Social media, location sharing and the fear of crime
An explorative study among female students in LeidenMaster Thesis
Written by: Simone Schoof Student Number: s1526685
Supervised by: Dr. M.D.B. Benraad Leiden University
Faculty of Governance and Global Affairs MSc Crisis and Security Management 17225 words
Index
Abstract 3
1. Introduction 4
1.1. Research question and relevance 5
1.2. Reading guide 5
2. Theoretical framework 7
2.1. Body of Knowledge 7
2.1.1. Use of mobile phones 7
2.1.2. Current state of social media 8
2.1.3. Women & Fear of crime 11
2.1.4. Neighbourhood conditions 12
2.2. Conceptualisation 15
2.2.1. Fear of crime 15
2.2.2. Social Media 17
2.2.3. Location Sharing Applications 18
3. Research Design 21
3.1. Case selection 21
3.2. Methods 22
3.3. Validity and reliability 25
4. Analysis / Results 26 4.1 General data 26 4.2. Tests 29 4.2.1. Dependent t-test 29 4.3. Findings 33 5. Discussion 39
5.1. Hypothesis 1: The use of location sharing social media increases fear of crime 39
5.2. Hypothesis 2: The use of location sharing social media reduces fear of crime 40
5.3 Hypothesis 3: The use of location sharing social media has no influence on fear of crime 42
5.4. Recommendations/Limitations 42
6. Conclusion 44
Literature 46
Appendix 50
Abstract
Social media usage is increasing all over the world and extra features are added to social media applications. Location sharing is such a feature, people can share their location with other users of the application and track for example their friends or family. It has not yet been researched whether this feature has a relation with fear of crime or any other security issues. This study explores the relationship between fear of crime, focussed on three categories (sexual assault, threat and physical abuse), and the use of location sharing social media applications (LSA’s). The focus group is female students in Leiden. The results of the survey that was used to indicate the relationship show that the use of LSA’s can reduce fear of crime. There were 109 respondents participating in this research. An additional important finding was that respondents were most concerned about their privacy when they were using LSA’s, because unwanted people might be able to know their location. However the main reason respondents gave to motivate why they felt safer when they used LSA’s, was also that people would know their location. It was a contradictory finding that respondents on the one hand felt more safe because people would know where they are, but on the other hand this also made them feel unsafe when they could not see or chose who was able to track their location.
1. Introduction
Social media is a very hot topic these days. Sharing information and expanding online social network characterise social media platforms. Social media are used all around the world and the platforms are still developing. A lot of research has been done about the negative safety consequences of social media, but there is a lack of research if the use of social media might also have positive consequences. A new feature that is recently added to some platforms is ‘location-sharing’. People are able to share their real-time location with their friends and other people. For this report it was reached whether the use of location sharing influences the feeling of safety of the user, or, put differently, influences the fear of crime. To make this research practicable as a thesis-study female students in Leiden are chosen as the focus group. This study is explorative by nature and further research is therefore recommended
beforehand. The research will focus on the following three hypotheses on location sharing via social media:
1) The use increases fear of crime. 2) The use reduces fear of crime.
3) The use has no influence on fear of crime.
I came to this research question because my (female) friends and I often let each other know when we are heading home after a night out. Mostly we go home together but eventually we have to split up. We end up saying: ‘Let me know when you are home!’ or ‘Send me a message when you are in bed!’. And we do actually send each other a message when we arrive home. In this way there is social control on whether or not someone gets home safe. One time a friend of mine was not at home the next morning. We immediately checked Whatsapp to see at what time she had gone home, and via the ‘last-seen’ option we saw that she had send us a message at 03.45 am and had been online until 04.00 am. Fortunately she shared her location on a social media platform so we could track her.
We could see that her last message was send from a location very close to her boyfriend’s house. This meant that she was probably with him and the stress was over…. After this incident I realised that social media are an interesting topic for security studies. It is also very upcoming in the Netherlands that neighbourhoods have a collective Whatsapp group against burglars, which also shows that social media might have a relation with safety (Dixon 2017).
1.1. Research question and relevance
The above-described occurrences are examples of the way we use social media and lead us to the research question: ‘Is there a relationship between location sharing social media and fear of crime?’
The research question is relevant for the academic field because there is a lack of
understanding and research on this topic. In prior research mainly the negative effects of social media on the feeling of safety have been pointed out. It is proven that social media and the increase of fear are related but it is hardly researched if it also might decrease fear
(Intrivia et al. 2017). Especially the location sharing features of social media are quite new phenomena and not much researched. This research can give new insights on social media debates. There is already an on-going debate about location sharing and the violation of privacy. The relationship between social media and fear of crime is relevant for this debate. Especially because so many people use social media these days and it has a big impact on their social lives, understanding of the underlying mechanisms is relevant and needs
(academic) research. The research question is of social relevance because if social media can decrease the feeling of fear, individuals and society can benefit from this. Also the other way around: if this research concludes that location sharing makes people feel less safe this should be taken into account in the on-going debates. This research also might be a start for
policymakers or companies to think about social media as a fear-reducing factor. If my research concludes that social media platforms can reduce fear, the platforms might be used differently or further advance their location sharing technology and increase the feeling of safety.
The research will be focussed on female students in Leiden because this increases the
feasibility. Being a female student in Leiden, this group is easily accessible to me. My social network can help me to spread the surveys to people and they might speak more openly because I am also a student. I chose for female students and not male students because of this access, but also because women are proven to be more fearful and are more aware of their fears and form therefore an interesting focus group.
1.2. Reading guide
This research is structured as follows. In this first chapter an introduction is given and the research question and hypotheses are introduced. The second chapter presents the theoretical
framework. In this chapter the conceptualization and body of knowledge are described. It strives to clarify the concepts of fear of crime, social media and location sharing applications. This section is there to support the reader for further reading and understanding the research. The third chapter contains the research design. It describes the way this research is structured and explains that methods that are used. The fourth chapter then apprehends the research analysis. Inhere the results are outlined and the statistics are extensively explained. Then the fifth chapter discusses this analysis in relation to the hypotheses and the literature. Also at the end of this section the limitations and recommendations are described. As last is chapter six, which provides the conclusion.
2. Theoretical framework
A lot of research has been done into social media. However because of the fast development of social media in the last few years new platforms arise and more research is possible and needed. Existing literature shows a lack of knowledge on the positive effects of social media. This research can improve understanding by doing an explorative study and search for a relationship between fear of crime and the use of social media. The research of Intrivia et al. (2017) did point out that social media have an impact on fear of crime, which is a good start to continue research on new social media developments and look if the use might also have an (reducing) effect on fear of crime. Their research however did not focus on location sharing features or ‘location sharing applications’ (LSA’s). Also it must be noted that there is not much research in the Netherlands about the consequences of the use of social media. Academic research did show a relation between anxiety and social media but not much research has been devoted to this in the Netherlands and to location sharing features specifically.
