duigen vallen Negative Neutral wegens gezichtsproblemen , meldt @nytimes : http://nytimes.com ? smprod=nytcore-iphone&smid=nytcore- iphone-sharenytimes.com/ ? smprod=nytcor ... #GermanWingsCrash In het huis van #germanwings zelfmoord dader heeft politie Negative Neutral papieren gevonden waaruit blijkt dat hij ziek was #Germanwings #GermanWingsCrash Liveblog : De schok en ongeloof bij Lufthansa en Germanwings Negative Neutral steeds groter #GermanWingsCrash http://www.lindanieuws.nl/nieuws/copiloot-german-wings-zette- daling-bewust-in/ ... pic.twitter.com / ZC0acNVqYo

Type 3: Due to a number of positive words in the tweet, the system labelled the tweet as positive. But in fact, the tweet expresses a negative sentiment.

Table 18: Mistake type 3 Tweet Prediction Gold annotation Wat een onvergetelijk schoolreisje had moeten zijn Positive Negative #GermanWingsCrash #kippenvel Leuk hoor voor de familie van de piloot , graag beetje Positive Negative consideratie . Hun hebben naast rouw , ook een schaamte . Gun hun privacy ! #Germanwings

Type 4: The tweet clearly conveys a negative opinion, but the system labelled the tweet as neutral. In these tweets, no elements were found that could have misled the system.

Table 19: Mistake type 4 Tweet Prediction Gold annotation Pffff elke keer als ik beelden zie van de #GermanWingsCrash Neutral Negative krijg ik tranen in mijn ogen #heftig #verdrietig 150 onschuldigen vermoorden... Niet te vatten ! Wat een wereld Neutral Negative , wat een wereld ! #Germanwings #GermanWingsCrash #rip

It can be concluded that through the help of the SemEval pipeline and the Duoman and Pattern sentiment lexicons, we were to some extent able to detect sentiment in the tweets in our two corpora. This could help Germanwings and Lufthansa to adopt the appropriate crisis response strategy in order to reach their key stakeholders efficiently. However, it should be 40 stressed that the system used for the classification of the English tweets was much more accurate than the system used for the classification of the Dutch tweets, since it is a machine learning system. As a consequence, there is a strong need for a machine learning system for the classification of Dutch tweets as well.

4.3 EMOTION CLASSIFICATION

In order to detect more tweets that are relevant for decision-making, we performed emotion analysis on our two corpora as well.

4.3.1 Quantitative discussion of English results

As already mentioned in section 4.2.1, we labelled a gold standard corpus of 200 tweets, in which 26 tweets were manually labelled as positive, 82 tweets as negative and 92 tweets as neutral. For the tweets expressing a positive or a negative opinion, we tagged the emotional content as one of the classes anger, fear, apprehension, confusion, contempt, disgust, embarrassment, guilt, sadness, surprise, sympathy and other. Table 20 provides an overview of the occurrence of these emotion classes in our English gold standard corpus. Sympathy, anger and contempt are the emotions that were most frequently expressed in the 200 English tweets. No tweets conveying embarrassment or guilt were found in our gold standard corpus. Furthermore, two tweets were labelled as other, which means that they convey an emotion that does not belong to any of the other classes. We labelled the emotions expressed by these tweets as critical.

Table 20: Occurrence emotion classes in English gold standard corpus Emotion class Number of tweets Anger 25 Fear 4 Apprehension 4 Confusion 2 Contempt 21 Disgust 9 Embarrassment 0 Guilt 0 Sadness 14 Surprise 1 Sympathy 26 Other 2

For each emotion class, an example of a tweet expressing this emotion is represented in table 21.

Table 21: Examples of English tweets expressing emotion Tweet Sentiment label Emotion label This documentary about Andreas Lubitz is making my Negative Anger blood boil #GermanWingsCrash Thanks to the evil #GermanWingsCrash I'm officially Negative Fear scared to fly, they should allow us to talk and meet our piolt incase. If the pilot used an axe on the door, whats to stop a Negative Apprehension terrorist? What other potential weapons r laying round on flights? #GermanWingsCrash Should I be worried or reassured by the Negative Confusion #GermanWingsCrash? It is good to know that the doors won't open from the outside...but then again... So this guy takes a picture in front of the Golden Gate Negative Contempt Bridge..The most used bridge for suicide jumps. Dude why not then? #GermanWingsCrash The Daily Mail coverage of the #GermanWingsCrash Negative Disgust has been repugnant. Headlines like 'how the nazis led to killer co-pilot' help no one. I feel really sad for the 150 families who are suffering Negative Sadness as a result of the #GermanWingsCrash. Beyond tragic. Blown away. Pilot locked out of the #Germanwings Negative Surprise cockpit?!? I thought I heard it all. #GermanWingsCrash Our thoughts and prayers go out to those who lost loved Positive Sympathy ones in the #GermanWingsCrash May God be with you in these hard times. I'm thinking this attn on #AndreasLubitz and the Negative Other - critical #GermanWingsCrash is overdone. It's tragic & I would rather see the focus on the victims. 42

4.3.2 Quantitative discussion of Dutch results

In the gold standard corpus of 200 Dutch tweets, 20 tweets were labelled as positive, 110 tweets as negative, and 70 tweets as neutral. The emotional content of the tweets conveying a positive or a negative opinion, was tagged as one of the classes anger, fear, apprehension, confusion, contempt, disgust, embarrassment, guilt, sadness, surprise, sympathy and other. An overview of the occurrence of these emotion classes in our Dutch gold standard corpus is provided in table 22. Sadness, contempt, disgust and anger are the emotions that were most frequently conveyed in the 200 Dutch tweets. No tweets expressing embarrassment, guilt or surprise were found in the Dutch gold standard corpus. Moreover, nine tweets were labelled as other. Two of these tweets expressed relief, three tweets expressed accusation, one tweet was supportive, two tweets conveyed emotion, and another tweet was labelled as expressing admiration.

Table 22: Occurrence emotion classes in Dutch gold standard corpus Emotion class Number of tweets Anger 15 Fear 7 Apprehension 8 Confusion 10 Contempt 17 Disgust 15 Embarrassment 0 Guilt 0 Sadness 35 Surprise 0 Sympathy 14 Other 9

For each emotion class, an example of a tweet conveying this emotion is provided in table 23.

Table 23: Examples of Dutch tweets expressing emotion Tweet Sentiment label Emotion label Wat een laffe teringlijer ben je dan zeg Negative Anger #GermanWingsCrash Ik vond vliegen al niet relaxed , maar met al die rampen Negative Fear durf ik nooit meer #GermanWingsCrash #vliegangstig @SofieGeyskens Ja echt ineens héél dichtbij hé . Mijn Negative Apprehension kInderen vliegen altijd met #Germanwings naar #Verona . No big deal , tot vandaag . @RuudHteB hoe kan zoiets ? Negative Confusion Nieuws brengen om nieuws te brengen..."We zijn nog Negative Contempt onderweg op een klein wegje " #GermanWingsCrash #hetjournaal zullen ze het dan nooit leren Zullen we even kappen met die foto van Negative Disgust @Maesi_Aregger in @DwarsdrVlaander ? Samen met die #GermanWingsCrash word ik een beetje onpasselijk.. Pffff elke keer als ik beelden zie van de Negative Sadness #GermanWingsCrash krijg ik tranen in mijn ogen #heftig #verdrietig veel sterkte voor de nabestaanden #Germanwings Positive Sympathy #GermanWingsCrash Zeker omdat ik binnenkort zelf ga vliegen ben ik hoe Positive Other – relief erg het ook is . Blij dat het geen technisch makement was . #GermanWingsCrash Men spreekt van meenemen in de dood maar voor mij is Negative Other - het moord #GermanWingsCrash accusation verstandig : @Fly_Norwegian past per direct beleid Positive Other - cockpit aan na #GermanWingsCrash supportive Wat een emotionele afsluiting van lieve Marianne Positive Other - @EversStaatOp538 met daarna @borsato , emotion mooi....#GermanWingsCrash Bijzondere helden , die RI collega's ; al 150 unieke Positive Other - DNA profielen , zo kort na de #Germanwingscrash admiration Maar wat een verdrietig contrast met #MH17

4.3.3 Qualitative discussion

Many previous studies focussed on the automatic analysis of sentiment, but research in order to understand emotions expressed in crisis-related tweets, on the other hand, has been relatively unexplored. As already mentioned in section 2.3.2 of this paper, emotion analysis is even more challenging than sentiment analysis because of the fact that it is a multiclass classification problem instead of a 3-class classification problem. 44

Despite the fact that some systems for the automatic analysis of emotions have been created, it still remains difficult to classify emotional content in crisis-related tweets. On the basis of the two corpora of crisis-related tweets that were created for this paper’s study, we will now make some suggestions of elements that occur in our two corpora that could be important for the automatic detection of emotions in crisis-related tweets.

First, we found that hashtags could be used in order to determine the emotional content of a tweet, for example the hashtag ‘#verdrietig’ exhibits negative sentiment and sadness. Moreover, exclamation marks, question marks and capital letters were often used to intensify an emotion. Finally, we found some tweets in which emoticons could be used to determine the emotional content of the tweet, e.g. ‘:-(‘ exhibits negative sentiment and sadness. A few examples of tweets containing hashtags, exclamation marks, question marks, capital letters or emoticons are represented in table 24.

Table 24: Elements that could help to determine emotional content Tweet Sentiment label Emotion label Eigenlijk is die Co . Pilot ook een terrorist ! ! Neemt ff Negative Anger 150 psg mee die gek #sick #4U9525 #A320 #GermanWingsCrash #Germanwings #GermanWingsCrash geen woorden voor ..... Negative Sadness #verdrietig #Germanwings heel veel sterkte familie van Positive Sympathy omstandelingen ! #sterkte #gecondoleerd #Germanwings #GermanWingsCrash #crashA320 The killer pilot should not be allowed 2have a grave!!! Negative Anger #GermanWingsCrash I'm sorry for all the life lost for pain the monster created! Overal betuigingen aan de nabestaanden van Negative Anger passagiers...en nabestaanden van de piloot misschien ? ? Ni hun schuld zeneuj ! #GermanWingsCrash #GermanWingsCrash C'mon this was terrorism FFS! Negative Anger ik krijg kippenvel als ik de berichten hoor over Negative Disgust #GermanWingsCrash - weten dat je al die mensen meeneemt in jouw ondergang AFSCHUWELIJK a grt loss #GermanWingsCrash â€¦ thoughts with family Negative Sadness :( #RIP Ik zat op dezelfde dag in een vliegtuig , van Dubai naar Negative Sadness Amsterdam . Komt echt neer op stomme pech . #GermanWingsCrash Gekke wereld : -(

In addition to hashtags, exclamation marks, question marks, capital letters, and emoticons, content words are of the utmost importance for the automatic detection of emotions in crisis- 45 related tweets. Strapparava et al. (2008) for example, exploited the use of words in a text, and particularly their co-occurrence with words having an explicit emotional meaning, in order to recognize emotions in that text. Furthermore, it could be very interesting to detect the target of a particular emotion. Due to this information, organizations would for example be able to know whether Twitters users are blaming the organization for what happened or if they are blaming someone or something else.

