Abstract

The research examines the association between hate material exposure and subjective well-being on Twitter using a sample of private and public universities in Lahore. The researcher aims to identify how life happiness & satisfaction are associated with higher exposure to hate material. Data is collected through an online survey (n = 400). The researcher used the theoretical framework of Routine Activity Theory. The findings of the research did not determine any significant relationship between the lower level of subjective well-being and exposure to hate material on Twitter (p > 0.05) because hatred content is easily available and accessible on the Internet and does not require any specific psychological or behavioural situation for having being exposed. Attention is given to the problems which become the cause of sharing hateful content online. The study recommends that SNS should focus more on policies to control hateful content as it is targeting people who result in violent behaviours.

Key Words

Subjective Well-being, Exposure, Online Hate Material, Routine Activity Theory

Introduction

Internet and technology have become an important part of our daily lives. The use of the internet has enhanced people's way of living, interacting with friends and family, making new decisions or completing their routine tasks. A Broadband Search Report (2020) shared that about 61.4% of people worldwide use the Internet. The Internet has provided different messaging apps, social media, and search engines to fulfil the requirement of the users. According to a report by Statista (2019), adolescents between the ages of 13 to 17 are the most frequent users (up to 97%) of social media, social applications and playing video games and engaging themselves in technology. Social Interactions have been transformed with the emergence of different social networking sites. Facebook claims to be had almost monthly one billion users on the site, and Twitter, LinkedIn & YouTube are also included in the vastly widespread sites worldwide. Social networking sites have provided their users to share thoughts and views about everything anytime. The content available on the internet is related to almost all aspects of society and provides new opportunities to the youth and young adults but there exists a chance of risk as well. So, it becomes essential to know which type of content is available on social networking sites.

Since the existence of social networking sites or social media is an open environment for users, they communicate with peers all over the world, share and publish any type of content they want, without knowing the fact that it may or may not harm someone else views or thoughts. Sharing of misleading information, wrong practices, cyberbullying, and hateful content often lead to harmful and negative impacts on the internet and other users. Social networking sites users came across Online hate content either by chance or can be the creators of it. The more a person is involved in online and social activities, i.e., interacting with online friends, and sharing their private and sensitive information on social networking sites platforms can lead to be a target of online hate. The factors associated directly or indirectly with exposure to online hate have been predicted by past researchers which include spending more time on social networking sites increase the chance one can be the target of online hate (Hawdon et al., 2016). Some of the studies relate that the increase in certain forms of victimization mainly is due to an increase in the time spent on internet platforms. (Hinduja & Patchin, 2010; Pratt et al., 2010). Attraction to adversary online activities (Hawdon et al., 2019), creating hatred material online (Hawdon et al., 2014), and being part of online discrepancies with other people (Hawdon et al., 2019; Costello et al. 2016b) can exhibit people targets. The study focused on finding out the role of Subjective Well-Being in exposure to online hate material.

The study moved forward towards criminological theory, Cohen and Felson's theory of Routine Activity Theory argues that when a motivated offender, a suitable target and a lack of capable guardian converge in time and space, crime occurs. (Cohen & Felson, 1970). Routine activity theory has been a part of the literature to explain online hate exposure (Costello et al., 2016), as well as hate material that specifically promotes negative behaviours and violence (Hawdon et al., 2019). The hate material available on Twitter is considered as an offender, Twitter users are the suitable targets which came across offenders when any anti-hate content laws and policies are absent from the platforms or different behavioural or psychological factors come across.

Subjective well-being is defined as a person's intellectual and emotional evaluation of their own life (Diener et al., 2002). The cognitive element refers to how an individual senses his/her overall life fulfilment concerning his/her personal, professional, and work life. The sentimental component is referred to emotions, moods and feelings. Sentiment can be negative when talking about feeling unpleasant i.e., anger, shame or hate and positive ones deals with joy, delight and friendliness. An individual with a high level of subjective well-being tends to have a happy life because they experience more positive effects and fewer negative events in their life. While calculating well-being, it is evaluated as how people visualize and feel about their lives. Subjective well-being is divided into the components of life satisfaction and life happiness that should measure and studied independently. Thus, while judging the correlation and predictors of subjective well-being, the instrument to measure must be kept under consideration. 