This research will be explorative by nature. Its goal is to explore whether the use of location sharing on social media influences fear of crime. Prior research has been done about related subjects and this will be outlined in the first section of this chapter, the body of knowledge. To do research on topics such as social media and ‘fear of crime’, these specific concepts also need to be clarified. The concepts of social media and fear of crime will be examined in the second part of this theoretical framework.
2.1. Body of Knowledge
2.1.1. Use of mobile phones
Previous findings showed that smartphone users worry about threats of physical theft or damage of their phone, data loss and insufficient back-up, malicious apps and wireless network attackers, limited battery lifetime and signal strength (Chin et al. 2012). These worries are however more connected to online safety than to fear of crime and the topic of this research focusses on fear of threat, sexual harassment and physical abuse because of the use of social media.
Prior research has been done on the use of mobile phones in relation to feelings of safety. Nasar et al. (2017) did a survey with 317 respondents and a follow-up survey with 305 respondents on this subject. Their survey respondents were undergraduates from Ohio State University. Their results showed that most of the college students used mobile phones and that most of these respondents felt safer when they had their phone with them when they were walking alone at night (Nasar et al. 2017: 870). The research however also concluded that this increased feeling of safety caused students to walk to places they would normally not go to after dark and therefore actually increased danger. But the authors also proved that having a mobile phone may have a positive role, seeing that 5.7% of the respondents used the device to call for help in a crime situation (Nasar et al. 2017: 870). This is quite a small percentage, so it can be discussed whether this outweighs the risks of going to more dangerous places because of the phone usage.
That people feel safer because of their phones might be because carrying a mobile phone may have protective and collective qualities as a reaction to fear of crime (Nasar et al. 2017: 865). There can be three categories of behavioural reactions distinguished concerning fear of crime: protective, collective and avoidance reactions. Staying away from people or situations that might be dangerous is a form of an avoidance reaction; a mobile phone does not influence this. However a mobile phone can protect someone because that person is able to call the emergency number; it is comparable to blowing a whistle. Therefore mobile phone usage can be a protective reaction. Collective reactions are connected to communication and the
interaction with people. Carrying a phone makes this communication part much easier and provides the possibility to be listened to. However it should be noted that this is only virtual. The person on the other side of the phone is not physically present (Nasar et al. 2017: 865). These qualities of mobile phone usage can increase peoples’ feeling of safety and lead them to take more risks in going to places they would normally not go to. But mobile phone usage may also reduce situation awareness. Talking on the phone can distract people’s attention and slow their reaction time (Nasar et al. 2017: 864). This can be dangerous in combination with the increased risk taking behaviour.
2.1.2. Current state of social media
Prior research has shown that media have influence on fear of crime. Traditional media, such as the newspapers and television, make people feel that victimisation is more likely.
Consequently, individuals that receive media messages about crime become indirect victims of their own fear (Kohm et al. 2012: 70). Kohm et al. (2012) have analysed this phenomenon by universities in Canada and the United States via three perspectives: cultivation,
substitution and resonance. From a cultivation perspective we can say that if media messages about violent crimes increase there will also be an increase of fear of crime. The substitution theory states that crime-related media messages only increase fear among individuals with no personal victimisation experience (Kohm et al. 2012: 71). And seen from a resonance
perspective it can be said that the media will increase fear when the media content is consistent with peoples’ experience (Kohm et al. 2012: 72). During Kohm’s research there was limited knowledge about the Internet but the authors already acknowledged the
importance of analysing the Internet as a feature that brings news. Therefore they included the Internet in their research (Kohm et al. 2012: 75). The authors concluded that fear levels tended to increase when the Internet was used for dating and social networking and therefore warrant additional theoretical development (Kohm et al. 2012: 88).
The article of Intrivia et al. (2017) focused specifically on social media and revealed that social media consumption is significantly related to fear of crime. The article concluded that the more someone uses social media, the more someone gets afraid of crime. A lot of research already was done into possible relationships to fear of crime and it is proven that individuals do receive most of their information about crime from media content (Intrivia et al. 2017: 158). The authors also point out that there is a lack of research on the impact of social media consumption on individuals’ fear of crime. And they argue that it is important to redress this gap for three reasons:
• The first reason is the fact that the number of social media users is growing tremendously since the last twenty years (Intrivia et al. 2017: 159).
• Second, it is important to do research on new popular technologies to improve communicating and messaging.
• And finally the authors point out that individuals’ concern about crime has only increased in the last 15 years according to American research (Intrivia et al. 2017: 159).
They pointed out that prior research is mainly focused on traditional forms of media and that the relationship of social media and fear of crime is still an under researched topic. The research done in the article was based on surveys that included adult respondents from three college campuses (Intrivia et al. 2017: 161).
An example of a new popular use of technology is online location sharing, which is central in this research. The increasing use of smartphones all over the world and the development of technologies such as GPS and wireless localisation techniques have made location sharing easy (Li 2013: 1). The feature of location sharing started with the possibility to let users of an application check-in on their location and share this information with others. One of the latest trends is all-time location tracking, to let people know where you are and to have the ability to make use of discounts and other ‘close-by features’. Application developers have made use of this new form of data sharing and developed family tracking apps. These apps collect user location data and report this information back to family members that are also using the application (Hasinoff 2017: 497). These applications advertise with the possibility for parents to protect their children by monitoring their physical location using GPS (Global Positioning System) and are therefore described as ‘very well concerned parents’ (Hasinoff 2017: 497). Via this way the application developers want to provide a safety feeling to the parents, but it can also be seen as false safety.
Life360 is an example of such a family tracking app. Hasinoff (2017) has done a case study on this application and its relation to ensure children’s safety. The author analyses how Life360 specifically constructs location data as meaningful safety information and is very critical on the question whether or not it really provides safety. Life360 is one of the most popular family tracking apps in the United States (Hasinoff 2017: 498). Applications like this promise that location data provides meaningful safety information. The app has an alarm button for when a user is in danger and makes it easy for the parents or observers to come straight to help. The application provides a feature that the observer receives an alert
whenever a person in the family circle leaves from or arrives at an indicated known location (Hasinoff 2017: 499). Hasinoff (2017) does argue conversely that location information alone does not protect users from harm. Known locations are not necessarily safe and unknown locations are not necessarily unsafe. It should be noted that most intentional and accidental deaths occur at the hands of family members, friends and other known people (Hasinoff 2017: 499). Life360 also provides information on the neighbourhood, it marks residences of
registered sex offenders. But it is debatable in what way this improves safety. A residence of a sex offender does not necessarily cause a threat. Hasinoff (2017) discusses therefore that Life360 might even create anxiety rather than that it reassures safety. It is problematic that the application is tracking you at any time, making it more like a surveillance app (Hasinoff 2017:
504). Surveillance often involves asymmetrical relations of visibility. In the case of Life360 parents can easily force their kids to share their location while hiding their own and creating a panoptic effect (Hasinoff 2017: 504). The application says that it provides a peace of mind, but it also produces a new desire to know every family member’s location at all times (Hasinoff 2017: 506). And when this desire is not being fulfilled a new anxiety might arise. Hasinoff (2017) concludes therefore that location sharing might create more problems than it solves. The author has researched the effect of location sharing for both the sender and the receiver. It must be noted that in this thesis the focus will only be on the sender of the location.