5. CONCLUSION

The main goal of this study was to investigate the feasibility of automatically detecting crisis- related emotions on Twitter. Furthermore, we also tried to find an answer to the following questions. What are the most frequently used words in the tweets and do the moments on which Germanwings or Lufthansa communicated have an impact on the contents of the tweets? Are there differences between the system’s predictions and the gold standard annotations for sentiment and what did the system do right or wrong? And finally: What emotions did the tweets express and what elements occurring in the tweets in our corpus could help to automatically detect the emotional content of these tweets.

In order to find an answer to these research questions, two corpora of respectively English and Dutch tweets posted during the two weeks after the crash of Germanwings Flight 9525 were created. For each corpus, 200 tweets were both manually and automatically labelled as positive, negative or neutral. In addition, we manually determined the emotional content of these tweets. However, one limitation of this paper’s study should be highlighted. In most studies, only tweets for which a good inter-annotator agreement was obtained, were utilized in experiments. In our study, on the other hand, only one person labelled the tweets as positive, negative or neutral, and tagged the emotional content of the tweets. It is possible that, if the tweets would have been labelled by more than one annotator, the results would be slightly different.

First, it was found that the tweets posted by Lufthansa and Germanwings in response to the crash focussed mainly on supporting the victims’ relatives and friends. As a result, Germanwings and Lufthansa efficiently reached their key stakeholders. By determining the most frequently used words in the tweets, we found that the moment on which Lufthansa and Germanwings communicated that the co-pilot deliberately crashed the aircraft, was the only 46 moment that seemed to have an impact on the contents of the tweets. In addition, we discovered that the negative emotions expressed by the Twitter users related to the co-pilot, and not to the organization. As a consequence, the reputational damage for both Germanwings and Lufthansa resulting from the crash is only minor.

Second, it was discovered that the accuracy of the SemEval pipeline (63%) used for the sentiment analysis of the English tweets was better than the accuracy of the system based on the Duoman and Pattern sentiment lexicons (54%) used for the sentiment analysis of the Dutch tweets. The Dutch system, and in particular the English system, performed well with regard to the negative class label. This is a significant advantage, since the detection of negative emotions is highly important in crisis situations. Furthermore, 126 out of the 200 selected English tweets, and 107 out of the 200 selected Dutch tweets were given the same label both manually and automatically. The mistakes made by both systems could be divided into four types of mistakes. Three of these types of mistakes were the same for both the English and the Dutch system.

Third, we found that sympathy and anger were the most frequently expressed emotions in the English gold standard corpus. In the Dutch gold standard corpus, sadness and contempt were the most frequently conveyed emotions. On the basis of the two corpora of crisis-related tweets that were created for this paper’s study, we discovered that hashtags, exclamation marks, question marks, capital letters and emoticons were often used to intensify an emotion. As a result, these elements could be important for the automatic detection of emotions in crisis-related tweets.

It can be concluded that the detection of negative tweets that are posted about an organization could lead to less reputational damage for that organization. Furthermore, we can conclude that automatic systems can help to enable the automatic detection of emotions: first through sentiment analysis and then through a more fine-grained detection of emotions. However, it should not be surprising that not all tweets that are relevant for decision-making in crisis management can yet be automatically detected, since it is a very challenging task. As already mentioned in section 2.3 of the theoretical framework of this paper, the results of present research are already auspicious and it is likely that future research will achieve even more promising results.

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7. APPENDIX 7.1 TWEETS POSTED BY THE ORGANIZATION

7.1.1 Tweets posted by Germanwings

03:52 - 24 mrt. 2015 INFO: We have recently become aware of media reports speculating on an incident though we still do not have any own confirmed information... 03:53 - 24 mrt. 2015 ... As soon as definite information is available, we shall inform the media immediately ... 03:53 - 24 mrt. 2015 ... Please monitor our website http://bit.ly/1eVcii for periodic updates.

06:28 - 24 mrt. 2015 The toll-free number 0800 11 33 55 77 (900 808 890 from Spain) is available to all the families of the passengers involved for assistance. 08:55 - 24 mrt. 2015 We must confirm to our deepest regret that Germanwings Flight 4U 9525 from Barcelona to Düsseldorf has suffered an accident over the 1/5 08:56 - 24 mrt. 2015 French Alps. The flight was being operated with an Airbus A320 aircraft, and was carrying 144 passengers and six crew members. 2/5 08:56 - 24 mrt. 2015 Lufthansa and Germanwings have established a telephone hotline. The toll- free 0800 11 33 55 77 number is available to all the families 3/5 08:56 - 24 mrt. 2015 of the passengers involved for care and assistance. Everyone at Germanwings and Lufthansa is deeply shocked and saddened 4/5 08:56 - 24 mrt. 2015 by these events. Our thoughts and prayers are with the families and friends of the passengers and crew members. 5/5 13:36 - 25 mrt. 2015 Lufthansa offers special flights to Marseille for the next of kin of flight 4U 9525 passengers http://germanwin.gs/1wgfFw3 16:21 - 25 mrt. 2015 Daily Summary 25 March 2015/ Germanwings today focused mainly on supporting the relatives and dependents ... http://germanwin.gs/1wgfFw3 06:09 - 26 mrt. 2015 We are shaken by the upsetting statements of the French authorities. 1/3

06:09 - 26 mrt. 2015 Our thoughts and prayers continue to be with the families and friends of the victims. 2/3 06:10 - 26 mrt. 2015 The next press conference will take place this afternoon at 2.30 pm (German time). 3/3 12:15 - 26 mrt. 2015 We are horrified to discover today that the aircraft that crashed in the south of France appears to … http://germanwin.gs/1wgfFw3 14:00 - 26 mrt. 2015 Daily summary 26 March 2015 http://germanwin.gs/1wgfFw3 #indeepsorrow 01:49 - 27 mrt. 2015 Germanwings sets up Family Assistance Center in Marseille: http://germanwin.gs/1wgfFw3 #indeepsorrow 03:00 - 27 mrt. 2015 “Our focus in these darkest hours is to provide assistance to the families & friends of the victims of flight 4U9525” /Thomas Winkelmann 03:03 - 27 mrt. 2015 "The suffering and pain this catastrophe has caused is immeasurable. No words can express it and no amount of consolation is sufficient" 1/2 03:05 - 27 mrt. 2015 "... but we want to be there for visiting family members and friends if our support is desired.” /Thomas Winkelmann 2/2 07:16 - 27 mrt. 2015 Lufthansa Group further refines its safety structures. Group member airlines to adopt “rule of two” http://germanwin.gs/1wgfFw3 08:09 - 27 mrt. 2015 Co-pilot of Germanwings flight #4U9525: Sick note was not submitted to Germanwings http://germanwin.gs/1wgfFw3 02:53 - 28 mrt. 2015 #indeepsorrow

10:13 - 28 mrt. 2015 Family and friends of the victims of Flight 4U 9525 request that the media 51

show restraint ... http://germanwin.gs/1wgfFw3 06:08 - 31 mrt. 2015 A video message from our CEO Thomas Winkelmann http://germanwin.gs/1NyYCdy 01:22 - 3 apr. 2015 @CraigMcCole1 Can you send me your Flight details via E-mail to [email protected]? I will have a look for you. Greetings RS

7.1.2 Tweets posted by Lufthansa

04:27 - 24 mrt. 2015 "We do not yet know what has happened to flight 4U 9525. My deepest sympathy goes to the families and friends of our passengers and crew 1/2 04:28 - 24 mrt. 2015 "...on 4U 9525. If our fears are confirmed, this is a dark day for Lufthansa. We hope to find survivors.“ Carsten Spohr 2/2 05:48 - 24 mrt. 2015 We must confirm to our deepest regret that Germanwings Flight 4U 9525 from Barcelona to Düsseldorf has suffered an accident over the 1/5 05:48 - 24 mrt. 2015 French Alps. The flight was being operated with an Airbus A320 aircraft, and was carrying 144 passengers and six crew members. 2/5 05:48 - 24 mrt. 2015 Lufthansa and Germanwings have established a telephone hotline. The toll- free 0800 11 33 55 77 number is available to all the families 3/5 05:49 - 24 mrt. 2015 of the passengers involved for care and assistance. Everyone at Germanwings and Lufthansa is deeply shocked and saddened by these events.4/5 05:49 - 24 mrt. 2015 Our thoughts and prayers are with the families and friends of the passengers and crew members. 5/5 08:22 - 24 mrt. 2015 Lufthansa CEO Carsten Spohr and representatives of the German government are on their way to France. An evening press release is planned. 00:39 - 25 mrt. 2015 "Seeing the site of the accident was harrowing. We are in deep mourning. Our thoughts are with the relatives of the victims.“ 1/3 00:40 - 25 mrt. 2015 "Germanwings and Lufthansa will do everything in our power to help in an uncomplicated and timely manner.“ 2/3 00:41 - 25 mrt. 2015 "We will enable the relatives to grieve on site as soon as possible.“ - Carsten Spohr 3/3 02:49 - 25 mrt. 2015 Lufthansa and Germanwings are calling for a minute’s silence today at 10.53 a.m. to commemorate the victims of 4U9525. #indeepsorrow 05:18 - 25 mrt. 2015 A video message from our CEO Carsten Spohr. #indeepsorrow http://t.lh.com/gw9z 06:57 - 25 mrt. 2015 "What was important to us yesterday now seems irrelevant in comparison." 1/5 06:59 - 25 mrt. 2015 "We cannot comprehend how a technically flawless airplane steered by two experienced pilots… 2/5 07:00 - 25 mrt. 2015 "...could encounter such a situation at cruising altitude. All of us at Lufthansa are working to ensure.." 3/5 07:00 - 25 mrt. 2015 that such an incident will never occur again.We cannot believe that this has happened. We are doing everything to support the families." 4/5 07:01 - 25 mrt. 2015 "Two special flights for families and friends of the victims will fly to France tomorrow." - Carsten Spohr 12:44 - 25 mrt. 2015 LH offers special flights to Marseille for the next of kin of #4U9525 passengers from BCN and DUS #indeepsorrow http://t.lh.com/fjTj 02:37 - 26 mrt. 2015 D-AIDI from DUS to MRS took off at 0920, expected arrival 1045. Flight no LH 9886. On board are 50 family members & a support team. 06:03 - 26 mrt. 2015 We are shaken by the upsetting statements of the French authorities. Our thoughts and prayers continue to be with the families... 1/2 06:04 - 26 mrt. 2015 ... and friends of the victims. The next press conference will take place this afternoon at 2.30 pm (German time). 2/2 52

