Temporal Mental Health Dynamics on Social Media (2020)

Abstract: We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant- supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depres- sion, supported by the literature. We propose a methodology for providing insight into tem- poral mental health dynamics to be utilised for strategic decision-making.

## introduction mental health issues pose a significant threat to the general population. quantifiable data sources pertaining to mental health are scarce in comparison to physical health data @xcite . this scarcity contributes to the complexity of development of reliable diagnoses and effective treatment of mental health issues as is the norm in physical health @xcite . the scarcity is partially due to complexity and variation in underlying causes of mental illness. furthermore, the traditional method for gathering population-level mental health data, behavioral surveys, is costly and often delayed @xcite . whilst widespread adoption and engagement in social media platforms has provided researchers with a plentiful data source for a variety of tasks, including mental health diagnosis; it has not, yet, yielded a concrete solution to mental health diagnosis @xcite . conducting mental health diagnosis tasks on social media data presents its own set of challenges: the users' option of conveying a particular public persona posts that may not be genuine; sampling from a sub-population that is either technologically savvy, which may lend to a generational bias, or those that can afford the financial cost of the technology, which may lead to a demographic bias. however, the richness and diversity of the available data's content make it an attractive data source. quantifiable data from social media platforms is by nature social and crucially (in the context of our cases study) virtual. quantifiable social media data enables researchers to develop methodologies for distant mental health diagnosis and analyse different mental illnesses @xcite . distant detection and analysis enables researchers to monitor relationships of temporal mental health dynamics to adverse conditions such as war, economic crisis or a pandemic such as the coronavirus (covid-19) pandemic. covid-19, a novel virus, proved to be fatal in many cases during the global pandemic that started in 2019. governments reacted to the pandemic by placing measures restricting the movement of people on and within their borders in an attempt to slow the spread of the virus. the restrictions came in the form of many consecutive temporary policies that varied across countries in their execution. we focus on arguably the most disruptive measure: the national lockdown. this required individuals, other than essential workers (e.g. healthcare professionals) to remain in their own homes. the lockdown enforcement varied across countries but the premise was that individuals were only permitted to leave their homes briefly for essential shopping (food and medicine). this policy had far reaching social and economic impacts: growing concern towards individuals' own and their families' health, economic well-being and financial uncertainty as certain industries (such as hospitality, retail and travel) suspended operations. as a result, many individuals became redundant and unemployed which constrained their financial re-sources as well as being confined to their homes, resulted in excess leisure time. these experiences along with the uncertainty of the measures' duration reflected a unique period where the general public would be experiencing a similar stressful and anxious period, which are both feelings associated with clinical depression @xcite . in this paper, we investigate the task of detecting whether a user is diagnosis-worthy over a given period of time and explore what might this appropriate time period be. we investigate the role of balance of classes in datsets by experimenting with a variety of training regimes. finally, we examine the temporal mental health dynamics in relations to the respective national lockdowns and investigate how these temporal mental health dynamics varied across countries highly-disrupted by the pandemic. our main contributions in this paper are: 1) we demonstrate an improvement in mental health detection performance with increasingly enriched sample representations. 2) we highlight the importance of the balance in classes of the training dataset whilst remaining aware of an approximated expected balance of classes in the unsupervised (test) dataset. 3) we analyse empirically proven relationships between populations' temporal mental health dynamics and respective national lockdowns that can be used for strategic decision-making purposes. ## diagnosis classifier experiments we describe the data mining methodology used to build a distantly supervised dataset and the classifier experiments conducted on this dataset. ## monitoring and analysis we prepare the unsupervised dataset and deploy the previously trained bilst m -self a classifier to annotate this dataset. we analyse the relationships between the temporal mental health dynamics and respective national lockdowns. ## conclusion our set of experiments have been conducted with the aim of providing organisations with a methodology for monitoring and analysing temporal mental health dynamics using social media data. we examine sample representations and their ability to impact classifier performance. we investigate the role of an imbalanced dataset in the classifier training regime. our classifier achieves encouraging performance on two fronts: the ability to discriminate, with reasonable performance, between diagnosed and control samples and identified the christmas depression phenomenon. finally, we analyse the rates of depression and their relationships with respective national lockdowns.