2.1.3. Women & Fear of crime
Research showed that women are more afraid then men and that women are most afraid of being raped (Hickman & Muehlenhard 1997, Braungart & Hoyer 1980, Fisher & Sloan 2003, Ferraro 1996). The authors Fisher & Sloan (2003) pointed out that this fear of being raped shadows fear of other crimes measured under female students (Fisher & Sloan 2003). Women fear rape even more than murder. It is also proven that younger women, such as students, are at a greater risk of experiencing rape than older women (Fisher & Sloan 2003: 635). The research done by Fisher & Sloan (2003) pointed out that women’s fear of victimisation is especially present during the night. Gender is for these reasons consistently the most
important predictor for the measurement of fear. Even though men are more likely to become a victim of crime, with the exception of rape, women are more afraid of all types of crime (Ferraro 1996: 667). The extreme fear of being raped can be the cause of this. Sexual assault may heighten the fear for other offenses, because rape is seen as the always-present terror (Ferraro 1996: 669). Ferraro (1996) proved this hypothesis, the fear of rape influences the fear of other victimisations and this explains why women have more fear of crime then men. This especially counts for younger women (Ferraro 1996: 687).
Women pointed out to be more afraid of rape by strangers than by familiar people, even though the chances of being raped by someone familiar are much higher than being raped by a stranger (Hickman & Muehlenhard 1997). This shows that women are not quite aware of the risks. It is likely that women protect themselves differently from rape by strangers then from rape by someone familiar (Hickman & Muehlenhard 1997: 529). It is also important to take age into account when measuring fear. It is proven that older people in general have
somewhat more fear of crime when compared with young and middle aged adults. However on all age levels, women appear to be more fearful than men (Braungart & Hoyer 1980: 63). 2.1.4. Neighbourhood conditions
The environment, such as a neighbourhood or city, plays a relevant role in a persons’ perception of fear. Therefore it is relevant to look at neighbourhood conditions when doing research on the perception of fear (Snedker 2015: 46). It has also been established that urban residents have a higher fear of crime than nonurban residents and that this fear is positively related to the size of the city, meaning that people are more fearful in a bigger city (Snedker 2015: 48). The article of Snedker (2015) examines how neighbourhood conditions, which influence risk assessments, differ for men and women.
As described earlier, the leading explanation for women’s higher fear of crime understands rape as a ‘master offence’ and this creates a higher general fear of other crimes. It is also proven that perceived risk is a powerful predictor of fear of crime, and that women express a higher perceived risk than men (Snedker 2015: 46). This is due to the fact that women have a greater perceived vulnerability, which makes them more afraid. Via the process of risk assessment, individuals scan a city or neighbourhood for signs of danger and then decide whether an environment feels safe or not (Snedker 2015: 47). The perceived environmental context is therefore relevant to take into account and this perception can differ for men and women.
Women do perceive their environment differently than men (Snedker 2015: 48). Risk signals in an environment that can be considered are: social disorder or weakening social controls, police presence and community related interactions with the police (Snedker 2015: 48). Snedker (2015) explored whether there is a gender difference in the perception of these neighbourhood conditions as signs of crime. The results of his research proved not so surprisingly that women reported more fear of crime in both the neighbourhood and the city than men (Snedker 2015: 56). Gender is an extremely powerful variable for fear of crime in the neighbourhood and in the city. The research showed that female respondents who perceive high risk in the neighbourhood are more likely to fear crime (Snedker 2015: 61). Both men and women rely on environmental conditions to signal certain risks, but women interpret those signals differently then men. Research has shown that women report less confidence in their spatial orientation and sense of direction than men (Snedker 2015: 65). This increases
their risk perception in unfamiliar environments. Women are also more aware of their
environment and are more likely to use environmental signs to judge a situation as dangerous or safe (Snedker 2015: 65).
Fisher & May (2009) also focused their research on the impact of the environment on the perceptions of fear by college students. They outlined four fear-provoking signals that all influence fear. The first one is the visibility, or lighting, of the surroundings. Individuals want to be able to see potentially threatening or harmful situations. They want to see what awaits them (Fisher & May 2009: 304). The second fear-provoking signal is foliage, such as flowers, bushes and trees. This is in line with the visibility, because the growth of foliage can block visual views and can provide a hiding place for offenders. This can result in heightened crime-related fear (Fisher & May 2009: 304). The third signal distinguished by the authors is ‘groups loitering’. Loitering youth or other individuals who indicate possible dangerous elements are signals that are being linked to heightened levels of fear (Fisher & May 2009: 305). Lastly the visibility of the police also influences the perception on fear of crime. But this relation is somewhat inconclusive. Some research shows that in certain situations, in example when the police are on foot patrol, people felt safer. Yet other studies found that increased police patrols increased fear of crime (Fisher & May 2009: 305).
These fear-provoking signals may not be gendered. The visibility of public safety officials such as policemen increased females’ fearfulness slighty more then men. This also counts for overgrown or massive foliage, poor lighting on sidewalks and common areas and loitering groups (Fisher & May 2009: 314). But these increased levels of fear by women were not significantly different across males according to the research of Fisher & May (2009). None of the signals had a stronger effect for one gender compared to the other. A possible
explanation for this lack of gender differences is that all college students may find comfort in seeing members of the college community walking on campus (Fisher & May 2009: 316). 2.1.5. Privacy concerns
The use of LSA’s is combined with discussions about privacy concerns. According to Kim (2015) privacy is defined as the following: “The feeling that one has the right to own private information, either personally or collectively” (Kim 2015: 399). LSA’s are sensitive for violation of privacy because users do not always have complete control on how their information is shared. If people trust a website, they are more likely to disclose information
than when they do not trust a website (Kim 2015: 399). The level of sensitivity of the information can also determine if a user is concerned about his privacy. Prior research has proven that people that shared more sensitive information about themselves on Facebook were more experienced with the platform and perceived more benefit from the use of
Facebook than those whose did not (Kim 2015: 399). People find it important to satisfy their motivations to share location information and are therefore less concerned about trading private information, which can result in people ignoring privacy concerns. Kim (2015) also concluded that the level of mobile phone use is plausible to be related to concern about online privacy. His study showed that the more students used their phone the more they were
concerned about their privacy online (Kim 2015: 401).