06:49 - 27 mrt. 2015 Airlines of Lufthansa Group adopt „rule of two“ in aircraft cockpits. http://ti.lh.com/PyHd 15:12 - 27 mrt. 2015 Lufthansa in mourning. In a show of solidarity for those lost, today our employee magazine is published in black. 06:09 - 1 apr. 2015 Lufthansa and Germanwings thank recovery workers and caregivers: http://ti.lh.com/afpd

7.2 CORPUS OF 200 ENGLISH TWEETS

Prediction Gold Tweet Emotion negative negative The Daily Mail coverage of the #GermanWingsCrash has been disgust repugnant. Headlines like 'how the nazis led to killer co-pilot' help no one. negative negative the whole #GermanWingsCrash honestly is reminding me of sadness the whole #secretarymarsh crash in #madamsecretary. It's so sad. too many bad things neutral neutral Also, here's my piece from Friday on mental health stigma in the wake of #GermanWingsCrash https://www.holyrood.com/articles/comment/lessons-lubitz- why-blame-game-should-carry-health-warning â€¦ neutral negative RT @AnonFatCat: If this man was Muslim he would be called contempt a terrorist, without a doubt! #Germanwingscrash #openyoureyes http://t.co/GkjfrXTâ€¦ negative neutral #GermanWingsCrash: when the solution to an issue creates a major problem. As a consequente Privacy and security basics need to be revisited neutral neutral #AndreasLubitz visited Israel on more than 10 separate occasions in the last year alone. #Germanwings #GermanWingsCrash #ShabbatShalom positive positive Sending good vibes - #GermanWingsCrash - May they all sympathy make their way home peacefully. negative negative If the pilot used an axe on the door, whats to stop a terrorist? apprehens What other potential weapons r laying round on flights? ion #GermanWingsCrash neutral positive #RIPGermanwingsVictims: Co-pilot acted deliberately in crash sympathy - http://edition.cnn.com/2015/03/26/europe/france- germanwings-plane-crash-main/index.html â€¦ #Germanwings #GermanWingsCrash negative negative A British friend had joked with me that Indians don't consider contempt mental illness as a problem or illness. So does the world #GermanWingsCrash negative negative Maybe yes, #AndreasLubitz was too crazy, too depressed, too contempt blind; but the main responsible is just the company #GermanWingsCrash negative positive Ever tried to put yourself in the situation of such people as the sympathy ones who lost their dear ones as a consequence of that #GermanwingsCrash? neutral neutral Talking to Cpt. Florian Austruy of CRS high mountain recovery team in Seyne-les-Alpes close to #Germanwingscrash @cnn positive neutral One solution for #GermanWingsCrash is having portable toilet in cockpit 53 neutral positive Boarding an Airbus bound for the French Alps; taking a sympathy minute for silent thought about the #GermanWingsCrash innocents. ðŸ˜” RIP negative neutral Letter found in home of Germanwings co-pilot wasn't received by employer. Kept secret! #flight4U9525 #GermanWingsCrash #France #Apls neutral negative @huntersjames @MaraWritesStuff that much of the coverage contempt of #GermanWingsCrash was ill informed and simplistic with regards to #mentalhealth neutral neutral Loucura.... "@euronews: #BREAKING #GermanWingsCrash:Second black box confirms â€œvoluntary actionâ€• of co-pilot" negative negative I'm thinking this attn on #AndreasLubitz and the Other #GermanWingsCrash is overdone. It's tragic & I would rather (critical) see the focus on the victims. neutral neutral What really happened to the #Germanwings plane? http://www.blogtalkradio.com/sottradionetwork/2015/04/05/be hind-the-headlines-what-really-happened-to-the-germanwings- planeÂ â€¦ #GermanWingsCrash positive neutral Sun shining on the Alps and the #GermanWingsCrash site - all good for search mission. Still no sign of data recorder. negative negative sb plz open that fucking door,and take that psycho away anger #IranTalks #GermanWingsCrash @LaurentFabius neutral negative Blown away. Pilot locked out of the #Germanwings surprise cockpit?!? I thought I heard it all. #GermanWingsCrash negative negative I feel really sad for the 150 families who are suffering as a sadness result of the #GermanWingsCrash. Beyond tragic. neutral negative Question: Why do they know so much so soon? contempt #GermanWingsCrash negative positive My heart goes out to the passengers, crew members and their sympathy families of the #GermanWingsCrash :( Seems to be a horrible year for the sky ... negative neutral #Germanwingscrash Spanish students in shock at Barcelona, Families and friends were coming to terms with their grief http://ind.pn/1N5Dk94 negative negative That German pilot, there hasn't been a worst German to be in disgust the sky with since Manfred von Richthofen #Germanwings #GermanWingsCrash neutral neutral â†’ http://buff.ly/1BvsiSp #Germanwingscrash Co-Pilot in Germanwings Crash Hid Mental Illness From Employer, Authorities Say Germanwings crasâ€¦ neutral neutral Aviation expert Guy Leitch says there's no don't need to worry about the safety of SAA fleet of Airbus 320s following the #GermanWingsCrash neutral neutral If that pilot was Muslim or Black this would have been a whole different situation #GermanWingsCrash negative positive #PatrickSonderheimer You're name should go down in history sympathy for being a hero in an ever so devastating situation. #GermanWingsCrash negative negative How can a guy that's been treated for suicidal tendencies still contempt have a valid aircrew Class One medical certificate? #GermanWingsCrash neutral neutral The #epigenetics of madness/#terrorism #germanwingscrash http://fb.me/3IEp3BHIA 54 negative negative Another "insane lone wolf" DELIBERATELY n wordlessly anger smashed an Airbus carrying 150 people. Yet he's not a TERRORIST. #GermanWingsCrash neutral neutral Pilot locked out of the cockpit in #GermanWingsCrash; tried to smash door to gain access Join me at 11pm PST/1am EST for the latest details negative neutral Germans tend to go to work when sick as they fear getting axed-research today by Boris Hirsch at Royal Economic Society #GermanWingsCrash neutral neutral @WGME What happened in the final moments of the #GermanWingsCrash? Details next w/ @JeffWGME, @JanaWGME & @Courtneykabot. neutral negative #GermanWingsCrash Have the list of spanish victims been fear released.My dad and his parents are spanish and could be on that plane. i need info. negative neutral Lufthansa now says it knew of #GermanWingsCrash pilot's history of "severe depression." http://www.nytimes.com/2015/04/01/world/europe/lufthansa- germanwings-andreaslubitz.html?hp&action=click&pgtype=Homepage&module=fir st-column-region®ion=top-news&WT.nav=top-news … negative negative Pls @nytimes no more #GermanWingsCrash push sadness notifications. The devastation was more than what I thought was possible from a single being. neutral neutral Audio last 60 seconds from flight deck http://youtu.be/_LnFB9vStH8 #Germanwings #GermanWingsCrash #france #spain #germany #GermanyWings negative negative Crazy white man but definitely #NotATerrorist contempt #GermanWingsCrash. negative positive Too bad that he's not getting more recognition than he sympathy deserves. R.I.P. Captain Patrick Sondheimer #GermanWingsCrash neutral neutral Follow-Up: Germanwings Pilot Had Suicidal Tendencies http://ow.ly/LaF26Â #GermanWingsCrash #AndreasLubitz negative negative The media's full of whitewashed, brainwashed tools -- call him contempt a terrorist if he's a terrorist, ya ninnies... #GermanWingsCrash #Germanwings negative positive sending my prayers out, so sorry for the people on board, sympathy especially the school group out for an exchange program #GermanWingsCrash negative negative #GermanWingsCrash ...too many leaks...investigators not contempt doing a very good job controlling information. Disappointing!! negative positive #Germanwings #GermanWingsCrash feel so sorry for those sympathy poor families that lost their relatives because of #Lubitz suicide attempt #RIP neutral positive #Rip ðŸ˜”ðŸ˜”ðŸ˜ðŸ˜ #GermanWingsCrash #4U9525 sympathy negative negative #whys why is it difficult 4Europe 2brand d contempt #GermanWingsCrash pilot a TERRORIST? @BBC_WHYS what's more terrifying than crashing a plane? negative negative #GermanWingsCrash makes me feel sad&angry thatthe airline anger whoever people didnt have better security or background knowledge of the copilot 55 negative positive So many planes coming down lately.... Respect and sympathy condolences to the relatives of the crashed plane victims #Germanwings #Germanwingscrash neutral neutral @DioscorusBoles #GermanwingsCrash Pilot's Ex-Girlfriend Says He Planned A 'Big Gesture' -- she left him bcz afraid. http://youtu.be/BrPlCjTA8Qk?a neutral neutral A string of air disasters in 2014 2015 http://f24.my/1HzBhKh #4U9525 #Germanwings #GermanWingsCrash positive positive The name to remember is Captain Patrick Sondheimer. The sympathy other one doesn't get to have his name remembered. #GermanWingsCrash #Germanwings negative negative Debbie Downer meets weekend update this lady is trying hard contempt to sensationalize this tragic event #GermanWingsCrash negative negative This documentary about Andreas Lubitz is making my blood anger boil #GermanWingsCrash neutral neutral ZAKA Rescue Team Heading To Germanwings Crash Site http://bit.ly/1bJQF9M #GermanWingsCrash @zakarescue negative negative @CNN That reporter Puthenpurackal talking about the copilot anger ex girlfriend "Maria" sounds like a bunch of bullshit!#GermanWingsCrash #CNN positive positive Thank you Captain Patrick Sondheimer. Thatâ€™s the sympathy pilotâ€™s name you want to remember. http://www.mamamia.com.au/lifestyle/patrick-sondheimer â€¦ #Germanwings #GermanWingsCrash neutral neutral Alps #GermanWingsCrash pilot had suicidal tendencies in past: prosecutors (http://goo.gl/6bGWOPÂ ) negative neutral #GermanWingsCrash #Germanwings #AirbusCrash #Airbus pilot likely to have committed suicide http://goo.gl/MRVcwJ negative negative #GermanWingsCrash So did Lubitz just Snap? I don't think so. contempt @DrDrewHLN @ChrisCuomo @dateline_keith @JoshMankiewicz neutral neutral Father of a #GermanWingsCrash victim calls for increased airline transparency, via @bbc; curated by @Asambiz | http://www.bbc.com/news/uk-32101305 â€¦ negative negative #GermanWingsCrash because the co pilot is not #muslim you anger will creat many excuses! He was #murder #terrorist no doubt! negative negative Praying for the families and friends of all who lost their lives sadness in the German plane crash today. So sad. #GermanWingsCrash #crashA320 negative negative Thanks to the evil #GermanWingsCrash I'm officially scared to fear fly, they should allow us to talk and meet our piolt incase. neutral neutral #GermanWingsCrash victim compensation: how much is a life worth?http://www.telegraph.co.uk/news/worldnews/germanwi ngs-plane-crash/11510573/Germanwings-victim- compensation-how-much-is-a-life-worth.htmlÂ â€¦ Ð• Ð² #Ð Ð¤ Ð¼Ð¸Ð»Ð»Ð¸Ð¾Ð½ Ñ€ÑƒÐ±Ð»ÐµÐ¹?! neutral neutral Rule of two in cockpit, soon be the norm http://www.aeronewstv.com/en/transport/security/2440-rule- of-two-in-cockpit-soon-be-the-norm.html â€¦ #security #aviation #aviationgeek #GermanWingsCrash @FAASafetyBrief @EASA negative neutral Many people talk about a variety of visual problems linked to antidepressants... - See more at: http://wp.rxisk.org/keeping- an-eye-on-the-ball-visual-problems-on- 56

