Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis

Depression afflicts one in four people during their lives. Several studies have shown that for the isolated and mentally ill, the Web and social media provide effective platforms for supports and treatments as well as to acquire scientific, clinical understanding of this mental condition. More and more individuals affected by depression join online communities to seek for information, express themselves, share their concerns and look for supports [12]. For the first time, we collect and study a large online depression community of more than 12,000 active members from Live Journal. We examine the effect of mood, social connectivity and age on the online messages authored by members in an online depression community. The posts are considered in two aspects: what is written (topic) and how it is written (language style). We use statistical and machine learning methods to discriminate the posts made by bloggers in low versus high valence mood, in different age categories and in different degrees of social connectivity. Using statistical tests, language styles are found to be significantly different between low and high valence cohorts, whilst topics are significantly different between people whose different degrees of social connectivity. High performance is achieved for low versus high valence post classification using writing style as features. The finding suggests the potential of using social media in depression screening, especially in online setting.

Prevalence of psychological distress, anxiety and depression in rural communities in Australia

Objective: To describe the prevalence of psychological distress, depression and anxiety in three Australian rural settings and to identify the levels of risk by gender and age.

Design and setting: Three cross-sectional surveys in the Greater Green Triangle area covering the south-east of South Australia (Limestone Coast), and south-west (Corangamite Shire) and north-west (Wimmera) of Victoria.

Participants: A total of 1563 people, aged 25–74 years, randomly selected from the electoral roll.

Main outcome measures: Psychological distress assessed by the Kessler 10, and anxiety and depression assessed by the Hospital Anxiety and Depression Scale.

Results: The prevalence of psychological distress was 31% for both men and women with two-thirds reporting moderate and one-third high levels of psychological distress. The prevalence of depression and anxiety was approximately 10%. The highest rate of psychological distress, anxiety and depression occurred in the 45–54 years age group. There were no consistent gender or area differences in the prevalence of psychological distress, depression or anxiety.

Conclusions: A third of the rural population reported psychological distress, with the highest prevalence observed in middle-aged men and women. Thus, health professionals should attend not only to physical health, but also to mental health status in this age group. It is also important to target prevention strategies at the 20% who reported moderate levels of psychological distress in order to prevent the development of more serious conditions.

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.

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.

Validation of the Edinburgh Postnatal Depression Scale for men, and comparison of item endorsement with their partners

BACKGROUND: The Edinburgh Postnatal Depression Scale (EPDS) has been validated and used extensively in screening for depression in new mothers, both in English speaking and non-English speaking communities. While some studies have reported the use of the EPDS with fathers, none have validated it for this group, and thus the appropriate cut-off score for screening for depression or anxiety caseness for this population is not known. METHODS: Couples were recruited antenatally and interviewed at six weeks postpartum. EPDS scores and distress caseness (depression or anxiety disorders) for 208 fathers and 230 mothers were determined using the Diagnostic Interview Schedule. RESULTS: Analyses of the EPDS for fathers using distress caseness (depression or anxiety disorders) as the criterion shows that a cut-off of 5/6 has optimum receiver operating characteristics. Furthermore acceptable reliability (split-half and internal consistency) and validity (concurrent) coefficients were obtained. For mothers the optimum cut-off screening value to detect distress caseness was 7/8. Item analysis revealed that fathers endorsed seven of the ten items at lower rates to mothers, with the most significant being that referring to crying. CONCLUSIONS: The EPDS is a reliable and valid measure of mood in fathers. Screening for depression or anxiety disorders in fathers requires a two point lower cut-off than screening for depression or anxiety in mothers, and we recommend this cut-off to be 5/6

Governing depression in Australian legal education : power, psychology and advanced liberal government

Legal educators in Australia have increasingly become concerned with the mental health of law students. The apparent risk posed by legal education to a student’s mental health has led to the deployment of a variety of measures to address these problems. By exploring these measures as productive power relations attempting to shape law students, this paper outlines how this government of depression is achieved, and the potential costs of these power relations. It examines one central Australian text offering advice about how students and law student societies can address depression, and argues that doing so not only involves students adopting particular practices of self-government to shape their legal personae, but also relies on an extension of the power relations of legal education. In addition, this paper will link this advice — which privatises the issue of depression, responsibilises individuals and communities, privileges psychological expertise, and seeks to govern ‘at a distance’ — to broader forms of social administration that presently characterise many Western societies. Doing so allows legal educators to reflect on the effects of their attempts to govern depression, and to consider new ways of altering the power relations of legal education.