A hate group is referred to a group of people who discriminate the other people or groups based on their ethnicity, religion or other characteristics and consider them evil or bad. Most of the definitions of hate view the entire group of people as ‘others’. Hate content is mainly defined as which promotes violence, creates separations, defamation, or spreads antagonism towards others based upon their religion, gender identity, race, affiliations and communities. 

In literature, the term exposure means ‘having access to' or 'coming across to' something which is experienced by any individual either online or offline. Exposure to hate material is the interaction of social networking sites users with the hatred, negative or violent content created or shared by the hatred groups available online. Targeting someone’s personal beliefs or views, and sharing hateful content in the form of videos, images and text against them resulted in exposure to the content by online users. Not only the targets are affected by the content shared, but once such material is available online anyone can have access to it and be exposed to it. Thus, the current research includes the concepts of subjective well-being, hate material and its exposure to the Twitter users studied in the sample of university students.

Review of Literature

Online Hate Groups and Hate Material

The Council of Europe defines xenophobic speech as any form of speech that disseminates, promotes or rationalizes hate, anti-religious, ethnic discrimination or other forms of hate, such as xenophobia towards violent nationalism, anger towards minorities and migrants (Council of Europe, 2013; Banken, 2011). In addition to the European Council's definition of racism and xenophobia, the researcher defines hate content as a gesture or act that expresses harmful and negative views towards an individual or a group of people. The target of hate material may be a specific community or group in society (minorities) or society as a whole. To express degrading or negative attitudes regarding a certain group, visual material like photos, videos or online games can be used (Foxman & Wolf, 2013; Nakamura, 2009). Online hate can be spread either by some specific groups or by those who are acting independently. The phenomenon of an increase in online hate is not new. Social Networking Sites have made it easy for hate groups to reach and recruit a substantial figure of technology users. Even an active online user is capable of creating a sufficient amount of hateful content that can be circulated through social networks like Twitter. Hate material not only includes hatred groups and highly active users who are disseminating hateful and negative content online but also the spreading of hate material in online settings. During the analysis of young people's exposure to hate online, researchers should also examine how this expression is used on social networking sites to spread hate across the internet. Focus of the most of the earlier studies had been the content, propagation and outcomes of hate material (Brown, 2009; Douglas et al., 2005; Glaser et al., 2002; Waldron, 2012). In 2008, according to a national survey (Ybarra et al., 2011), only 3.5% of minors aged 10 to 15 in the United States had come across hate content, while 18% of European youth had encountered hate content in 2010 Internet (Livingstone et al., 2011, p. 97). Being exposed to racist content online leads to both direct and indirect harm. Indirect harm to society raises legal and ethical questions about whether or not the dissemination of hate content in society is permissible (Waldron, 2012). Direct harm of the hate material involves damaging the behavioural and psychological aspects at both group or individual levels (Leets & Giles, 1997; Tyne’s, 2006). Long-standing effects of being exposed to hate material online may result in emphasizing judgement against defenceless groups (Foxman & Wolf, 2013). Also, individuals and online hatred groups can novice younger members for the hateful ideologies to support and join in their actions (Lee & Leets, 2002). The risk of being exposed to online hate material is higher among active Internet users (Livingstone & Helsper, 2010). Thus, this study defines hate material as any type of negative material for which the key purpose is to disturb a group or specific people through graphical content, text or another communicative way (Foxman & Wolf, 2013; Hawdon, 2012). To express hate for some collective is known as hate material or online hate speech (Blazak, 2009; Hawdon et al., 2014). A different form of cyberviolence, Online hate material is unlike cyberstalking or cyberbullying. Hate material online does not outbreak in loneliness, but to express hate or devaluing attitudes the material includes videos, photos, games, text, and other forms of communication. Hate material prompts arrogance to degrade others on the basis of their religion, ethnicity, race, sexual orientation, nationality, or gender for a group or individual. Online hate groups and hateful websites have increased online occurrences since the invention of the Internet (Bowman-Grieve, 2009). The sites include different pages or links on Social Networking Sites (Social networking sites), chat groups on the Internet, various discussion forums and blogs (Douglas, 2007). We do not consider sites that contain hate material as offenders because they do not essentially purpose to victimize anyone (Gerstenfeld et al., 2003; McNamee et al., 2010). Unlike the offenders of outdated crimes, the targets of online hate material are not only the specific victims but gathering and recruiting compatible people to support their deeds (Douglas, 2007; McNamee et al., 2010). They often think to guide others about their cluster, encourage their group, boost partaking to join them and accuse others of insulting their group and community (McNamee et al., 2010). However, hate material once posted, people can be the targets of it. It also needs to know that some people actively search for hate material online and do not consider themselves victimized. Indeed, there exists evidence that individual who sees hate material emotionally harms them (Lee & Leets, 2002; Leets, 2002; Tyne's, 2006; Ybarra et al., 2008). Exposure to hate material is more related to an act of violence (Foxman & Wolf, 2013; Kiilakoski & Oksanen, 2011). 