ssris/#sthash.DjimTFMt.dpuf â€¦ #GermanWingsCrash negative negative is wondering if #GermanWingsCrash could hav been avoided? anger Why had #doctor not told employer directly that #copilot couldn't attend work? negative negative Are post-9/11 security measures rendering airplane cockpits apprehens too inaccessible? #GermanWingsCrash http://bit.ly/1D2Hduk ion neutral neutral Two opera singers are dead in #GermanWingsCrash http://slippedisc.com/2015/03/ via @RoundupReader negative positive My sympathies to those who lost loved ones on sympathy #GermanWingsCrash. A320 is still one of the safest aircraft around. Won't stop me from flying. neutral neutral #GermanWingsCrash pilot captain tried to break down cockpit door with an axe http://toyeenbalogun.com/news- germanwings-pilot-captain-tried-to-break-down-cockpit-door- with-an-axe â€¦ #U49525 #Dubai #MyDubai #UAE #Spain #US positive neutral Why Israeli recovery teams are proving to be invaluable at the #GermanWingsCrash site http://tlv1.fm/?p=36005 negative negative As if flying wasn't scary already... now #GermanWingsCrash fear prayers for the families neutral neutral RT sciam: Search teams probe the wreckage of a German Airbus in the French alps: http://ow.ly/KNU3Y #science #GermanWingsCrash negative neutral The law should require doctors who treat pilots b obligated to contact the airline if theres problm w/ the pilots health. #GermanWingsCrash negative negative #GermanWingsCrash conspiracy theorists preying on fragile anger minded ppl for their own benefit. Exactly like organized religion. Damn shame. neutral neutral #Â» #Germanwings #crash #Germanwingscrash #Germanwings #Crash #Raises #Questions #Aboutâ€¦ http://bit.ly/1Ic1paU #Germanwingscrash |B neutral neutral International airlines are changing cockpit procedures in light of #GermanWingsCrash. Read the story http://ow.ly/KTuMJ #HotOffThePress negative neutral Weather doesn't look great for search efforts Wednesday. Rain to snow and even fog. #Germanwings #GermanWingsCrash neutral negative The #GermanWingsCrash brings back chilling memories of sadness Robert Brown: http://www.getreading.co.uk/news/local- news/alps-air-crash-brings-back-8952557Â â€¦ neutral neutral Muslims praising #AndreasLubitz #GermanWingsCrash http://ln.is/www.jewsnews.co.il/2/7LNWK â€¦ negative neutral Germanwings Pilot Was Locked Out of Cockpit Before Crash mobile.nytimes.com/2015/03/26/world/europe/germanwings- airbus-crash.html?referrer= â€¦ #GermanWingsCrash negative negative And don't tell me they all follow the same rules! They are anger suppose to follow the same rules. #GermanWingsCrash negative neutral The copilot was muslim. One look at the Facebook page should tell. Nothing should be ruled out #GermanWingsCrash positive positive My heart goes out to everyone who is even remotely affected sympathy by this #GermanWingsCrash negative positive Can't believe there was another plane crash?!? Deepest sympathy sympathies to the victims' families and friends 57

#GermanWingsCrash ðŸ’• negative neutral #Germanwings tragedy highlights stigma surrounding #mentalillness â€” http://samadimd.com/latesthealth/germanwings-tragedy- highlights-stigma-surrounding-mental-illnessÂ â€¦ #GermanWingsCrash negative neutral Investigators now believe the #GermanWingsCrash was deliberate. Via Bloomberg: http://www.carriermanagement.com/news/2015/03/26/137336. htm â€¦ neutral negative Where Was God When #Germanwings 9525 Crashed? anger http://wp.me/p4k0nJ-cfI via @RevRichardOBX #Germanwingscrash @UMInsight positive positive To the people aboard #Airbus320 may you rest in peace. To sympathy the families our prayers are with youâ•¤ï¸• #GermanWingsCrash neutral neutral Prosecutor denies reports of #cellphone #video from inside #GermanWingsCrash plane http://cnn.it/1I3RZld neutral negative Jan Cocheret, Dutch Emirates pilot, predicted a few weeks ago fear that something like this could happen. Eerie. #GermanWingsCrash negative positive #GermanWingsCrash tragic news, my heart and thoughts go sympathy out to all the families. neutral neutral @weartv New details in the investigation of the #GermanWingsCrash. What was happening in the cockpit next w/ @WEARJared on #3itm. negative negative @AusNewsNetwork heartbreaking RIP to those people xx sadness #GermanWingsCrash positive positive Pray for #GermanWingsCrash May all the passengers and sympathy crews be fine. :( neutral neutral German regulator says unaware of co-pilotâ€™s depression #GermanWingsCrash http://bit.ly/1C3GseD negative neutral #GermanWingsCrash Reports indicate a loss of pressurisation - 24 year old plane flew on autopilot straight down at 4000 ft a minute reported neutral negative @TODAYshow Saturday Nite Live went to far with the anger #GermanWingsCrash I'm appalled!!! negative negative Why the pilot and not the copilot? There are any pic of the contempt crash !! Just sound.Everybody lie #Germanwings #GermanWingsCrash #4U9525 negative negative Utter bullshit. I'm not buying suicide at all. That video is anger nonsense #GermanWingsCrash neutral neutral Prosecutors: #GermanWingsCrash co-pilot researched suicide methods, cockpit security http://k2ne.ws/1NE31flÂ #LiveOnK2 negative negative What was the religion of the last Inspector of that plane on disgust Monday? SERIOUSLY? Sad to hear about another plane crash. #GermanWingsCrash negative negative a grt loss #GermanWingsCrash â€¦ thoughts with family :( sadness #RIP negative neutral Suicidal tendencies has nothing whatsoever to do with homicidal ideation. #justsaying #GermanWingsCrash #MentalHealthAwareness negative neutral French authorities said there have been no intact bodies found 58

at the #GermanwingsCrash site. 400 to 600 body parts recovered so far neutral positive R.I.P. #4U9525 #GermanWingsCrash sympathy negative positive Our thoughts and prayers go out to those who lost loved ones sympathy in the #GermanWingsCrash May God be with you in these hard times. negative negative #GermanWingsCrash What the hell happened to the days anger when you had a navigator in the cockpit? Now he's obsolete because of fly-by-wire? neutral neutral .#GermanWingsCrash causes weakest box office session in Germany so far this year. @Screendaily: http://www.screendaily.com/box-office/cinderella-crosses- 300m-global/5085831.article â€¦ negative negative so sad about the #GermanWingsCrash can't believe it was sadness intentional neutral negative You kill150 people in cold blood & the media print a picture of anger you doing charity work? #ThePerksOfBeingWhite #GermanWingsCrash neutral neutral #Germanwings #GermanWingsCrash VIDEO OF THE LAST MOMENTS OF THE FLIGHT #GermanNuevoVideo http://m.liveleak.com/view?i=aad_1427916293Â â€¦ negative neutral Suicide by Plane Crash Is Rare but Not Without Precedent #GermanWingsCrash nyti.ms/1EZmy7N @nytimes neutral neutral Read insightful analysis of #GermanWingsCrash aftermath by @SchulichSchool Profs @ethicscrane & @dirkmatten http:http://craneandmatten.blogspot.ca/2015/03/germanwings- 4u9525-art-of-asking-right.htmlÂ â€¦ negative negative The killer pilot should not be allowed 2have a grave!!! anger #GermanWingsCrash I'm sorry for all the life lost for pain the monster created! neutral neutral French investigators: Co-pilot accelerated plane on descent: http://bit.ly/1EPLukYÂ #GermanWingsCrash negative negative #GermanwingsCrash The co-pilot knew he wasn't fit to fly but contempt went ahead & did it anyway, there was clear risk of/intent to murder - shocking negative neutral Pilots secretly breathing a sigh of relief as the #GermanWingsCrash just killed the idea of a #SinglePilotCockpit negative negative Why is everyone buying this psychosomatic disorder for the anger Germanwings co-pilot? They have no EVIDENCE. #GermanWingsCrash neutral neutral GERMANWINGS UPDATE: CO-PILOT LIED TO DOCS http://bit.ly/1xF5sMzÂ #Germanwings #GermanWingsCrash #AndreasLubitz #flight9525 #Copilot #Germany neutral neutral Were was the torn note from the doctor of #Lubitz found? In the waste basket? #GermanWingsCrash neutral neutral Germanwings Crash Raises Questions About Shifting Ideas of Pilot Fitness #germanwingscrash http://bit.ly/19hW06P positive neutral Lufthansa cancels its 60 year anniversary celebrations originally planned on April 15 #GermanWingsCrash negative negative He locked out the other pilot. Ugh we live in a sick world. disgust #GermanWingsCrash negative negative So sorry to hear about the #GermanWingsCrash really no sadness 59