The sharing of personal information with unintended audiences is of particular concern. This is especially difficult with applications that do not distinguish the user’s network. This is problematic when a user does not want to share all his information with all of his friends on the platform. It is therefore important to acknowledge that the privacy of social media users is concerned when the users cannot determine the appropriate audience for their information sharing (Misra & Such 2016: 96). There is a need for so-called ‘relationship-based access controls’, where a model uses a different set of features to define different relationships so that there can be a better disclosure of personal information.
The above described privacy concern is, according to Gürses & Dias (2013), a social privacy problem. Another privacy matter is the surveillance problem. Surveillance privacy problems are not independent from social privacy problems nor the other way around. Social network providers have the power to decide who has access to which information and can access all the user-generated content, so when a provider changes its settings it might violate existing privacy settings (Gürses & Dias 2013: 29). But focussing on the surveillance problem,
governments did acknowledge that online social networks could engage the public toward the practice of their rights and basic freedoms (Gürses & Dias 2013: 30). But it can also be the other way. Intelligence agencies can work together with social network providers. This can limit freedom of speech by censoring user content (Gürses & Dias 2013: 31). Therefore there should be made a difference if a privacy problem arises because of the technology-induced risk or because of the harms perceived by users.
2.2. Conceptualisation 2.2.1. Fear of crime
Research on ‘fear of crime’ is concerned with the experience of safety, threat and danger. The purpose of this thesis is to measure fear of crime as experienced by female students in Leiden. To do this, it will be examined in what way this can be measured and explained. Vanderveen (2006) addresses the question how fear of crime and possible parts thereof are measured, and discusses if these current habits of measurement are appropriate (Vanderveen 2006: 3). She acknowledges that there is a lack of clear conceptualisation and measurement of fear of crime. One of her main research questions is how the concept (of fear of crime red.), both the term and its meaning, as well as its measurement developed (Vanderveen 2006: 6). The answer to this question is relevant for this research because it explains how to measure fear of crime in the surveys to be used for this research. It can however also give clarity with respect to the way social media is related to fear of crime.
In the Netherlands the topic of fear of crime is well discussed in politics. Many policy agencies are devoting their time and money to the reduction of fear of crime (Vanderveen 2011: 40). The concept of fear of crime refers to a network of social relations, it is not a pre-discursive social fact. The meanings and related issues are influenced by historical, cultural and social circumstances or contexts (Vanderveen 2011: 41). Vanderveen (2011) argues that without statistics, surveys and the need for knowledge on attitudes and opinions, the concept fear of crime would not exist. Statistics provide the first base for analysing crime. The emergence of official crime statistics in the nineteenth century made the construction of national overall picture of crime possible. This also made it possible to see fear of crime as a national problem requiring a national solution (Vanderveen 2011: 42). In the Netherlands, it is the Central Bureau of Statistics (Centraal Bureau voor de Statistiek - CBS) that began
collecting statistics on crime figures. Their goal was to find out if crime was reducing or increasing. The government started receiving information whether the general public thinks problems concerning crime should have priority over other problems (Vanderveen 2011: 44). But next to this the government can also observe trends that might be useful for policy making. Politicians and policy makers often refer to public opinion, and the fear of crime, to legitimise a policy measure. Lower fear of crime indicates that policy measures have worked and that they therefore should be sustained. Alternatively an increase in the fear of crime indicates that taking action should have more priority (Vanderveen 2011: 45). Vanderveen
(2011) also concludes that survey data on fear of crime can give a better understanding of fear as an expression and as a reflection of one’s own ideological position.
Fear of crime is also defined as “an emotional response of dread or anxiety to crime or symbols that a person associates with crime” (Vanderveen 2006: 18). Vanderveen (2006) points out that fear of crime is an umbrella concept that touches on fear of becoming a victim of crime. It is a complex concept because it uses a lot of other borrowed terms, such as emotions can be explained on its own again. There is a difference between feelings and emotions. Feelings are more momentary and less intense compared to emotions. An emotion is considered a mental state with sometimes-high intensity (Vanderveen 2006: 18). In this research the focus will not be put on the concepts of emotions and feelings, but on the concept of fear of crime as a whole. Fear of crime can be a terminological chaos and therefore it is important to classify the different categories of crime. In this research the main category will be ‘fear of victimisation’. Vanderveen (2006) underlines the fact that questions on the fear of becoming a victim of a specific crime are very common (Vanderveen 2006: 53). In this thesis it is explored whether social media can reduce the fear of becoming a victim of a crime or not, so therefore fear of victimisation is used as a central concept. But victimisation can vary from violent assault to burglary, so this raises the question: the victimisation of what? In this research the focus will be on victimisation of sexual assault, physical abuse, robbery and stalking.
Prior research has shown that fear structures and maintains social relations on a large and on a small scale. There are three dominant factors of fear of victimisation; novelty, blind spots and others. New or unknown environments provoke fear of criminal victimisation (Warr 1990: 893). New things might feel dangerous to people, people do see their own neighbourhood as safe because it is familiar to them. But also in familiar surroundings, something unfamiliar such as people that they have never seen before can provoke feelings of fear (Warr 1990: 893). The second fear provoking factor is a lack of visibility. If something is out of sight or behind someone’s back this provokes a feeling of fear, because people cannot prepare on what is happening. This also explains why the dark is such an important factor for the feeling of fear, people see this as a dangerous time (Warr 1990: 894). A third fear provoking facor is whether their or other people around or not. Being alone can provoke fear and the presence of others can be a very comforting thought. This might be because the feeling that others can come to aid if something is wrong, can be reassuring. This is not the case when the others are
unfamiliar and threatening but overall people do rate others as a fear-reducing factor. This might also be because if an individual is alone, he or she is a much easier target for criminals than if this individual is part of a group (Warr 1990: 895). In this research the fear of being alone is mostly measured, because this can show a better and more clear indication wheter social media has any influence on fear of crime. If respondents are not alone, they might not be afraid at all and then it is difficult to measure whether LSA’s have any influence on fear of crime.
