A framework for classifying online mental health related communities with an interest in depression

Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620,000 posts made by 80,000 users in 247 online communities. We have extracted the topics and psycho-linguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modelling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed empirical validation of the model on the crawled dataset where our model outperforms recent state-of-the-art baselines.

The impact of leisure participation on mental health for older people with depression: a systematic review

Aims: This systematic review aimed to discover whether participation in individually chosen leisure activities improved mental health for people aged over 65 who have depression. Methods: Twenty-six potentially relevant studies were identified from an electronic database review. Of these 12 met the inclusion criteria and scope of the review. Results: The current evidence base is relatively small, and includes a diversity of subject groups, subject situations, and study designs. In all cases, participants were either directed to choose from a number of leisure activities, or provided with a pre-designed program that did not allow for individual choice. Conclusion: Currently, there is insufficient evidence available to determine whether participation in individually chosen leisure activities improves mental health for people aged over 65 who have depression.

Differentiating sub-groups of online depression-related communities using textual cues

Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the others.Good predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health.

Depression as a risk factor for fracture in women: a 10 year longitudinal study

BACKGROUND: Previous research has demonstrated deficits in bone mineral density (BMD) among individuals with depression. While reduced BMD is a known risk for fracture, a direct link between depression and fracture risk is yet to be confirmed. METHODS: A population-based sample of women participating in the Geelong Osteoporosis Study was studied using both nested case-control and retrospective cohort study designs. A lifetime history of depression was identified using a semi-structured clinical interview (SCID-I/NP). Incident fractures were identified from radiological reports and BMD was measured at the femoral neck using dual energy absorptiometry. Anthropometry was measured and information on medication use and lifestyle factors was obtained via questionnaire. RESULTS: Among 179 cases with incident fracture and 914 controls, depression was associated with increased odds of fracture (adjusted odds ratio (OR) 1.57, 95%CI 1.04-2.38); further adjustment for psychotropic medication use appeared to attenuate this association (adjusted OR 1.52, 95%CI 0.98-2.36). Among 165 women with a history of depression at baseline and 693 who had no history of depression, depression was associated with a 68% increased risk of incident fracture (adjusted hazard ratio (HR) 1.68, 95%CI 1.02-2.76), with further adjustment for psychotropic medication use also appearing to attenuate this association (adjusted HR 1.58, 95%CI 0.95-2.61). LIMITATIONS: Potential limitations include recall bias, unrecognised confounding and generalizability. CONCLUSIONS: This study provides both cross-sectional and longitudinal evidence to suggest that clinical depression is a risk factor for radiologically-confirmed incident fracture, independent of a number of known risk factors. If there is indeed a clinically meaningful co-morbidity between mental and bone health, potentially worsened by psychotropic medications, the issue of screening at-risk populations needs to become a priority.