adjectives to explain that sorrow.& there were totally 144 people including 2 babies. negative negative I'm sorry I'm struggling here. Ever since the anger #GermanWingsCrash EVERY ANGLO-AMERICAN NEWS OUTLET HAS TRIED TO HUMANISE A WHITE TERRORIST. positive negative @prrrsiankitten Wow, that's really offensive to make fun of anger #GermanWingsCrash . @Lufthansa_DE @DerSPIEGEL negative negative Anybody looking to see if this dude has ties to ISIS contempt #GermanWingsCrash looks like a suicide bomber to me...maybe he was promised 7 virgins neutral neutral #GermanWingsCrash France officially requests assistance from FBI in crash investigation neutral neutral #TyphoonMaysak #alshabab #GermanWingsCrash #FLIGHTAC624 #4u9525 #FLIGHT587 #happyeaster #Kenya #KenyaAttack #easter negative neutral Psychiatry Could Not Have Prevented the Germanwings Disaster http://nyr.kr/1GUFQO4Â #Germanwings #GermanWingsCrash #AndreasLubitz #Lubitz negative negative #GermanWingsCrash is a tragedy. As for what may have sadness caused it, either rapid depressurization, a fire, or terrorism by religious radicals negative negative Reading new reports of the #GermanWingsCrash like wtf anger man.that sick bastard to crash the plane full of innocent ppl and kids. #MyheartBreaks neutral neutral Sources tell me there were 2 Iranians on the #GermanWingsCrash flight @seanhannity who were they? neutral neutral #GermanWingsCrash | Father of one of 150 victims says pilot welfare must be taken into greater consideration. negative positive My thoughts go out to the families of the victims sympathy #GermanWingsCrash negative negative Why let a man fly a plane when they know this seriously anger #GermanWingsCrash negative neutral #AndreasLubitz and preventing another #GermanWingsCrash: 1. Police found doctorsâ€™ notes torn up and thrown (cont) http://tl.gd/n_1slhe93 negative negative I wonder if that co-pilot was committing suicide since the pilot confusion was locked out of the cockpit & was trying to get in #GermanWingsCrash ðŸ˜” neutral neutral Killer pilot spent 18 months in treatment for mental health issues during training, @thetimes reports #GermanWingsCrash neutral negative @thedailybeast compares Andreas Lubitz disgust #GermanWingsCrash copilot to a god. Are you kidding? http://www.thedailybeast.com/articles/2015/03/27/pilot-s- dream-becomes-world-s- nightmare.html?source=TDB&via=FB_Page â€¦ negative neutral Prosecutors say #Germanwings co-pilot was researching suicide methods, cockpit door security http://cir.ca/s/dAKBÂ #GermanWingsCrash positive negative Should I be worried or reassured by the #GermanWingsCrash? confusion It is good to know that the doors won't open from the outside...but then again... negative neutral Several Germanwings flights cancelled after crew refused to fly | via @Telegraph 60

http://www.telegraph.co.uk/news/worldnews/europe/germany/ 11493388/Several-Germanwings-flights-cancelled-after-crew- refused-to-fly.html â€¦ #GermanWingsCrash neutral neutral Police carry documents out of 2 properties in hunt for Andreas Lubitz clues http://huff.to/1CsJkFF #GermanWingsCrash negative negative Had it been brown skin. They would have very easily declared contempt him #terrorist by now. #Germanwings #GermanWingsCrash positive neutral @lufthansa #GermanWingsCrash the cockpit door should be made in such a way that it can be locked & unlocked from both sides neutral neutral #lowcost airlines are high stress for #pilot http://www.business-standard.com/article/opinion/adam- minter-low-cost-airlines-are-high-stress-for-pilots- 115033000982_1.htmlÂ â€¦ #Germanwings #GermanWingsCrash neutral neutral Voice recordings show one pilot locked out cockpit http://bit.ly/18Zec4M #Germanwings #GermanWingsCrash #blackbox neutral neutral Helicopter Video Shows Germanwings Crash Site [ #Germanwings Plane Crash] #GermanwingsCrash 5stars.website/aerial-video-helicopter-video-shows- germanwings-crash-site-germanwings-plane-crash/ â€¦ neutral negative How much stigma will there be re mental illness after the apprehens #GermanWingsCrash? #Germanwings ion negative negative If the #GermanWingsCrash co-pilot had any remote Muslim contempt background, I'm sure this macabre incident would have been branded "terrorism" neutral neutral New developments in #GermanWingsCrash. #blackbox indicates pilot was locked out of cockpit. http://cbsn.ws/1FWxhBk negative negative White privilage my foot. Dude killed people and you brand anger him depressed? Thats terrorism! If he were brown or black ..... #GermanWingsCrash neutral neutral Vision troubles plagued #GermanWingsCrash pilot, @nyt... https://twib.in/l/gBMd4zddMbaÂ via @youthsnews #australia #news negative neutral Â» http://buff.ly/1Nni9Q8 Germanwings Crash: How Often Pilots Commit Aircraft-Assisted Suicide #Germanwingscrash 116 negative negative God bless those people who died on that flight in FranceâœˆSo sadness sad! I hope that man rots in hell! #GermanWingsCrash negative negative #GermanWingsCrash C'mon this was terrorism FFS! anger negative neutral #GermanWingsCrash The point of suicide is generally to escape pain. Mass murder is all about inflicting it on others. #stigma #depression positive negative I'm a big @onedirection fan but It's mind boggling to see contempt #ZaynMalik leaving the band getting more coverage than the #GermanWingsCrash neutral positive Please repost ðŸ™• Remember Captain #PatrickSonderheimer sympathy #Germanwings #Hero #GermanWingsCrash #4U9525 positive negative The man responsible for #GermanWingsCrash gets his wish to disgust be in headlines and we forget Capt Sondenheimer heroism. 61

http://edition.cnn.com/2015/03/30/europe/germanwings- captain-patrick-sondenheimer/index.htmlÂ â€¦ positive negative @AnonyCrypt: Fucking bottom feeders @CNN milking the anger coverage of 9525 #GermanWingsCrash crash for all its worth.Hell-Yeah! negative negative So this guy takes a picture in front of the Golden Gate contempt Bridge..The most used bridge for suicide jumps. Dude why not then? #GermanWingsCrash neutral neutral @cnn.com #GermanwingsCrash #Readiness test should be obligatory on all pre-flight crew meetings neutral neutral Kathrin Goldbach: â€˜Im pregnant with Andreas Lubitz childâ€™ http://scallywagandvagabond.com/2015/03/kathrin- goldbach-im-pregnant-with-andreas-lubitz-child/ â€¦ #Kathrin Goldbach #Andreas Lubitz fiance #germanwingscrash negative negative Watching #WildTales after #GermanWingsCrash is so ... Can't sadness even find the words. Coincidence?? positive negative @CBSThisMorning @lufthansa It's a little too late for Other Lufthansa to help the families of those who lost loved ones on (critical) the #GermanWingsCrash. neutral neutral Robin says 470 items (personal belongings) of #GermanWingsCrash victims have been recovered neutral negative @NickPatrickNYC extreme trash talk #noclass anger @MicheleBachmann #GermanWingsCrash neutral neutral Investigators have identified DNA strands from the #GermanWingsCrash victims. http://bit.ly/1HVw6Ey neutral neutral UAE airlines review policies after #Germanwings tragedy l http://www.arabiansupplychain.com/article-11093-uae- airlines-review-policies-after-germanwings-tragedy/ â€¦ #GermanWingsCrash #Aviation neutral negative The strange fast definition of a truth: usually it takes yrs of contempt investigating a planecrash, but in case of #GermanWingsCrash result in 48h ? positive negative Now the pilot got what he wanted. People will now remember disgust him as one of the biggest losers in history. #GermanWingsCrash negative negative Am I the only person that doesn't want to read the final disgust transcript from the #GermanWingsCrash? It seems somewhat morbid. negative negative What a shame to hear about another aircraft accident, makes sadness you think about all the people who take a fly everyday! #GermanWingsCrash neutral neutral Memorial Service set: 11a Apr22 for #GermanWingsCrash #Nokesville victims Yvonne,Emily #Selke @wusa9 @arlingtonchurch positive neutral Severe weather hampers rescue operation of #Germanwings plane crash. FULL REPORT: http://bit.ly/1Cpku9A #GermanWingsCrash #FloodsInSpain negative neutral #Aviation: #Austerity-mad #Germany's anti-#Labor #Lufthansa seems criminally liable in #GermanWingsCrash co-pilot's mass murder/suicide plot neutral neutral #GermanWingsCrash news conference on @GoodDayChicago right now. #fox29goodday negative negative @cnni @BBCBreaking Accidents in #aviation take a long time contempt to clarify. It seems as in #GermanWingsCrash they wanted to 62

blame someone FAST. neutral neutral A German Wings operated Airbus 320 flight crashes in Southern France #A320 #Prayers #GermanWingsCrash negative neutral #Germanwingscrash #Germanwings #crash #Germanwings #crash: #service #held #for #victim http://bit.ly/1D7xbq4 |F neutral positive Praying for the familie of the victims #GermanWingsCrash ... sympathy ðŸ™•ðŸ’” .... neutral neutral After Airline Tragedy, New Focus on #MentalHealth at Work http://www.wsj.com/articles/after-airline-tragedy-new-focus- on-mental-health-at-work-1427918253Â â€¦ #etiology #WSJ #APA #health #GermanWingsCrash #DLAKY positive positive Sending love and support to all people struggling with sympathy depression. This must be such a difficult time 4U. #GermanWingsCrash #mentalillness negative negative Imagine what would happen to Germany and its battle against apprehens #Pegida if co pilot was converted muslim.. Not good. ion #GermanWingsCrash neutral negative The French Alps should have been the view from the window sadness seat - not their grave #germanwingscrash http://cnn.it/1DAl7QaÂ @cnn neutral neutral “#GermanWingsCrash Second black box 'confirms co-pilot Andreas Lubitz acted deliberately' http://www.telegraph.co.uk/news/worldnews/germanwings- plane-crash/11513967/Second-black-box-confirms-French- Alps-crash-co-pilot-Andreas-Lubitz-acted-deliberately.html … ” negative negative Wasn't expecting that! Horrible bastard. #GermanWingsCrash disgust neutral neutral Francois Hollande and Angela Merkel arrive at Le Vernet, close to crash site. #GermanWingsCrash @Independent negative negative If you wanna kill yourself,then fucking kill yourself!why the anger fuck you took 150 ppl lives along with yours!?#GermanWingsCrash #Germanwings negative positive thoughts and prayers are with the families and the victims of sympathy the tragedy #germanwings, #GermanWingsCrash neutral neutral 'Everything pulverised' at French plane crash site - http://m.tvnz.co.nz/news/top_stories/6267155 â€¦ #GermanWingsCrash #Germanwings