Even though fear of crime is a complex concept, there is a surprising consistency in the way it is measured (Vanderveen 2006). The most important indicators are “feeling safe alone in the neighbourhood at night” and “are there any areas around here where you would be afraid to walk at night” (Vanderveen 2006: 45). Vanderveen (2006) also underlines the importance of the indicator ‘feeling safe when walking alone’. The advantage of this indicator lies in its widespread use; standard questions make comparing patterns in responses possible (Vanderveen 2006: 62). These are relevant indicators for the surveys during this research. Vanderveen (2006) notes that the terms fear, worry and concern are frequently exchanged. She also underlines that the three core categories that appear to be central in the fear of crime are victimisation, risk perception and fear. In this thesis the focus is om fear reduction, so these categories will be taken into account.
2.2.2. Social Media
The media environment has changed rapidly, in parallel with the technological revolution. Smartphone and social media advances have caused a sort of paradigm shift (Workman 2014: 111). People have adopted social media as a technology into their lives.
It is interesting to examine why people adopted social media so easily. Prior research has been done on the age and gender differences in the use of technologies. Conclusions are that
women tend to use technology more for social purposes than men, and that younger people tend to adopt new technologies more than older people (Czaja et al. 2006). Just like traditional media there are different types of social media. Some types of social media are more used for networking or connecting with others, while other types are more task-specific orientated (Workman 2014: 111). For example there is a difference between the applications Facebook and Linkedin, which are both social media applications. Facebook is used mainly for
use to apply for a job or other business purposes. Based on the research of Workman (2014) it can be concluded that social media usage evolves continuously as societies change. It just depends on a lot of things why and how social media are adopted by a certain community or society. The primary reason to use social media is to connect with others for social or business and networking purposes (Workman 2014: 116).
The fast adoption of social networking sites (SNSs), also raises an important question about the implications of the usage. SNSs are broader than applications because they can be acessed through a webbrowser, instead of only working on smartphones on certain software. SNSs are very popular by applications developers, because they form a well used category of new applications. Some academics are afraid that SNSs can decrease the social environment of an individual and can lead to less face-to-face interaction and loneliness (Brandtzaeg 2012: 467). But people can use SNSs in several different ways such as accessing information, debating, socialising and also for entertainment. The way the sites are used depends on the user and different usage patterns might also have different social implications (Brandtzaeg 2012: 467). Social connections can be seen as needs that are important to individuals and can be
supported via the use of SNSs. Connections that are made via SNSs are supported by tools that help people to connect and share experiences in larger social networks (Brandtzaeg 2012: 468).
2.2.3. Location Sharing Applications
This research will concentrate on the social media platforms Facebook, Instagram, Snapchat and Whatsapp. Instagram and Whatsapp are a part of the Facebook company and Snapchat refused an offer to become a part of the company. These applications all have in common that they all have location sharing features and are the most frequent used social media
applications (Statista 2018). Location sharing is mostly discussed in relation with the violation of privacy, but it is not yet researched if it can also have positive effects on fear. Social Networking Sites (SNS) are a special category in mobile applications that is rapidly growing. SNS developers added an extra feature to the apps, where the users could share their location. This operated through a global position system (GPS) and geo-tagging functions (Beldad & Kusumadewi 2015: 102). Now these apps also became part of Location Sharing Applications (LSA’s), also referred to as Location Based Social Networks (LBSN). Beldad & Kusumadewi (2015) researched to what extent do benefits, trust, and social influence
positively affect the use of LSA among university students in Indonesia. The benefits of a form of technology are an important reason why a technology is adopted. Also the social pressure of impression management, where people want to control the impression of others about themselves, is a strengthening factor for the use of LSA as a form of a social
networking tool (Beldad & Kusumadewi 2015: 104). But while people keep sharing their personal information online, there are organisations and third parties that collect this data. Together with this comes the concern for privacy and the dilemma of trust. But the study of Beldad & Kusumadewi (2015) found out that Indonesian students do trust the LSA operator and believed that their information would be safe with them.
Facebook became a location sharing application in 2010, when the company released the feature to ‘check-in’ with your mobile phone on certain locations (Wilken 2014: 1092). Two years later the company added the ‘Nearby’-feature, where people could get informed of their location. Together with the rise of location sharing platforms came the collection of data for the service providers. This data can support surveillance practices with considerable privacy and social implications (Wilken 2014: 1090). Facebook was aware of the importance of the engagement in location sharing and even bought the applications Instagram and Whatsapp as a part of their strategy (Wilken 2014: 1094). Data integration with Instagram and Whatsapp would boost significantly the volume and quality of the geolocation data added to Facebook. This resulted in Facebook being a key player in mobile social networking, local search and location-based mobile advertising (Wilken 2014: 1095). Wilken (2014) stated that the market position of Facebook as a global social media platform has strengthened when it was
conceived as a mobile-based locative platform.
It is already stated that the messaging app Whatsapp contributes to security (Dixon 2017). In Amsterdam there are neighbourhoods that have a collective Whatsapp group chat and
advertise with this as a surveillance measure against criminals (Dixon 2017: 493). Dixon (2017) argues that these groups offer a sense of rootedness amongst people, connect people with eachother and make people aware of things that are happening in a neighbourhood. It helps to bind the community through feelings of collective presence and the feeling of being in this together (Dixon 2017: 494). The author also underlines that strangers in the
neighbourhood are closely observed, something that connects with the feeling of fear. Whatsapp goes further than just simply being a tool for communication, it also relates to a way of being with technology (Dixon 2017: 496). Whatsapp is very much adopted into
people’s lives and can even contribute to the constitution of a community as is proven with the neighbourhood Whatsapp chats.
3. Research Design
The purpose of this research is to explore the relationship between location sharing social media and fear of crime. This means that it is important to know how to interpret ‘fear of crime’. The variables in this research will be the use of social media and fear of crime. The research will be deductive, meaning that the hypotheses are based on existing theory and that the research design to test these hypotheses is made afterwards. The research is conducted with reference to the theory (Bryman 2014: 711). This research is structured as follows: at first there is focus on how female students in Leiden use LSA’s on their smartphones with reference to the literature (in the theoretical framework) and later on the focus is on the possible connection with fear of crime. This relation is investigated with the aid of surveys. As explained in the introduction, the research will focus on three hypotheses on location sharing via social media:
1) The use increases fear of crime 2) The use reduces fear of crime
3) The use has no influence on fear of crime
3.1. Case selection
This research is focussed on female students in Leiden. As described in the theoretical framework, women in general are more afraid then men. Therefore the influence of location sharing social media on fear of crime might be most visible when measured among women. Leiden also has a lot more younger (20 to 25 years old) female inhabitants then younger men; there are 74 men per 100 women (CBS 2018). So relatively seen Leiden has a lot younger women living there, and this makes it a well-considered choice to take female students as a focus group in Leiden. Because the majority of the students are female, it makes
generalization slightly easier but still not reliable. This group was also chosen because they are easily accessible for doing the survey and because women are more fearful the effects of LSA’s on fear of crime might be easier to measure.
