Social media platforms have fundamentally transformed how information spreads through populations, generating new challenges for understanding their impact on mental health. Anchored in the frameworks of emotional contagion and infodemiology, this dissertation investigates how affective information diffuses through online social networks and its implications for individuals with depression, proposing a novel expansion of infodemiology to encompass non-communicable mental health conditions. The methodology combines computational and qualitative approaches across two datasets from the Reddit and Twitter platforms. Reddit data from the r/depression subreddit were analysed to identify behavioural and linguistic markers associated with depression. This was achieved through content analysis, natural language processing techniques and thematic analysis, generating a body of domain-specific knowledge that extends beyond clinical diagnostic tools. Topic modelling techniques using latent Dirichlet allocation, non-negative matrix factorisation, Top2Vec and BERTopic revealed nuanced thematic patterns in discourse informed by lived depression experiences. A qualitative thematic analysis consolidated these findings into four major themes and 23 sub-themes, providing comprehensive domain knowledge of these lived experiences. From this, a depression-specific sentiment lexicon was constructed, demonstrating competitive performance against general-purpose lexica and even large language models when applied to depression-related and broader mental health data. Insights derived from Reddit informed the development of a novel discrete stochastic agent-based model of a Twitter network. In this simulation, sentiment-bearing interactions were modelled to evaluate the mechanisms by which affective information diffuses across social ties. This approach incorporates higher-order Markovian properties through historical agent states, as well as the influence from connections with other agents. The simulation revealed emotional reinforcement as the primary mechanism of affective transmission rather than simple contagion, with agents forming reinforcing groups that amplify existing emotional tendencies. Verification and validation procedures confirmed the robustness of the model, while scenario testing of network-level interventions, such as muting or blocking, yielded improvements in well-being but with heterogeneous effects across user groups. Individuals with depression demonstrated differential susceptibility, processing affective information differently from non-depressed users and showing particular vulnerability to reinforcement effects. The findings demonstrate that social media content carries meaning beyond its informational value, exerting a direct impact on mental health outcomes. This research expands the scope of infodemiology beyond traditional disease surveillance to encompass the direct health effects of information exposure. By extending infodemiology to non-communicable conditions, this study makes three primary contributions through providing a methodological framework for modelling affective information diffusion, the construction of a depression-specific sentiment lexicon and a novel application of agent-based simulation to affective information diffusion as it relates to depression. The implications extend to multiple stakeholders: researchers gain computational tools for studying mental health in online contexts, practitioners obtain insights into contemporary depression experiences, platform developers receive evidence-based recommendations for promoting user well-being and social media users benefit from understanding their agency within algorithmic environments. As social media continues to evolve and data access becomes increasingly restricted, this research provides timely evidence for the urgent need to prioritise mental health considerations in digital platform design and public health discourse.

Depression often goes undiagnosed because of reluctance by patients to seek professional help. Individuals may seek health care information about depression online in the form of depression inventories for self-reporting, which reflect some of the symptoms listed in the manuals used by mental health practitioners to diagnose patients. Alternatively, patients may turn to online mental health communities (OMHCs) for social support, to seek advice and to engage with similar others. Four topic modeling approaches and a subsequent thematic analysis were employed on 11,975 posts from the r/depression subreddit to develop themes that represent the experiences of people with depression. The results of the topic models were used to identify twenty-three sub-themes that were categorized into four greater themes: (1) depression attributions, (2) manifestations of depression, (3) mechanisms for coping with depression, and (4) expressions of depression. The twenty-three sub-themes encompass the nuanced and diverse ways in which people communicate about depression. The use of a computational topic modeling approach and a qualitative thematic analysis approach, when applied to unstructured text data from an OMHC, led to finding symptoms from the text that mirror the symptoms outlined in established depression diagnostic tools. The criticisms of revisions made to depression inventories and standardized diagnostic tools can be addressed by fine-tuning the diagnostic criteria to consider the aspects of depression expressed by individuals experiencing depression.

Online fora are one of the oldest spaces for internet communication. These fora provide internet users the opportunity to connect with others of similar interests and experiences. Online mental health communities are fora for individuals that wish to connect with others based on a shared mental health experience. These digital ecosystems offer an avenue for seeking advice or validation, as well as for freely sharing experiences with similar others within a culture of reduced stigma and social support. Reddit is a popular and readily accessible platform that hosts moderated communities known as subreddits, which include communities for mental health concerns such as depression, anxiety, trauma and eating disorders. This study is an analysis of data collected by way of the Reddit application programming interface (API) from the r/depression subreddit. A conceptual framework was developed for preparing and evaluating the data by way of an empirical content analysis. The content analysis was conducted on 11975 posts made by 9593 unique user accounts over a 56-day period. The analysis of user behaviour in this subreddit relates to general engagement, the temporal attributes of the dataset, and the demographic and geographic attributes of the users. This investigation provides insights into the words most frequently used in posts made to this subreddit, the time of day and day of the week that most individuals posted to the forum, the age and locational distribution of r/depression subreddit users accounted for in the dataset and how these insights relate to the global prevalence of depression.

Efficiency is the measure of how well a process performs, and businesses are constantly looking for ways to improve their productivity. Traditional performance measures are commonly used and applied to data, but often do not consider the effect that multiple inputs and outputs have on the performance of a service unit. Thus, it is important to measure efficiency within the current capabilities of service units. One way to measure the capabilities of efficiency is through benchmarking, which identifies best-practice service units and compares all service units to the best practices. The benchmarking tool used in this study that embodies this notion is known as data envelopment analysis. Data envelopment analysis (DEA) is a linear programming tool used to determine relative efficiency for a group of service units and provides a score on the level of efficiency relative to other service units. DEA is applied to the data of a prominent South African retailer, and multiple DEA models are applied to the data to provide insight into the efficiency of service units for the considered retailer. Numerous extensions and adaptations of DEA have been developed to provide deeper insights into the efficiency of service units, depending on the available data. The CCR model and the BCC model are the main DEA models used in this thesis. Multiple regression analysis is also performed on the efficiency scores of DEA and the information that the models require. Important components for DEA are the decision of inputs and outputs, as well as the number of service units considered at one time, all of which have an effect on the discriminatory power of the models. The data are grouped into categories and DEA is run on these groups to better understand the results that DEA provides. The efficiency scores from the different models are determined for each of the considered service units order for the retailer to make decisions on minimising resources or maximising its outputs in future. DEA is not only a diagnostic tool for determining where inefficiencies exist, but how these inefficiencies should be approached, relative to best-practice units. DEA results were applied to data of 1207 stores over 26 weeks, and it was identified that new fashion products generally perform better than older products. Regression analysis used for productivity measurement, while better for statistical analysis when compared to DEA, is limited in its ability to calculate efficiency for multiple inputs and multiple outputs at once. The results also provide confirmation on the discriminatory power of the choice of components used in DEA, and that isolating one component as a measure of efficiency is not enough for service units, since performance is dependent on multiple factors. The overall result is that DEA be used in tandem with other performance measures to diagnose where inefficiencies occur, and use the information of DEA to move towards improved productivity.