7.3 CORPUS OF 200 DUTCH TWEETS

Predictio Gold Tweet Emotion n negative neutral @mvanderKist @HumbertoTan Eigenlijk zijn we het in basis eens , maar op voorhand ( zonder uitkomst onderzoek ) wordt door media gestigmatiseerd positive negative Er zijn ook mensen die als de sodemieter hun aandelen disgust #Lufthansa dumpen a.g.v. #GermanWingsCrash Niet te geloven , allemaal . negative negative Duitse schoolklas met vijftien kinderen , ook dood . Slecht sadness verhaal . #GemanWings #GermanWingsCrash negative negative Wat een laffe teringlijer ben je dan zeg #GermanWingsCrash anger 63

neutral neutral @JurrVanWessem er stond een artikel over op de #NOS-site , maar dat is er af gehaald . positive positive Wat een verschrikkelijke ramp . Alle betrokken familieleden , sympathy vrienden en kennissen van harte gecondoleerd #GermanWingsCrash negative negative Ik zat op dezelfde dag in een vliegtuig , van Dubai naar sadness Amsterdam . Komt echt neer op stomme pech . #GermanWingsCrash Gekke wereld : -( negative neutral Andreas Lubitz , de copiloot die 149 mensen dood injoeg . http://www.hln.be/hln/nl/33222/Vliegtuigcrash- Germanwings/article/detail/2265655/2015/03/26/Andreas- Lubitz-28-de-copiloot-die-149-mensen-de-dood-injoeg.dhtml ? utm_source=hootsuite&utm_medium=Social&utm_campaign= socialredactie&utm_content=nieuws ... #GermanWingsCrash positive negative Hoe kunde zoiets doen ? ! #GermanWingsCrash disgust positive negative ongelooflijk dat de namen van de piloten na 48 h. nog altijd contempt niet vrijgegeven zijn.#GermanWingsCrash neutral neutral Hier in Frankrijk wordt gemeld dat vanavond is begonnen met bergen van lichamen slachtoffers #GermanWingsCrash . #hvnl pic.twitter.com/2YBXSVO4JF positive neutral #Germanwings #GermanWingsCrash Nieuwe mokerslag voor nabestaanden als piloot #flight9525 vrijwillig liet neerstorten pic.twitter.com / Tg7rK42q5M negative negative @telegraaf @wehkamp reclame over jumpsuits voor filmpjes contempt over de ramp is nogal vreemd . Commercials uitzetten misschien ? #GermanWingsCrash negative neutral Als de analyse van Michael Persson in de @volkskrant klopt , dan zijn er bij de #GermanWingsCrash 150 mensen gedood door de computer negative negative Als de #GermanWingsCrash zelfmoord is , dan is 9/11 dat ook Other . Dit is een moordaanslag op 150 levens . #dwv (Accusatio n) neutral neutral RT : " @sanderdelang : Overzicht aantal #vliegrampen als gevolg van #zelfmoord : http://news.aviation- safety.net/2013/12/22/list-of-aircraft-accidents-caused-by- pilot-suicide/ ... #GermanWingsCrash #U49525 " positive positive Net vliegtuigreisje geboekt . Maar gedachten zijn nu bij familie sympathy en vrienden van de slachtoffers rampvliegtuig..#GermanWingsCrash negative negative Waarschijnlijk waren passagiers en crew zich dus ook bewust sadness van ongeluk . Damn . #RIP #GermanWingsCrash positive neutral @Benavra Dan is er geen ' override ' van buitenaf meer mogelijk . Bij ' onwel ' geworden kan dit nog wel . #GermanWingsCrash neutral negative Wat bezielt iemand om het leven van 150 mensen moedwillig sadness te beeindigen...te triest voor woorden #GermanWingsCrash positive positive En dan is 17 juli weer heel dichtbij . Sterkte voor alle sympathy nabestaanden en de hel waar ze in terecht zijn gekomen . #GermanWingsCrash negative negative #GermanWingsCrash gruwelijk zeg dit...... disgust negative negative Wauw . De vanzelfsprekendheid waarmee ik normaal een fear 64

vliegtuig in stap is nu eventjes weggeslagen : ( #GermanWingsCrash positive negative Helaas heeft de nieuwsgeilheid het weer gewonnen van een contempt grondig en onafhankelijk onderzoek van de feiten #GermanWingsCrash negative neutral Tweede zwarte doos gevonden ! ! #GermanWingsCrash neutral negative Ben vaak in Duitsland , maar afgelopen week zal meer sadness bijblijven dan vele andere . #GermanWingsCrash pic.twitter.com / JlrZaGr7rX negative negative #GermanWingsCrash bizar dat er nu een aantal apprehensi vliegtuigongelukken op rij gebeuren . Daarbij raakt de on vliegtuig zoek.. #patroon #weeskritisch negative negative Wat een afschuwelijk , verschrikkelijk , schokkend verhaal... sadness #GermanWingsCrash neutral negative Het begint meer en meer op een aanslag te lijken vind ik apprehensi #GermanWingsCrash on negative negative Onbegrijpelijk . Zoveel verdriet . Wat heeft die co-piloot disgust bezield ? #opzettelijk #GermanWingsCrash negative neutral Het woord is nooit gevallen . Mr wat Lubitz deed was #terrorisme . Wil een aanslag impact hb , moet ze puur destructief zijn . #GermanWingsCrash negative neutral #GermanWingsCrash geen ongeluk maar bewuste aktie copiloot . neutral neutral #GermanWingsCrash #4U9525 - Belgisch slachtoffer is Christian Driessens ( 59 ) woonachtig in Barcelona http://www.hln.be/hln/nl/33222/Vliegtuigcrash- Germanwings/article/detail/2263145/2015/03/24/Minstens- een-Belgisch-slachtoffer-aan-boord-van-rampvlucht.dhtml ... negative negative Vliegtuigramp laat me toch niet onberoerd ! Heel erg voor al sadness die families #GermanWingsCrash positive neutral #GermanWingsCrash . 2 belangrijke vragen : 1 . Waarom werd een van 2 piloten buiten gesloten ? 2 . Waarom worden namen piloten niet vrijgegeven ? neutral neutral Iemand al eens gedacht aan een wc in de cockpit ? #GermanWingsCrash positive positive Reizen hoort plezant te zijn , niet gevaarlijk met mijn hart sympathy steun ik de familieleden van de overledenen #GermanWingsCrash neutral neutral BFMTV De co piloot reageerde niet op de communicatie van de verkeerstoren . Hij ademde dus was bij... #GermanWingsCrash positive positive Mijn gedachten zijn bij de familie en vrienden van slachtoffers sympathy van de vliegramp #GermanWingsCrash . negative negative Triest . Heel erg triest . #GermanWingsCrash sadness neutral negative De wereld is om zeep ! #GermanWingsCrash #Germanwings apprehensi #suicide on positive neutral Het is wachten op nieuw luchtvaartprotocol ; altijd twee personen in de cockpit #GermanWingsCrash positive neutral Aanrader ! #Germanwings crash : Niet het hele verhaal ? : http://nl.sott.net/article/833-Germanwings-crash-Niet-het-hele- 65

verhaal ... ---- #vliegtuigongeluk #Lubitz #GermanWingsCrash #waarheid negative neutral De psyche van de mens is moeilijk , soms helemaal niet , te doorgronden . #GermanWingsCrash #ramp positive positive #Germanwings heel veel sterkte familie van omstandelingen ! sympathy #sterkte #gecondoleerd #Germanwings #GermanWingsCrash #crashA320 neutral neutral @goedscheiden ja die vraag was bij mij ook bovengekomen . tot op heden nog geen info gezien of gehoord die zonder twijfel dat bevestigt positive negative Daar bennek efkes niet goed van... #GermanWingsCrash sadness negative negative ik krijg kippenvel als ik de berichten hoor over disgust #GermanWingsCrash - weten dat je al die mensen meeneemt in jouw ondergang AFSCHUWELIJK negative negative Co piloot die ff zelfmoord wil plegen . Wat triest . Neem je sadness 150 mensen mee de dood in #GermanWingsCrash negative negative Kom op , man ! Je vermoordt al die mensen , en je vertelt niet contempt eens waarom ? Column Roelof Hemmen over #GermanWingsCrash http://www.rtlnieuws.nl/columns/column/roelof- hemmen/maak-die-verdomde-deur-open ... negative neutral In het huis van #germanwings zelfmoord dader heeft politie papieren gevonden waaruit blijkt dat hij ziek was #Germanwings #GermanWingsCrash neutral negative En weer... ben ik stil... #GermanWingsCrash #rip sadness positive negative Hoe de fuck kunde een piloot in vertrouwen nemen als die tot anger zo'n dingen in staat zijn ? #GermanWingsCrash positive positive Bijzondere helden , die RI collega's ; al 150 unieke DNA other profielen , zo kort na de #Germanwingscrash Maar wat een (admiratio verdrietig contrast met #MH17 n) negative negative Copiloot had ' een depressie ' . Euh , ik vermoed dat het wel contempt wat meer was dan dat om een massamoord te begaan . #GermanWingsCrash negative negative Vreselijk weer ! Opnieuw een vliegtuig crash... #Germanwings sadness #GermanWingsCrash negative neutral #GermanWingsCrash was waarschijnlijk zelfmoord , #Clarkson kreeg de zak , tentamenstress negeerde ik en begon een blog . #student negative negative Precies dit . Eng. " @PatriciaJonk : Acht minuten tot de dood fear #GermanWingsCrash #U49525 http://www.rtlnieuws.nl/columns/column/pieter-klein/acht- minuten-tot-de-dood ... " negative negative Blijf het een raar verhaal vinden van die piloot die de cockpit confusion niet meer in kon . #GermanWingsCrash negative negative zelfmoord dus . Jezus . #4U9525 #GermanWingsCrash sadness positive negative Wat een onvergetelijk schoolreisje had moeten zijn sadness #GermanWingsCrash #kippenvel negative neutral @tommyboysinger dat is zeker maar een onbetrouwbare piloot is een zwaardere blaam dan een defect aan het vliegtuig #rtlln negative negative +1 RT @RuuDii : Hoe idioot moet je zijn om 149 anger 66