Leiden is not a small city in the Netherlands, neither is it a very big one. The city has 124.000 inhabitants in 2018 and is therefore on the 21st place of biggest cities in the Netherlands. Amsterdam is the biggest one with 854.000 inhabitants (CBS Statline 2018). This is relevant information for this research because it places Leiden in the bigger picture. Also important to
note is that Leiden is proven to be in the top three of most safe university cities in the Netherlands. Prior research among over 1000 respondents throughout the country have showed that Leiden is in third place, 52% of the students have said to experience the city as (very) safe (Studentwegwijzer 2015).
The municipality of Leiden has done relevant research on different aspects of safety in the city. An important part of experiencing a living environment as safe is the social cohesion. Good relationships with people that are living around in the neighbourhood increase the feeling of safety and homeless people or drug addicts decrease this feeling (Gemeente Leiden 2017: 11). The research report of Leiden’s municipality included ‘subjective safety’ as a part of experiencing a living environment. This subjective safety is specifically the feeling of safety in people’s own neighbourhood, which is relevant for this research. This also includes people’s behaviour to avoid possible unsafe situations. The reports results show that 38% of the people feel unsafe sometimes in their neighbourhood, Leiden’s average safety score is a 7.4 compared to an average of 7.1 from the 32 biggest cities in the Netherlands (Gemeente Leiden 2017: 11). The score of people that have felt never unsafe in their neighbourhood is 10 % (Gemeente Leiden 2017: 12). From these 38% that have sometimes felt unsafe, almost half of these people were afraid to become a victim of crime. It turned out ass well that women in Leiden are feeling unsafe more often compared to men, both in general and in the
neighbourhood. The report also concluded that youngsters (15 to 43 years old) felt themselves unsafe more often than other age categories and this also counts for people with a higher education (Gemeente Leiden 2017: 12). This should be noted while reading this thesis, because female students fit to all of these three categories. In general the feeling of safety in Leiden is higher compared to the other 32 bigger cities (70.000 inhabitants or more).
However, compared with smaller cities, the feeling of safety in Leiden is lower (Gemeente Leiden 2017: 37).
3.2. Methods
The indicators of fear are tested in this research with a survey. This survey will contain questions about the way social media platforms are used and why. There are questions about fear indicators included such as: ‘Do you use social media when you walk home in the middle of the night?’. As is swiftly described in section 2.2.1, it is important to use the right
Doing a proper survey is impossible without any theory beforehand. The concept of fear of crime is a concept with different constructs so it is better to measure one construct reliably than many constructs unreliably (Vanderveen 2006: 315). To measure a construct, it is important to use the right indicators during the survey. Vanderveen (2006) points out that the common practice of using just one item as indicator of the concept of fear of crime is
insufficient. When only one item is used as an indicator of a concept, the possibility arises that their will be random measurement error which decreases reliability. To get enough reliability, the instruments to measure a construct should at least include three items per set (Vanderveen 2006: 315). This thesis is focussing on the fear of victimisation. The three instruments to measure this that will be central are: fear of becoming a victim of threat, sexual harassment and physical abuse. The survey is going to test the hypothesis: ‘Social media applications can reduce fear of crime by female students in Leiden’. After the survey, there will be in-depth interviews to know more about the use of specific types of social media and their relation to fear of crime.
This research will follow the traditional approach and will continue with the work that has been done on measuring fear of crime so far. Therefore it will continue with the indicators that have been used before, which results in the use of instruments that are based on an inventory of these indicators. An advantage of continuing with the items that are used frequently is that it is still possible to make comparisons over time (Vanderveen 2006: 226). However a disadvantage is that the popularity of crime victim surveys has led to the
problematisation and politicisation of ‘fear of crime’. This has a consequence that policy measures to decrease ‘fear of crime’ were taken together with measures to prevent crime (Vanderveen 2006: 228). The survey will also include some questions about the respondents’ characteristics, to see if the respondents are relevant to the purpose of the survey. This is important to determine whether or not a sample is representative of the population of interest (Folz 1996: 25).
Next to using the right indicators it is also important to think about a sampling approach. According to Folz (1996) “sampling is the science of selecting cases in a way that enables the researcher to make accurate inferences about a larger population” (Folz 1996: 43). This research is concentrated on female students in Leiden, so the survey must reach these respondents. But we must also take into account that a sampling bias will occur. Bryman (2012) identified three sources of sampling bias. These are:
2. If the sampling frame is inadequate.
3. If some sample members refuse to participate or cannot be contacted, meaning that there is non-response” (Bryman 2012: 188).
In this research there will be a sampling bias from the first category. Because the surveys will be spread via a snowball method, the selection of the sample is not random. Snowball
sampling has as a problem that it is very unlikely that the sample will be representative of the population (Bryman 2012: 203). For this survey friends and fellow students were asked to spread the survey, so only a certain (undetermined) group of people was reached. For example people that do not check their e-mail or that are not very active in Whatsapp-groups or on Facebook were not reached. This means that there was convenience sampling during this research. Convenience sampling implies according to Bryman (2012) that: “the sample is simply available to the researcher by virtue of its accessibility” (Bryman 2012: 201). This has as a consequence that the data cannot be generalized ideally, however this could provide a start for further research (Bryman 2012: 201). This is also valid for this research. There has to be kept in mind that this study is explorative, meaning that it is not the aim of the research to come up with general findings (nor is it possible).
The necessary size of the survey sample is very difficult to determine. The absolute size of a sample is important and not its relative size. This implies that when the size of the sample grows, the sampling error decreases. Therefore it is important to determine the sampling error that is acceptable (Bryman 2012: 197). In this research a relatively high sampling error is acceptable, because it has an explorative goal. For this reason the aim was to get around 50 respondents. Because this research is mainly qualitative this statistically provides enough data to have a feasible research. The survey was spread via social media platforms, mainly
Whatsapp and Facebook, because the respondents are required to use social media platforms. The research used quantitative and qualitative methods. This is mainly because a ‘mixed methods approach’ can provide deeper insights (Bryman 2006: 106). As described earlier, the quantitative method will be the use of a survey. The qualitative method will be the literature review and the in-depth questions from the survey can also be seen as a qualitative measure. A survey alone is not enough to understand a relationship between social media and fear of crime, it is important to look closer to the deeper relationship and explain how a relationship is constructed (if so).
