User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User interactions and behaviors in a large-scale online emotional support service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

User Interactions and Behaviors in a Large-Scale Online Emotional Support Service

Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically

A Multi-Method Study Investigating Sickness Absence in the Ambulance Service and its Association with Work-Related Stress and Coping Styles

Ambulance service employees have higher levels of sickness absence (SA) compared to other populations. Understanding key factors that are associated with SA are crucial in developing interventions to improve SA. Therefore, the aim of this thesis was to investigate SA in one UK ambulance service and its association with stress and coping styles whilst developing ways in which it can be improved. This multi-method thesis included a systematic review (study 1) and an exploratory, sequential mixed methods study (study 2). Prior to the experimental study, a systematic review was conducted to establish interventions that were effective in reducing sickness absence. Due to limited research in the ambulance service, interventions for healthcare workers were evaluated. Study 1 found three interventions that were effective in reducing SA, however, these interventions were inapplicable to the ambulance service due to problems regarding the timing, location and acceptability of delivery. Due to the ineffective application of current SA interventions to ambulance staff, study 2 aimed to further investigate sickness absence in this context. Study 2 consisted of a explanatory, sequential mixed methods study with a quantitative phase followed by a qualitative phase of research. Within the quantitative phase, variables of interest (workload, perceived control, responsibility and social support) were examined in relation to coping styles (rational, emotional, avoidance and detached) and sickness absence across a 6-month time period. Data were collected using three self-report questionnaires to investigate the association between stress (NIOSH Job Stress Questionnaire and Daily Hassles-Revised), coping styles (Coping Styles Questionnaire) and SA in one UK ambulance service (n = 101). Full-time employees were recruited from one ambulance service in the United Kingdom using an opportunity sample. Data were analysed using negative binomial regression and results suggested an association between a decrease in social support and an increase in SA, an association between avoidance coping, mixed coping styles and an increase in SA compared to rational coping styles. The qualitative phase of the study recruited a diverse range of participants from the quantitative phase using maximum variation sampling. This allowed participants with varying levels of SA, work-related stress and coping styles (n = 12) to be interviewed about their reasons, experienced and perceptions of SA. Data were collected using in-depth semi-structured interviews and found that participants were engaging in SA to maintain their wellbeing and to protect others. An overall negative perception of SA was found amongst participants who perceived a strict and unfair SA policy. SA was also used as other types of leave and participants discussed engaging in presenteeism rather than SA. The findings of this thesis are paramount in developing interventions with a specific focus on developing appropriate coping styles and increasing social support to improve SA within this population. Despite this, there are several limitations of the current research including the exclusion of part-time employees, the self-selecting nature of the recruitment methods and the measurement of perceived rather than physiological measures of stress. Nevertheless, this thesis contributed to the understanding of SA in the ambulance service by offering an insight into the associations between stress, coping styles and SA