The main motive of the study is not to know how damaging the hate sites are, it identifies that hate material is risky to some and not to others. The researcher also does not discourse on the specific content of the hate material. There are some sites that are dedicated to spreading hate while some include only posts that express hate like Twitter. In the thesis, the researcher simply explores if young adolescents view material that they interpret as hate and then correlates the exposure.




Routine Activity Theory and Exposure to Online Hate 

The routine activity theory of criminology was first articulated by Lawrence E. Cohen and Marcus Felson (1979) and later advanced by Felson which studied the crime as an incident, highlighting its relation to time and space and stressing its implications. According to Routine activity theory, a crime occurs when a potential offender, a suitable target and the absence of a capable guardian converge in time and space. Cohen and Felson’s (1979) Routine activity theory and editions of it (Reyns et al., 2011) help us understand the correlation of exposure. Thus, the increase in the chances of victimization by exposure to perilous places, situations and people is mainly due to the activities in which people involve themselves and results in fluctuating the capability of protectors to provoke potential offenders (Cohen & Felson 1979). Oksanen et al. (2014) used the theoretical concepts of Routine Activity Theory to explore the cross-national setting of exposure to online hate material (i.e., Finland, Germany, the United Kingdom, and the United States). Costello et al. (2016) used this framework as well to study the online habits of young adults who are exposed to online hate material. Following the footprints of the prior researchers, the roots of the Routine Activity Theory are used to study the crime where a potential offender (online hate material) came across a suitable target (Twitter users) in the absence of a capable guardian (personal protective measures, laws and policies against hate material or parental guidance).