3.3. Validity and reliability
The research has a few validity issues. The main issue is that the results cannot be
generalised, but because it is an explorative study this is not problematic. It is difficult to do any form of generalisation about the Netherlands because the respondents in this research will only live in or around Leiden. This is only one city out of many in the Netherlands, so it is going to be very difficult to say something about the bigger population. This means that the external validity might not be as good as possible. It cannot be examined whether the feelings of the people in Leiden are representative for the rest of the Netherlands. Beforehand there is no reason to presume that these feelings differ very much from other Dutch medium-sized cities, but the Netherlands does also have a few bigger cities (i.e. Amsterdam) and
countryside. This sociological deficiency should be kept in mind when reading this research, because the outcome might be different on the countryside and in bigger cities. This research can however provide the first steps for doing the necessary further research in the rest of the Netherlands. It should be kept in mind that the goal of this research is not to prove that location sharing can or cannot influence fear, the goal is to explore whether there might be a possible relation.
In this research it is also important to note the internal validity. The concept of fear of crime is very complex and has a lot of definitions and meanings attached to it. Internal validity
addresses the need to measure what you actually want to measure (Yin 2003: 36). Therefore it is important that the survey is well structured with the right indicators. These indicators will be broadly explained. Fear is very subjective, and this should be taken into account when reading this report. The experience of fear can differ for every person with different
experiences in any region. It is comprehensively described how fear of crime is used in this report to reassure that the results are more valid.
4. Analysis / Results
In this section the data that is collected through the survey is analysed and described here. This is done at chronological order, beginning with the first questions of the survey and ending with the last questions. At first the general data, such as the average age and social media use of the respondents, are explained. This can help to better understand the rest of the results. In order to analyse the answers to the questions 8 to 11 (concerning scenario’s with and without the use of social media) correctly, a test was necessary. This test is explained in the second part of this section. The last questions, question 11 to 15, are explained in the final part. The results of these questions are directly relevant to this research because people were asked directly about the concepts. In this section the results are not linked to the hypotheses and discussed in detail. This is done in chapter 5. The zero hypothesis (H0) in this analysis is that there is no relationship between social media and fear of crime. The alternative
hypothesis (H1) is that social media increases fear of crime and the second alternative hypothesis (H2) is that social media reduces fear of crime.
4.1 General data
This section analyses the results of the first three questions from the survey, being: 1. What is your gender?
2. What is your age?
3. Are you currently living in Leiden?
4. Are you currently studying on university level or HBO? 5. Do you use social media applications on your smartphone 6. Which applications do you use on a daily basis?
7. Do you allow these applications to use your locations?
The survey that has been used for this research had 131 respondents. The programme that is used to analyse the data is SPSS (Statistical Package for the Social Sciences). Because this research focusses on female students in Leiden, 22 respondents were excluded from the data set. To make a valid selection, a filter was used to exclude these other 22 cases. The survey asked the respondent what kind of gender they had (question 1), if they lived in Leiden (question 3) and if the respondent is currently studying on university level or doing a HBO (question 4). Only female students living in Leiden were selected. After this selection, a
dataset of 109 valid respondents remained. The average age of these respondents was 21,9 years. The age distribution is depicted in table 1.
What is your age?
Frequency Percent Valid Percent
Cumulative Percent Valid 18 3 2,8 2,8 2,8 19 4 3,7 3,7 6,4 20 16 14,7 14,7 21,1 21 11 10,1 10,1 31,2 22 34 31,2 31,2 62,4 23 24 22,0 22,0 84,4 24 13 11,9 11,9 96,3 25 3 2,8 2,8 99,1 26 1 ,9 ,9 100,0 Total 109 100,0 100,0 Table 1.
Beforehand, it was assumed that all respondents have a smartphone. This was confirmed as all of the respondents answered ‘yes’ to the question if they used social media applications on their smartphone. The applications that were used on a daily basis are shown in table 2. This information is gathered via question 6, which was a multiple-choice question. Probably not surprising at all, all of the respondents used the application Whatsapp. Next to this application also Facebook, Instagram and Snapchat were used very frequently. As explained in chapter 2 all of these applications have location sharing features. Twitter was not used frequently and is therefore excluded from this analysis. This might be because Twitter is more for
business/politics purposes and is therefore less used by students and more used by older people (Kapko 2017). There were 10 respondents that answered the option ‘other’. Together with the option ‘other’ was also asked to fill in what kind of application the respondent then used. From these 10 respondents answered eight of them that they also used the application Linkedin and two others answered that they used NOS (a Dutch news app) and Pinterest. Linkedin is a business application and differs a lot from the other applications and is therefore also not included in the rest of this analysis. NOS and Pinterest are excluded since they have no location sharing capabilities and were only mentioned two times.
Table 2.
In question seven the respondents were asked if they allowed the application to use their location. The answers are shown in table 3. The biggest part of the respondents allowed the applications access to their location and the majority allowed this only when using the
applications. This means that when the application is offline, the app cannot track the location of its user. This might be because of privacy reasons as explained in the theoretical
framework at the beginning of this report.
Do you allow these applications to use your location?
Frequency Percent Valid Percent
Cumulative Percent
Valid Yes, always 13 11,9 11,9 11,9
Yes, when I use them 77 70,6 70,6 82,6
No, never 16 14,7 14,7 97,2
Not applicable 3 2,8 2,8 100,0
Total 109 100,0 100,0
4.2. Tests
This section analyses the results of the following 4 questions (with the categories threat, sexual harassment and physical abuse):
8. When you are walking alone in the streets on a normal day, how afraid are you that you will become a victim of…
9. When you are walking alone in the streets on a normal night, how afraid are you that you will become a victim of…
10. If you are not able to use social media applications on your phone, how afraid are you when you are walking alone in the streets during the day that you will become a victim of…
11. If you are not able to use social media applications on your phone, how afraid are you when you are walking alone in the streets during the night that you will become a victim of…
To analyse the relationship between social media and fear of crime the four above mentioned variables in the survey needed to be tested. The questions 8 to 11 dealt with how afraid the respondent was in certain situations. Respondents were asked to rate their fear from 1 to 5 (‘not afraid at all’ to ‘very much afraid’), for the three categories of victimisation separately. In question 8 it was researched how afraid the respondents felt when they are walking alone on the streets that they will become a victim of threat, sexual harassment and physical abuse during a normal day. Question 10 is almost the same as question 8, the only difference being that in question 10 respondents were asked to imagine that they are not able to use social media applications in the same situation as question 8. The difference between these two answers indicates if the use of social media influences the respondents fear and to what extent. To determine if conclusions are scientifically relevant it is necessary to exclude statistical errors. For this reason the so-called ‘dependent t-test’, also known as the paired sample t-test, was conducted using the aforementioned SPSS software.