onschuldigen mee in je wanhoopsdaad te sleuren #dtv #GermanWingsCrash negative neutral Andreas Lubitz zag zijn droom van piloot in duigen vallen wegens gezichtsproblemen , meldt @nytimes : http://nytimes.com ? smprod=nytcore-iphone&smid=nytcore- iphone-sharenytimes.com/ ? smprod=nytcor ... #GermanWingsCrash positive positive Weer goed thuis gekomen uit Londen met dit mooie other werkpaardje : http://vliegtuighomepage.nl/f70.htm En dit op de (relief) dag van #GermanWingsCrash positive positive veel kracht voor de familieleden die de Alpen gaan bezoeken sympathy #GermanWingsCrash negative positive #Respect nabestaanden slachtoffers van de ramp en voor sympathy ouders Lubitz #GermanWingsCrash http://www.rtlnieuws.nl/nieuws/buitenland/meeste- nabestaanden-weer-weg-uit-de-alpen ... pic.twitter.com / N0E9rye5GA " negative negative Men spreekt van meenemen in de dood maar voor mij is het other moord #GermanWingsCrash (accusation ) neutral neutral Hoe 2015 is dit ? #GermanWingsCrash nagespeeld met een vluchtsimulator http://www.lavenir.net/cnt/DMF20150324_00622713 ... #ziek #gezond ? ? negative negative Over 2 weken vlieg ik met #Germanwings sowieso al fear vliegangst - weet nt of ik nog durf . Zo erg vr de nabestaande #GermanWingsCrash negative neutral @dwdd #GermanWingsCrash tijdelijke procedure : ( co)piloot ff weg , ( getrainde ) purser zolang in cockpit als 2e man / vrouw positive neutral ' Co-piloot gaf extra gas voor de crash ' http://nos.nl/artikel/2028364-co-piloot-gaf-extra-gas-voor-de- crash.html ... #GermanWingsCrash negative neutral Liveblog : De schok en ongeloof bij Lufthansa en Germanwings steeds groter #GermanWingsCrash http://www.lindanieuws.nl/nieuws/copiloot-german-wings- zette-daling-bewust-in/ ... pic.twitter.com / ZC0acNVqYo neutral neutral De zoektocht naar de lichamen van de 150 slachtoffers van de vliegramp in Zuid-Frankrijk is gestaakt . #GermanWingsCrash #A320Crash negative neutral Opluchting voor #Airbus ; Geen fout in software automatische piloot . #GermanWingsCrash negative negative #GermanWingsCrash was dus gevolg van strenge anger #antiterrorism wetten voor de luchtvaart . Dat was toch niet de bedoeling ! http://nyti.ms/1HC7g9i positive neutral #4U9525 #GermanWingsCrash - Plaatst van crash . Nieuwe beelden vanuit de helikopter . https://youtu.be/YYFYJzHzjzY neutral neutral Ook KLM wil dat voortaan altijd 2 bemanningsleden in de cockpit zitten . #GermanWingsCrash #hvnl neutral neutral Na het gezinsdrama nu dus het vliegtuigdrama . #GermanWingsCrash negative negative Zelden zoveel onzinnig geleuter gehoord en gelezen na een contempt ramp . #GermanWingsCrash 67 neutral negative @lammert rampgebied denk ik aan , #Tsunami #mh17 anger #GermanWingsCrash en dat soort ongein . Niet aan een fucking stroomstoring . negative negative Onvoorstelbaar . 150 mensen de dood in jagen anger #GermanWingsCrash negative neutral Ik kwam net op idee , naar aanleiding van de #GermanWingsCrash en de gesloten deur . Deze was dan electronisch beveiligd , toch ? #dtv positive positive veel sterkte voor de nabestaanden #Germanwings sympathy #GermanWingsCrash negative negative Heel beklemmend . Het verslag van het neerstorten van het sadness vliegtuig . #GermanWingsCrash . negative negative Er worden weer personen veroordeeld op basis van aannames . anger Evidence ? Het zal jouw vader maar zijn . #GermanWingsCrash @volkskrant #media positive negative Vliegtuigen zijn prachtig maar toch weer spijtig om zo'n sadness nieuws te horen.. #A320 #Airbus320 #crash #GermanWingsCrash positive negative Ik wÃƒÂl het niet geloven . #Germanwings sadness #GermanWingsCrash positive negative Zullen we even kappen met die foto van @Maesi_Aregger in disgust @DwarsdrVlaander ? Samen met die #GermanWingsCrash word ik een beetje onpasselijk.. positive negative #GermanWingsCrash Ons brein werkt soms zo complex , dat apprehensi je soms niet wil begrijpen hoe het werkt . on positive neutral #GermanWingsCrash . Mogelijk een poging tot zelfdoding . Dat laat maar blijken hoe belangrijk het is om meer over je personeel te weten . positive positive Wij betuigen onze medeleven voor de slachtoffers van de sympathy vliegtuigramp in Frankrijk . #Germanwings #GermanWingsCrash pic.twitter.com / GJgRLGnh6I negative negative Wa voor ne zieke mens zijt ge als ge me opzet 150 mensen anger vermoord ? ? ? #GermanWingsCrash positive negative WTF #GermanWingsCrash " @tijd : ' Copiloot heeft toestel anger bewust laten crashen ' http://bit.ly/1HKrtgu " positive negative @RuudHteB hoe kan zoiets ? confusion positive negative Een Nederlander aan boord , is die meer waard dan ? disgust #GermanWingsCrash #Germanwings positive neutral Hoe " normaal " je ook bent , een psychose kan iedereen krijgen #Germanwings #GermanWingsCrash positive neutral Nog altijd geen naam van de piloot die aan het stuur zat van de 9525 ? #GermanWingsCrash #ZaharieShah negative negative Moest het nu een moslim zijn , durfde men het dan wel contempt #terrorisme noemen ? Of was het dan ook zelfmoord ? #dtv #GermanWingsCrash neutral negative Pffff elke keer als ik beelden zie van de #GermanWingsCrash sadness krijg ik tranen in mijn ogen #heftig #verdrietig negative positive Wat is er precies gebeurd in de cockpit #GermanWingsCrash . sympathy Zo triest nieuws mijn medelieven aan families en relaties . pic.twitter.com / ne3A0LIsQw negative negative Ik vond vliegen al niet relaxed , maar met al die rampen durf ik fear nooit meer #GermanWingsCrash #vliegangstig 68 neutral neutral Ook piloten van de KLM mogen solo in de cockpit verblijven http://www.rtlnieuws.nl/nieuws/binnenland/ook-klm-piloot- kan-alleen-cockpit-blijven ... pic.twitter.com/7slykdcnXi positive negative Leuk hoor voor de familie van de piloot , graag beetje anger consideratie . Hun hebben naast rouw , ook een schaamte . Gun hun privacy ! #Germanwings negative negative Echt gestoord . Bewust +150 mensen de dood injagen.. anger #Germanwings #GermanWingsCrash negative negative #GermanWingsCrash Bewust 149 onschuldigen de dood in sadness jagen , 8 minuten lang http://bit.ly/1HKrtgu , daar zijn geen woorden voor . negative negative Misschien issie wel flauw gevallen of heeft ie een beroerte confusion gehad . Dan kun je toch ook blijven ademen... #GermanWingsCrash positive negative Mijn hemel . #GermanWingsCrash wordt steeds meer bizar . confusion #Zelfmoord met medeneming van alle passagiers . neutral negative Nu is het pas erg...Tenminste 1 NL slachtoffer bij sadness vliegtuigramp #GermanWingsCrash http://www.nu.nl/vliegramp-germanwings/4017500/150- inzittenden-omgekomen-bij-vliegramp-frankrijk.html ... positive positive Sterkte ook aan alle hulpverleners . #GermanWingsCrash sympathy #vrtjournaal negative negative Vreselijk geen woorden voor... #GermanWingsCrash sadness #Germanwings negative positive gedachten gaan naar de slachtoffers gekke vluchtdata . sympathy http://nl.flightaware.com/live/flight/GWI9525/history/2015032 4/0835Z/LEBL/EDDL/tracklog ... #GermanWingsCrash negative negative Bizar , volgens onderzoekers heeft copiloot bewust het confusion vliegtuig naar beneden gestuurd . #GermanWingsCrash positive neutral De #SpaanseKoningenKoniging zijn wel snel ter plaatse bij #Hollande Zuid-Frankrijk #GermanWingsCrash negative negative Soms is de werkelijkheid nog bizarder dan de fantasie , 150 disgust mensen de dood injagen verzin je niet #GermanWingsCrash negative negative Vrij egoÃƒÂ¯stisch om 149 mensen mee de dood in te nemen contempt #GermanWingsCrash positive negative Bizar ! Een piloot die een vliegtuig laat crashen , jeuh , wat een contempt scenario , ik had het niet kunnen verzinnen #GermanWingsCrash positive neutral Vergeet #driverless cars . Alle innovatiepower op #pilotless planes . Niet veel voor nodig , systemen aanwezig . #GermanWingsCrash #Germanwings neutral neutral Foto's van de rampplek uit Le Dauphine Libere : http://www.ledauphine.com/haute-provence/2015/03/24/un- a320-s-ecrase-dans-la-zone-de-barcelonnette ... #GermanWingsCrash pic.twitter.com/2wEL4Om7WW negative negative Daar krijg ik dus koude rillingen van . #GermanWingsCrash disgust neutral negative Waarom met jezelf nog 149 anderen.. ? #GermanWingsCrash disgust negative neutral Wat ging gisteren mis bij het onderhoud van het gecrashte toestel ? #GermanWingsCrash negative neutral Acht minuten tot de dood #GermanWingsCrash #U49525 http://www.rtlnieuws.nl/columns/column/pieter-klein/acht- minuten-tot-de-dood ... 69 negative negative Hoe ziek zijn deze mensen ? anger https://www.facebook.com/pages/Soutien-%C3%A0-Andreas- Lubitz-h%C3%A9ro-de-lEtat-Islamique/431945353632095 ... #GermanWingsCrash #Germanwings negative negative Pff , wat een dag . Met dat nieuws over #GermanWingsCrash sadness Vreselijk toch ? ! Het zal je maar gebeuren . Verschrikkelijk . negative negative Pff : Iraanse journalisten op terugweg van El Clasico sadness omgekomen in vliegtuigcrash http://sportnieuws.nl/iraanse- journalisten-op-terugweg-van-el-clasico-omgekomen-in- vliegtuigcrash/ ... #GermanWingsCrash negative neutral ' Co-piloot zocht hulp voor problemen met zijn ogen ' #GermanWingsCrash http://www.nrc.nl/nieuws/2015/03/28/co-piloot-had-mogelijk- problemen-met-zijn-ogen/ ... positive positive Onbegrijpelijk . Veel sterkte aan de familie en vrienden sympathy #Germanwings #GermanWingsCrash positive neutral Op Duitse tv begint kritiek los te komen op snelle conclusie oorzaak #GermanWingsCrash . Heeft co-piloot cockpitdeur bewust niet geopend ? negative negative #GermanWingsCrash toch bewust gedaan blijkt uit gegevens sadness blackbox 2 . Triest , zeer triest ! ! ! ! negative negative ' De #co-piloot van #GermanWingsCrash was #Duits , had dus contempt geen #terroristische profiel ' ! ; wat moet dit nu betekenen , mensen ? negative negative Nieuws brengen om nieuws te brengen..."We zijn nog contempt onderweg op een klein wegje " #GermanWingsCrash #hetjournaal zullen ze het dan nooit leren positive negative @Cheekyfenn En precies op het juiste moment zodat hij in zijn confusion geliefde alpen kon crashen . Hij kon dat niet plannen m.i. negative negative Waarom in godsnaam zoveel mensen meenemen in je disgust wanhoopsdaad ? Dit is geen zelfmoord meer , maar een aanslag . #Germanwings #GermanWingsCrash neutral neutral @TPOnl Sinds 9-11 is de deur van een cockpit hermetisch afgesloten #Germanwings positive neutral @ockhams die naam van de piloot is toch allang bekend ? Andreas Lubitz positive negative Er wordt tegenwoordig toch veel gecrasht zenne #airplains apprehensi #GermanWingsCrash on negative neutral Categorie ' verwarde man ' of categorie ' debiele terrorist ' ? Dat zal nu wel de vraag worden . #GermanWingsCrash positive positive Zeker omdat ik binnenkort zelf ga vliegen ben ik hoe erg het other ook is . Blij dat het geen technisch makement was . (relief) #GermanWingsCrash negative negative Blijft raar dat door afluisteren voice recorder geconcludeerd confusion wordt dat copiloot met handeling de daling heeft ingezet #GermanWingsCrash positive neutral ' Co-piloot was 100 procent geschikt om te vliegen ' . Werkgever heeft inmiddels nogal wat uit te leggen #GermanWingsCrash negative neutral #Germanwings houdt cruciale informatie over bekend probleem met zenuwgif in #Airbus A320 achter http://bit.ly/1xFqefk #GermanWingsCrash positive positive Mooi om te zien . Minuut stilte voor de #GermanWingsCrash . other 70