Most of the existing research relies on routine activities theory to explain exposure to and victimization by web-based racist material. Therefore, the main motivation of this study is to study the key claims of the Routine activity theory with the point of existing research on online-hate exposure. According to Routine activity theory, everyday routines of social media users dwell more risk of exposing them to dangerous sites, individuals and groups. The concept suggests that a crime can only take place in space and time when there is a suitable target, an inspired criminal, and no guardianship of the target. Originally, the theory assumed that criminals were motivated and thus focused relatively little on the role that motivation plays in criminal behaviour. Instead of it, researchers keep on challenging how activities develop chances of crime. People's daily routines affect their exposure to dangerous places and people, and guardians' ability to defend themselves against offenders. According to the routine activities theory, when an individual's habits in unsupervised situations expose them to potential offenders, the likelihood of crime increases (Cohen & Felson, 1979; Miethe & Meier, 1990; Reyns et al., 2011). Likewise, the closer you are to a motivated perpetrator, the more likely you are to be exposed to hateful material. When studying online hate speech, it is important to remember the limitations of routine activity theory, as it was originally developed to explain offline crime. Yar (2005, 2013) argued that the convergence of space and time is problematic since online and offline dimensions are different. This has sparked a debate about the importance of offline and online environments. However, following Yar's criticism, the routine activities theory was also modified to account for online crime. Reyns and co-workers (2011) revised the routine activity theory based on the work of Clarke and Eck (2003) to explain cyberlife style and routine activities perspective. And perspective helps to understand cyberspace attackers, suitable targets and insufficient surveillance. Although cyberspace perpetrators and victims do not interact face-to-face, they are connected via network devices and Internet services (Reyns et al., 2011). Numerous previous researchers have used routine activity theory to explain victimization and detection of online behaviours ranging from internet fraud (Pratt et al., 2010; Wilsem, 2011) and identity theft (Reyns, 2013) to harassment (Bossler et al ., 2012) is enough; Marcum et al., 2010), cyberstalking (Reyns et al., 2011) and cyberbullying (Navarro & Jasinski, 2013). In this study, cyberbullying and cyber violence are compared to a previously studied form of online hate speech (Wall, 2001). Exposure to such material contradicts significantly from cyberbullying in that hate material is not targeted at individuals in isolation; Rather, this form of violence occurs when individuals are exposed to material that expresses hatred against a group or community against their will. Focusing on the likelihood of Twitter users being exposed to such content, the researcher tests the variables identified in routine activity theory and other studies of exposure to hate content.

Media Exposure and Social Foundations of Well-Being

Human life existence is not just about simple living but also about a well-lived life (Keyes & Haidt, 2003). Or it can be said that human life is not just about being but it is about well-being. Previous researchers consider well-being mostly related to happiness and satisfaction and it is not only a short-time liking but a long-time pleasure. Therefore, well-being compromises intellectual assessment of overall life happiness and life satisfaction through recurrent positive and occasional negative effects (Diener et al., 1999). The happiness frame (i.e., life satisfaction and zest for life) represents a person's personal assessment of their existence; therefore, wellness refers to subjective well-being. The purpose of the current research is to examine the relationship between elements of online social networking use and subjective happiness. Exposure to different views can play an important role in a person's health (Mutz, 2002). When discussing the association between online social networking activities (e.g., Twitter and Facebook use) and a person's mental status, the literature provides evidence that collaborating with others through social networking is a positive relationship to greater enjoyment of life, life satisfaction and self-esteem (Brusilovskiy et al., 2016; Ellison et al., 2007; Liu et al., 2015; Nabi et al., 2013; Valenzuela et al., 2009; Valkenburg et al., 2006). Valkenburg et al. (2006) discovered that social media users feel happier and more fulfilled when they receive positive feedback or responses. Ellison et al. (2007) discovered that people with lower life satisfaction are more likely to participate in online networks to improve their well-being. Additionally, Internet-based conversations and interactions can improve a person's emotional and behavioural health, which is directly related to their quality of life (Liu et al., 2015). By creating public profiles, social networking sites allow users to connect with others (Ellison et al., 2007). The connections made on social networking sites make sharing data in the form of text, images, life events, and other content easy and fast. Each social networking site has its own additional features; hence it may have a negative correlation with subjective well-being. Because of the great popularity of Twitter, the present study specifically focused on how reduced subjective well-being is associated with Twitter users. Subjective well-being is a broad phenomenon that includes life satisfaction, affective responses, and satisfaction with life (Diener et al., 1999). Diener et al. (1985) describe the most common way of measuring life satisfaction and pleasure as a life satisfaction scale (single or multi-level) that measures how satisfied a person is with their existence. Despite the fact that life satisfaction has been measured in numerous studies of social networking site use (Keyes, 2007; Seligman, 2011; Seligman et al., 2004), researchers have identified both life satisfaction and life happiness as a single indicator measured for subjective well-being -in this study. Previous studies have shown that Facebook use is positively associated with personal happiness (Ellison et al., 2007; Grieve et al., 2013; Oh et al., 2014; Valenzuela et al., 2009), while improper and envious use of Facebook is negatively associated with well-being (Chou & Edge, 2012; Krasnova et al., 2013; Satici & Uysal, 2015). The researcher applied the same conclusion to Twitter users and discovered a link between subjective well-being and Twitter users.