4.2.1. Dependent t-test
In the first case, the dependent t-test calculates whether the respondents’ total score in
a high enough probability (here: 95%)) to the total score in condition 2 (question 10, without the possibility to use social media). To perform the test there are 4 steps that can be followed:
1. Calculate the means of question 8 and 10.
2. Calculate the average difference between the mean scores of question 8 and 10. 3. Check if the test is significant
To be able to compare the answers to the different questions, the ‘average response’ or ‘mean’ per question is needed. This is because questions 8 and 10 both have the three
sub-questions/categories. The first step is therefor to calculate these means per question. Every respondent was asked to rate their fear in the three sub-questions/categories (becoming a victim of threat, sexual harassment and physical abuse) between the values 1 and 5. Value 1 meant that the respondent was not afraid at all and value 5 that the respondent was very afraid. So the means can have a value from 1 to 5. To calculate the means of question 8 and 10, two extra variables needed to be made. These two means were called ‘MeanQ8day’ and ‘MeanQ10daySM’.
The next step is to calculate the difference between each respondent’s mean score in question 8 and 10, and adding up these differences to get the total amount of difference. Then this total is divided by the number of respondents. This gives the average difference, in the test called ‘Mean Difference’, meaning how much on average a person’s score differed in question 8 compared to question 10 (Field 2009: 327).
T-test Test
Table 4.
T-test Statistics
Mean N Std. Deviation Std. Error Mean Pair 1 Mean Q8 day 1,4074 108 ,55877 ,05377 Mean Q10 day SM 1,6142 108 ,87323 ,08403 Mean Differen ce Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper t df Sign. (2 tailed) Pair 1 Mean Q8 day -
Mean Q10 day SM
The output of the t-test is shown in table 4. Since one respondent did not answer question 10, the test only analyses 108 respondents. In the statistics we can see that the means from questions 8 and 10 are both between the values 1) ‘not afraid at all’ and 2) ‘not really afraid’. The mean of question 8 is slightly lower than the mean of question 10, resulting in a 0.2 mean difference. This means that respondents were slightly more afraid when they imagined that they could not use social media applications when they were walking alone in the streets during the day. In table 4 it can be seen whether the difference between the means of the two questions was large enough to be statistically relevant. The ‘95% confidence Interval of the Difference’ gives the boundaries within which the true mean difference lies with 95%
certainty. The interval of the difference is in this case a negative number, which means that it can be said with 95% certainty that respondents are more afraid in question 10 then in
question 8. It can be discussed that the difference might be smaller or bigger then the mean difference of 0.2, but it is clear that there is a difference between the means of the two questions because the interval is negative. Because of the big amount of respondents the confidence interval is very close to our measured mean difference.
The last step is to check whether the test is significant. As shown in table 4, the significance in this test in SPSS-terms was 0,000 (meaning that the actual number was smaller than 0,001) and this is extremely good/ accurate. Because this value (p) is smaller than 0,05 it is safe to say that the difference in the responses is statistically meaningful (Field 2009: 331). The outcome of the test therefore supports the following conclusion: On average, respondents that were walking alone on the streets during the day experienced significantly greater fear when they were not able to use social media applications (M = 1.6142, SE = .08403) than when they could use social media applications (M = 1.4074, SE = .05377), t(108) = -3.685, p < .05. It should be noted that this greater fear was still small and is somewhere between the value 1) ‘Not afraid at all’ and 2) ‘Not really afraid’.
Questions 9 and 11 are very similar to the questions described above. The only difference is that the situations in these questions are during the night. So the respondents were asked in question 9 how afraid they are walking alone on the streets on a normal night and in question 11 how afraid they are if they are not able to use social media in that situation. Again they were asked to rate their fear separate for the three categories of victimisation being threat, sexual harassment and physical abuse. As is discussed in the theoretical framework, it is proven earlier that women are more afraid during the night. Therefore the relationship
between social media and fear of crime might become clearer when analysing the results of questions that are focused on the night-time. To analyse the difference between these two questions the same test as described above was used. The output of the test is shown in table 5.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean Pair 1 Mean Q9 night 2,5448 108 ,92322 ,08884 Mean Q11 night SM 2,9136 108 1,10542 ,10637
Table 5.
As expected, respondents are more afraid during the night. This can be concluded because the mean from question 8 (day-time, with social media) is much lower than the mean from question 9 (night-time, with social media) and the mean from question 10 (day-time, without social media) is also lower than the mean from question 11 (night-time, without social media). But the focus in this research is not on the difference between day and night, but on the
difference between with and without the use of social media. With the help of the t-test the following can be reported about the night-time: On average, respondents that were walking alone on the streets during the night experienced significantly greater fear when they were not able to use social media applications (M = 2.9136, SE = .10637) than when they could use social media applications (M = 2.5448, SE = .08884), t(108) = -6,110, p < .05. Both of the means were between the value 2) ‘Not really afraid’ and value 3) ‘Neutral’.
According to the above, it can be concluded that there is a relationship between fear of crime and the use of social media. In both question pairs (8 and 10 & 9 and 11), respondents answered on average to be less afraid when they could use social media. In both pairs the mean difference was not even half a point on the 1-5 scale. The biggest difference was between question 9 and 11, being -.36883. On a 1-5 scale, that is not much.
Paired Samples Test
Mean Std. Deviati on Std. Error Mean 95% Confidence Interval of the Difference t Df Sig. (2- tailed) Lower Upper Pair 1 Mean Q9 night - Mean Q11 night SM -,36883 ,62737 ,06037 -,48850 -,24915 -6,110 107 ,000
For the dependent t-test it is assumed that the data are normally distributed. This is allowed according to the Central Limit Theory, when there are more than 30 respondents (Kenton 2018). The dependent t-test is chosen because the answers of the same respondents to different questions must be analysed (otherwise the so-called ‘independent t-test’ applies).
4.3. Findings
The results of the last four questions of the survey are directly relevant for this research. This is because these questions provided the respondents the possibility to add text to their
answers. These questions distinguish themselves from the question 8 to 11, because they are scaled differently. These results show directly the opinions of the respondents on the
relationship between social media and fear of crime. The questions are the following:
12. I feel more safe when I’m walking alone in the streets during the night when I can use social media applications that can share my location
13. I feel more safe when I’m walking alone in the streets during the day when I can use social media applications that can share my location
14. Did you ever enable location sharing on your social media applications to feel more secure?
15. Did you ever feel less safe when you were sharing your location on social media? Question 12 and 13 give the respondent the option to scale their agreement. In both questions the respondent can answer on a 7-point scale, varying from the values 1) ‘strongly agree’ to 7) ‘strongly disagree’. For the values 1) and 7) there was a possibility to add text to explain the chosen answer. The frequencies of the answers of question 12 are shown in table 6. The results show that the majority of the respondents agreed to the hypothesis, 66.1% gave answers that they somewhat agreed, agreed or strongly agreed.