#MotoGP #QatarGP #nuopeurosport (emotion) neutral negative Goh zeg.. #GermanWingsCrash sadness negative negative This was your captain speaking... of hoe 1 gek 149 disgust onschuldigen meesleurt in de dood . #GermanWingsCrash #freakend negative neutral Gegevens van Black box zijn bruikbaar... en er staan gegevens op van de fatale vlucht , bevestigt onderzoeksteam . #GermanWingsCrash negative negative Ik snap dat iemand zo diep in de shit kan zitten , dat hij disgust zelfmoord pleegt . Maar dan in godsnaam geen 149 anderen mee . #GermanWingsCrash neutral neutral MEER VRAGEN DAN ANTWOORDEN ! ! #GermanWingsCrash COMPLOT DENKERS KRIJGEN STEEDS MEER STEUN.. pic.twitter.com / i0aUFohSbB negative negative En weer een vliegtuigcrash ..... Ellende blijft doorgaan sadness #Germanwings #GermanWingsCrash negative neutral @NOS heeft #problemen met de #extra #nieuws #uitzending n.a.v. de #GermanWingsCrash . #logo's ontbreken , #teksten zijn onleesbaar . #storing neutral neutral Ooggetuige : We dachten eerst aan een lawine http://www.weer-en-wind.nl/forum/viewthread.php ? thread_id=793&pid=3365#post_3365 ... #GermanWingsCrash #frankrijk negative negative De copiloot had " psychische problemen " jaja gelle denken dat contempt we dom zijn zeker #GermanWingsCrash negative negative Overal betuigingen aan de nabestaanden van passagiers...en anger nabestaanden van de piloot misschien ? ? Ni hun schuld zeneuj ! #GermanWingsCrash positive negative Men zegt niemand heeft het overleeft , maar er is nog geen confusion personeel op de grond bij de ramplek .#GermanWingsCrash negative neutral #Germanwings #GermanWingsCrash Is Andreas Lubitz ( 28 ) copiloot die 149 mensen de dood injoeg ? http://www.hln.be/hln/nl/33222/Vliegtuigcrash- Germanwings/article/detail/2265655/2015/03/26/Andreas- Lubitz-28-de-copiloot-die-149-mensen-de-dood-injoeg.dhtml ... pic.twitter.com / mCHrLulNQW positive positive Wat een emotionele afsluiting van lieve Marianne other @EversStaatOp538 met daarna @borsato , (emotion) mooi....#GermanWingsCrash negative negative @onno0268 @mvbergen Het feit dat de co-piloot de crash sadness bewust heeft veroorzaakt , maakt de ramp nog erger dan het al is . #GermanWingsCrash negative neutral @SimoneLaurey ah , die vlieghadi . Bizarre daad van een verwarde man negative negative Waarom 150 mensen meenemen in de dood als hij dan toch contempt zelfmoord wou plegen #GermanWingsCrash positive neutral Zo meteen live persconferentie van Lufthansa op @een over de crash ! Benieuwd of we dan weer iets meer te weten gaan komen . #GermanWingsCrash neutral negative in wa voor een wereld leven wij ? #crashA320 apprehensi #GermanWingsCrash on

71 positive neutral In zoektocht naar relevante sprekers #GermanWingsCrash mogen piloten van @KLM niet praten . KLM wil niet in verband gebracht worden met crash positive positive verstandig : @Fly_Norwegian past per direct beleid cockpit other aan na #GermanWingsCrash (supportive ) negative negative Alweer een vreselijke crash op een budget lijn sadness #GermanWingsCrash positive neutral KLM-piloot kan ook alleen in cockpit blijven http://www.nu.nl/algemeen/4019324/klm-piloot-kan-alleen-in- cockpit-blijven.html ... #KLM #rampvlucht #Germanwings #GermanWingsCrash #crash negative negative @MommyChannie ongelofelijk akelig nieuws he , over fear #GermanWingsCrash . Ik moet over 45 minuten naar het vliegveld voor TPE-AMS . #kippenvel neutral neutral NOS heeft foto op de site van de D-AIPT maar het schijnt te gaan om de D-AIPX #GermanWingsCrash pic.twitter.com / FdeVyEsu6i negative neutral #GermanWingsCrash Een Duitse terrorist je verwacht het niet hÃƒÂ¨ . Of toch wel . Feit is dat Lufthansa een blijvende smet op haar blazoen heeft . negative negative Eigenlijk is die Co . Pilot ook een terrorist ! ! Neemt ff 150 psg anger mee die gek #sick #4U9525 #A320 #GermanWingsCrash #Germanwings neutral neutral Het nieuws van vandaag : 1 . Neergestort vliegtuig #GermanWingsCrash 2 . de baarmoeder v #AngelinaJolie 3 . #Rutte's asielbeleid & zijn oogharen neutral neutral 20-jarige vrouw uit Brabantse #Deurne in toestel German Wings #GermanWingsCrash neutral neutral #GermanWingsCrash CO-PILOOT liet toestel crashen ! ! ! Dat gaat veel annuleringen opleveren voor de vakanties Worldwide . #nieuws neutral negative #GermanWingsCrash geen woorden voor ..... #verdrietig sadness negative negative #GermanWingsCrash #A320Crash Alles is #dubbelcheck in confusion een vliegtuig maar je mag alleen achter een gesloten deur.... ? negative negative Kwaadaardig opzet . Wat een nachtmerrie . sadness #GermanWingsCrash negative negative @SanneVanGalen Heb ik ook gehoord ja . Echt bizar wat disgust bezield die mensen toch . negative negative Bizar , afschuwelijk en heel erg beangstigend . fear #GermanWingsCrash negative neutral Copiloot had niet mogen werken dinsdag , maar verzweeg ziekte . Er is meer dan # stroomstoring . #GermanwingsCrash neutral negative 150 onschuldigen vermoorden... Niet te vatten ! Wat een sadness wereld , wat een wereld ! #Germanwings #GermanWingsCrash #rip negative negative Weer eens diep bedroefd dat een mens tot zo iets vreselijks in sadness staat is...... #GermanWingsCrash neutral negative Bah , je zou haast vliegangst gaan krijgen ! fear #GermanWingsCrash positive negative Vraag me af wie er belang bij heeft dat er zo snel contempt onderzoeksresultaten in de media worden geslingerd . 72

#GermanWingsCrash . negative negative Bizar dat #Lufthansa denkt weg te komen met communiceren ' other eigen waarheid ' in crisis #GermanWingsCrash #reputatie (accusation #exitceo kwestie van tijd ) neutral neutral Wederom speculaties omtrend #GermanWingsCrash|#pauw negative negative Herman Brood pleegde zelfmoord . #Lubitz massamoord . contempt Daar zit helaas nogal wat verschil tussen . | #Germanwings #GermanWingsCrash positive negative Hoe kan een video niet bestaan en toch nep zijn , vraag ik u confusion af.||#GermanWingsCrash negative neutral @ruudpeys |Duitse website met info dat copiloot zich tot islam had bekeerd geblokkeerd|http://xandernieuws.punt.nl/content/2015/03/Duits e-website-met-info-dat-copiloot-zich-tot-islam-had-bekeerd- meteen-geblokkeerd ...... |http://www.gctje.nl negative negative Copiloot liet toestel Germanwings moedwillig dalen disgust http://nu.nl/buitenland/4019088/copiloot-liet-toestel- germanwings-moedwillig-dalen.html ... ( via @NUnl)||Te bizar voor woorden.. #GermanWingsCrash negative negative Wat erg : O #GermanWingsCrash . |Eerst #MH370 ( misschien sadness ) neergestord door piloot en nu dit neutral negative Stil van...|#GermanWingsCrash sadness positive negative @quehdodk : Kunnen jullie allemaal stoppen met Twitter te anger gebruiken als jullie emotionele dagboekje ||#GermanWingsCrash positive neutral Waarom zit de zwarte doos v e vliegtuig nog in het vliegtuig ? ? ? Kan dit niet ' live ' op een speciale server ? |#flight9525|#GermanWingsCrash negative neutral Was piloot ziek geworden , massief attack of hartaanval ? |Waarom is een van de piloten uit cockpit vertrokken ? #GermanWingsCrash #drama positive negative @ytekedejong @armburlage Ook grote vraagtekens bij contempt overname onderzoek #GermanWingsCrash door Franse OM.|Zo ' heurt ' het niet negative negative Erg triest gegeven dat @GemDeurne voor 2e keer in amper sadness ÃƒÂ©ÃƒÂ©n jaar te maken krijgt met vliegramp met dodelijke afloop... #GermanWingsCrash #OB positive negative @SofieGeyskens Ja echt ineens hÃƒÂ©ÃƒÂ©l dichtbij apprehensi hÃƒÂ© . Mijn kInderen vliegen altijd met #Germanwings naar on #Verona . No big deal , tot vandaag . positive negative Ja ! @RTLZ heeft hem ! " Het was een gewone voorbeeldige contempt man " die #piloot... ||Was te verwachten.... #Germanwings #GermanWingsCrash negative neutral Het experimenteren met antidepressiva kan gevaarlijke gevolgen hebben #GermanWingsCrash|http://www.elsevier.nl/Buitenland/achter grond/2015/3/Co-piloot-Andreas-Lubitz-leed-aan-zware- depressie-1736546W/ ... neutral neutral #GermanWingsCrash EÃƒÂ©n van de piloten verliet even de cockpit en kon er daarna niet meer in . En toen opende de hemeldeur voor zovelen #nieuws 73 neutral negative @depostduif_ : O wow , softwarefout in ÃƒÂ¡lle airbussen . apprehensi #Germanwings #GermanWingsCrash @nos dus via @wol on