The factors of the basic differences in Subjective Well-Being are related to the argument of how hate material affects it. Prior studies regarding the effects of exposure to hate material are occasional while the negative effects are relevant to our discoveries. Thought processes and like-minded behaviours towards anti-social young people affect media use. Increase in antagonism, stress, disturbed expressive responses and pro-social behaviour related to violent media consumption i.e., videos and games (Anderson & Bushman, 2001). When an individual is exposed to online hate material, certain characteristics are considered to be related to negative self-esteem and a lower level of subjective well-being (Harrison & Hefner, 2008; Brown & Bobkowski, 2011). The relationship between Subjective Well-Being and the usage of online media platforms for hate material remains uncertain. The interaction of hate material and media is affected by the life satisfaction and happiness of individuals who are participating in online activities. Subjective Well-Being is constructed by three prime areas and it can be determined by emotional responses including positive and negative ones, realm happiness and finding overall life satisfaction (Proctor et al., 2009; Schiffrin & Nelson, 2010; Demir & Özdemir, 2010). Positive and negative responses involve the evaluation of an individual's life events or situations (Oishi & Diener, 2001). A general evaluation of someone's life in a positive way of life satisfaction is related to a state of happiness while a negative evaluation is associated with depression and anxiety. Studies suggested that encountering the different stages of experiencing online hate material, there exist a reciprocal association between exposure and Subjective Well-Being. Prior research suggests that the more one will be exposed to online hate material, the lower will be subjective well-being. In this study, it is assumed that persons with a lower level of subjective well-being are more likely to view content that expresses hate. Or those who are less satisfied and less happy with their current situation in life are more attracted to the content that expresses the same level of dissatisfaction and happiness that someone describes as hateful. Thus, the study aimed to fill the gap by examining the reciprocal relationship between exposure and subjective well-being. The focus population of the previous studies had always been Western Countries, hence proposed study aimed to fill the gap of considering exposure to online hate for Asian social media users without the consent of a cross-sectional study.

Secondly, this study tried out the concept and assumption of a criminological "Routine Activity Theory" from the perspective of online Hate Material Exposure previously it has been tested on the victimization of online hate content. 

Methodology

A quantitative Survey approach is used for the current study. The study focuses on the online hate material available on Twitter. As per on statistical report on the number of Twitter users there are approx. 330 million active users on the site in the world out of which 3.40 million in Pakistan in early 2022. The purposive sampling technique is used to derive a sample from the Population of university students of Lahore (2 public and 2 private universities) either Undergraduates, Graduates or Post Graduates who are frequent users of Twitter. After carrying out a needs sample, the student quota of each university is determined. Measuring the extent to which participants were exposed to online hateful material is the dependent variable exposure to online hateful material. (Helliwell et al., 2014) considers the global measure of happiness as one of the independent variables and tests it with a widely used questionnaire to determine general subjective well-being. In addition to enjoyment, life satisfaction is also measured as an independent variable. Finally, the scientist evaluated the study based on its socio-demographic characteristics. These variables included the age, gender, level of education and occupation of the respondents as factors influencing people's subjective well-being. Exposure to online material was measured using the most common scale (Reichelmann et al., 2021; Harriman et al., 2020; Hawdon et al., 2011; Oksanen et al., 2014). Along with exposure to online hate material, a proprietary life satisfaction scale (Diener et al., 1985) and a satisfaction rating scale (Pontin et al., 2013) were introduced to measure individuals' subjective well-being. The data from the study are analyzed using the Statistical Package for the Social Scientist (SPSS).

Results

A survey questionnaire has been shared online via WhatsApp with the students of two public and two private universities in Lahore (University of Punjab, Lahore College for Women University, University of Central Punjab and University of Management and Technology). Data is collected during the time period of one month and is later plotted on SPSS for further analysis.




 

 

Table 1

Descriptive Statistics of the Age of Participants

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Age of Participant	394	17	60	22.79	4.35

 




Table 1 shows the data of the sample of 400 students (out of which 396 has mentioned their ages, while 4 had left the answer blank) between the ages of 17 to 40 having undergraduates, graduates and post graduates. The mean of the ages of the respondents was 22.6 (SD = 3.44).




 

Table 2

Respondents from Public and Private Universities of Lahore

	No. of Participants (n)	%Age of n	Valid Percent	Cumulative Percent
Punjab University	141	35.3	35.3	35.3
Lahore College for Women University	77	19.3	19.3	54.5
University of Central Punjab	85	21.3	21.3	75.8
University of Management and Technology	97	24.3	24.3	100
Total	400	100	100

 




The results showed the maximum participation from University of Punjab (141), followed by University of Management and Technology (97), then University of Central Punjab (85) and lastly Lahore College for Women University (77) making it a sample of 400 respondents.




 

Table 3

The ratio of Male and Female Participants in the Survey

Gender of Participant
	Frequency	Per cent	Valid Percent	Cumulative %
Male	154	38.5	38.5	38.5
Female	244	61	61	99.5
Prefer not to say	2	0.5	0.5	100
Total	400	100	100

 




The research has been proposed to analyze the equal ratio of male and female participants but as having one female public university (Lahore College for women university) in sample ratio of male and female differs (Male = 154 and Female = 244, Prefer not to say = 2). Table 3 shows the percentage of male and female participants in the survey.

To measure the subjective well-being of the participants, scales for life satisfaction and life happiness has been used.  Life satisfaction of the respondents has been measured with the most widely 5 items questionnaire evaluated on a 7-point Likert scale and 7 items questionnaire having a 5-point Likert scale used to measure Life happiness.




 

Table 4

Reliability Statistics of Subjective Well-Being Scale

Reliability Statistics
	Cronbach's Alpha	No. of Items
Life Satisfaction	.872	5
Life Happiness	.870	7

 




Table 4 shows Cronbach's alpha value of both life satisfaction and life happiness as these scales are already been used and are reliable enough to measure subjective well-being.

Overall Subjective well-being of the participants was considered by the separate scores of life happiness and life satisfaction according to the results. 34% of the respondents were satisfied with their life and achievements based on the questions asked of them. 35% marked them as slightly satisfied, 16.5% consider themselves as slightly dissatisfied and 4% stayed neutral. 176 (44%) out of 400 participants consider themselves very much happy in their lives, followed by moderately happy (30.5%) and only 2% are not happy at all. Hence, from overall statistics, the researcher concluded that respondents did not have a lower level of subjective well-being as they showed themselves while filling out the questionnaire.

The main objective of our study was to determine the extent of Exposure to Hate Material among Twitter users. Respondents were asked the most commonly used question to find the extent of exposure to hate material i.e., In past three months if they have seen any hateful or degrading material.




 

Table 5

The extent of exposure to Online hate material among Twitter users

In the past 3 months, have you seen hateful or degrading material online that attacked certain groups of people or individuals?
	Frequency	Per cent	Valid Percent	Cumulative Percent
Yes	316	79.0	79.0	79.0
No	44	11.0	11.0	90.0
May be	40	10.0	10.0	100.0
Total	400	100.0	100.0

 




79% of the sample responded Yes to the question, 11% no and 10% were not sure about their exposure (Table 5). Keeping in view that there was no specific type of hate material or any type of content (video, image, or text) provided in the questionnaire and respondents were free to think and interpret what they think online hate material is.

The research aims to find out the lower life satisfaction of an individual as a significant predictor of being exposed to online hate material (H1). Prior research has assumed and explained the impact of being exposed to hate material on the subjective well-being of individuals. Seeing hatred, and negative content online or offline last different violent impacts on behavioural and psychological well beings. The independent measure was the exposure to hate material and life happiness and life satisfaction was kept as dependent variables to evaluate whether having a low level of satisfaction and happiness with life resulted in more exposure towards the hatred content. The assumed hypothesis are mentioned below:

H1: There is a significant relationship between Life happiness and exposure to online hate material.

H2: There is a significant relationship between Life satisfaction and exposure to online hate material.

H3: There is a significant relationship between Lower subjective well-being and exposure to hate material on Twitter.

Linear regression analysis was applied to data collected from the sample of university students to find out the predictive relationship between exposure to hate material on life happiness and life satisfaction.




 

Table 6

Linear Regression model for Exposure to hate Material and life Satisfaction.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.004a	.000	-.002	.64496
a. Predictors: (Constant), Life Satisfaction




The first statistical model (Table 6) of the study shows the relevancy of the independent and dependent variables which shows that satisfaction from life is not a significant predictor of exposure to online hate material.




 

Table 7

Coefficients of model Exposure to hate Material and life Satisfaction

Coefficients
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1.301	.122		10.701	.000
	LifeSatisfaction	.000	.005	.004	.080	.937

 




The table of coefficients (Table 7) shows the significant value of life satisfaction with respect to exposure to online hate material. From the table, we can see that the p-value is greater than 0.05 (p=0.937) which means that the hypothesis ‘there is a significant relationship between life satisfaction and exposure to hate material’ is not true and we cannot say IV as a predictor of DV.




 

Table 8

Linear Regression Model for Exposure to hate material and Life Happiness

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.016a	.000	-.002	.64488
a. Predictors: (Constant), LifeHappiness

 




The statistical model (Table 8) of the study shows the relevancy in the independent and dependent variables which shows that happiness from life is not a significant predictor of exposure to online hate material.




 

Table 9

Coefficients of model for exposure to hate material and life happiness.

Coefficients
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1.267	.138		9.189	.000
	LifeHappiness	.002	.006	.016	.322	.747

 




The table of coefficients (Table 9) shows the significant value of life happiness with respect to exposure to online hate material. From the table, we can see that the p-value is greater than 0.05 (p = 0.747) which means that the hypothesis 'there is a significant relationship between life happiness and exposure to hate material’ is not true and we can say that less happiness in life is not a significant predictor of exposure to hate material. However, comparing the p values of life happiness and life satisfaction, happiness seems to be more significant towards exposure to online hate material.




 

Table 10.

Linear Regression Model for SWB and Exposure to hate material on Twitter

Model Summary
Model	R	R Square	Adjusted R Square		Std. Error of the Estimate
1	.010a	0	-0.002	0.644
a. Predictors: (Constant), SWBLevel

 

Overall statistical model (Table 10) of the study shows no variance in subjective well-being and exposure to hate material.

 

Table 11

Coefficients of model for SWB and exposure to online hate material

Coefficients
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1.283	.138		9.320	.000
	SWBLevel	.001	.003	.010	.205	.837

 




The table of coefficients (Table 11) shows the significant value of subjective well-being with respect to exposure to online hate material. From the table, we can see that the p-value is greater than 0.05 (p = 0.837) which means that there is no significant relationship between subjective well-being and exposure to hate material.

These models include the variables which were assumed to be significant while analyzing their effects on the dependent variable. Therefore, based on the results, the study concluded there is no significant relationship between a lower level of subjective well-being and exposure to hate material found to be true as seen from the table of coefficients (p-value> 0.05). Hypothesis based on the gender discrimination of individuals being exposed to hate material online assumed that male participants have more exposure to hate material on Twitter.




 

Table 12

Comparison of male and female participants on exposure to hate material

In the past 3 months, have you seen hateful or degrading material online that attacked certain groups of people or individuals? * Gender of Participant Crosstabulation
Gender of Participant
	Male	Female	Prefer not to say
Yes	122	192	2
No	20	24	0
May be	12	28	0
Total	154	244	2

 




However, our result statistics (Table 19) showed a different angle explaining that female Twitter users have more exposure as compared to males. This may happen due to deviation in the sample of male and female participants as the sample included one female university.

Discussion

There is no question that the power of the Internet has brought diverse opportunities to members of society; on the other hand, however, it also provides a place for the expression of hate-based opinions. The study mainly aimed to investigate whether there is a connection between lower levels of subjective well-being and exposure to hate content on Twitter among students at the University of Lahore. There have been many studies in the past that have examined the effect that exposure to hate content has on a person's subjective well-being; However, the present research filled the gap by examining the impact that a person's level of subjective well-being has on their exposure to hate material. Few studies have explored the relationship between exposure to hateful content online and individuals' subjective well-being, although hate online has become a prominent concern in contemporary society (Brown, 2009; Douglas et al., 2005; Waldron, 2012). This research helped fill a knowledge gap about the impact that subjective well-being has on the process of being exposed to hateful content online. According to the results of the study, Twitter users are more likely to accidentally come into contact with hateful content than to have actively searched for it. The results showed that 79% of respondents had been exposed to content that promotes hate on Twitter. According to the survey results, sixty per cent of respondents who use Twitter have accidentally encountered content they found hateful. This implies that content of this type that is available online is not difficult to find on social network sites. No correlation was found between any of the socio-demographic factors and the level of exposure to racist or hateful content. Despite all these limitations, the study aimed at examining the role that subjective well-being plays in increasing exposure to online content, with a particular focus on targeting Twitter users. Previous studies had explained levels of subjective well-being as a result of exposure to online hate material. The extent to which a person is exposed to hate material online is not determined by that person's subjective well-being but by the other variables involved in the situation. These other variables include online and negative offline behaviours that affect the online experience, as well as the laws and policies that should be in place to control the content that promotes hate or negative views of someone. 

Conclusion

The results of the study led the researchers to conclude that subjective well-being, also known as lower life happiness and life satisfaction, is not significantly involved in greater exposure to online hate content on Twitter. This is due to the fact that many other variables play a role, such as Eg increased online activities, participation in discussion forums, poor offline experiences, and anti-hate laws and regulations, all of which have resulted in exposure to hate content online. If the content on the social network site is readily available and easy to get, people are more likely to have stumbled upon it. In addition, when the material is publicly accessible, there is no discrimination based on demographic criteria (such as age, gender, qualifications or professional position). Lower subjective well-being is not a powerful forecaster of exposure to hate content among Twitter users who are students at Lahore universities, as examined by previous researchers. However, exposure to online hate material may impact the person's subjective well-being.

Limitations & Future Recommendations

The sample for this research came only from universities in Lahore. This is the only limitation of this study. It is recommended that future studies work on a larger sample of populations from many cities so that they can make cross-sectional comparisons in the Asian setting. This research is limited to generally available hate material on the Internet; However, it can be expanded in many ways by focusing on a specific category of hate content. In summary, the study was only conducted among those who use Twitter. It is possible to extend them to social media users or social network users. Researchers advocate examining the time individuals spend viewing hate content across a variety of websites, including those that focus on specific groups (religion, personal beliefs, gender, ethnicity, etc.). In addition to the survey approach, face-to-face interviews or content analysis may be conducted on websites that contain negative, hostile, or one-sided content, as well as on individuals who are exposed to material promoting hate and who are a particular focus of such content. It is not difficult to determine who is the author or publisher of anything posted on social networking sites such as Twitter since most of the content on these sites is ranked by the most popular or trending topics of the moment. However, on websites promoting hate or evil beliefs, the main perpetrator of these acts remains unknown and they target a wide variety of communities and organizations. Considering the websites that promote hate, applications from the research study would be an excellent contribution to future literature.

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