Impact of political violence, social trust, and depression on civic participation in Colombia

Civic participation is important for peacebuilding and democratic development; however, the role of mental health has been largely overlooked by policymakers aiming to stimulate engagement in civil society. This study investigated antecedents of civic participation in Colombia, a setting of protracted political conflict, using bootstrapped mediation in path analysis. Past exposure to violence, experience with community antisocial behavior, and perceived social trust were all significantly related to civic participation. In addition, depression mediated the impact of past exposure to political violence and perceived social trust, but not community antisocial behavior, on civic participation. In this context, findings challenged depictions of helpless victims and instead suggested that when facing greater risk (past violence exposure and community antisocial behavior), individuals responded in constructive ways, taking on agency in their communities. Social trust in one’s neighbors and community also facilitated deeper engagement in civic life. Relevant to the mediation test, interventions aiming to increase civic participation should take mental health into account. Limitations and possible future research are discussed.

Validation of the Edinburgh Postnatal Depression Scale on a cohort of South African women.

Postnatal depression occurs in 10-15% of women. The Edinburgh Postnatal Depression Scale (EPDS) is a 10-item self-report scale designed specifically as a screening instrument for the postnatal period. It was initially validated for use in the UK, but has subsequently been validated for other communities. It has not been validated for an African community.

Affective, linguistic and topic patterns in online autism communities

Online communities offer a platform to support and discuss health issues. They provide a more accessible way to bring people of the same concerns or interests. This paper aims to study the characteristics of online autism communities (called Clinical) in comparison with other online communities (called Control) using data from 110 Live Journal weblog communities. Using machine learning techniques, we comprehensively analyze these online autism communities. We study three key aspects expressed in the blog posts made by members of the communities: sentiment, topics and language style. Sentiment analysis shows that the sentiment of the clinical group has lower valence, indicative of poorer moods than people in control. Topics and language styles are shown to be good predictors of autism posts. The result shows the potential of social media in medical studies for a broad range of purposes such as screening, monitoring and subsequently providing supports for online communities of individuals with special needs.

Analysis of circadian rhythms from online communities of individuals with affective disorders

The circadian system regulates 24 hour rhythms in biological creatures. It impacts mood regulation. The disruptions of circadian rhythms cause destabilization in individuals with affective disorders, such as depression and bipolar disorders. Previous work has examined the role of the circadian system on effects of light interactions on mood-related systems, the effects of light manipulation on brain, the impact of chronic stress on rhythms. However, such studies have been conducted in small, preselected populations. The deluge of data is now changing the landscape of research practice. The unprecedented growth of social media data allows one to study individual behavior across large and diverse populations. In particular, individuals with affective disorders from online communities have not been examined rigorously. In this paper, we aim to use social media as a sensor to identify circadian patterns for individuals with affective disorders in online communities.We use a large scale study cohort of data collecting from online affective disorder communities. We analyze changes in hourly, daily, weekly and seasonal affect of these clinical groups in contrast with control groups of general communities. By comparing the behaviors between the clinical groups and the control groups, our findings show that individuals with affective disorders show a significant distinction in their circadian rhythms across the online activity. The results shed light on the potential of using social media for identifying diurnal individual variation in affective state, providing key indicators and risk factors for noninvasive wellbeing monitoring and prediction.

Nonparametric discovery of online mental health-related communities

People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities.

An exploratory study of screening practices and barriers for postpartum depression in Texas

Purpose. To evaluate the prevalence of Postpartum Depression (PPD) screening among practicing obstetrician-gynecologists in Texas, and to identify factors and barriers associated with routine depression screening practices.^ Subjects. One hundred and eighty-nine fellows and junior fellows of the Texas Association of Obstetricians & Gynecologists (District XI).^ Methods. A survey questionnaire was developed and sent to 2,028 obstetriciangynecologists, asking about their current screening practices related to PPD. The survey questions were related to the physician's demographics, the patient population, screening practices, barriers to screening, and perceptions about resources in the community. Responses were analyzed to determine associations between these factors and the physician's screening practices. ^ Results. The respondents (n=189) constituted 9.3% of the surveyed population, thus the findings cannot be considered representative of all practicing Ob-Gyns in Texas. However, the following trends were observed. Of the respondents, 85.4% reported routinely screening for PPD, while 14.6% did not. However, of those that screened, only 20.2% used the Edinburgh Postnatal Depression Scale and 7.6% screened with the Postpartum Depression Screening Scale, both validated screening tools. The majority (77.2%) reported using an informal patient interview to screen. For those who did not routinely screen, inadequate training and inadequate resources to screen for PPD were the top two barriers. Physician's age was associated with routine screening practice, as older physicians were less likely to screen routinely. Primary insurance coverage of the patient population was also associated with screening practice; physicians with Medicaid and uninsured patients were less likely to screen routinely. Lastly, physicians that believed that adequate resources existed in their communities for the treatment of PPD were more likely to screen than those that did not.^ Conclusions. The present study is the first attempt at assessing Postpartum Depression screening practices and barriers in Texas. Although the response rate was low, the findings related to informal screening methods and inadequate training indicated that education and training with regards to PPD screening and validated screening tools among Ob-Gyns stand to be improved. Connecting physicians to psychiatric resources may also improve screening rates. This first look at screening practices in Texas serves as a platform for future research in order to gain definitive insight into the diagnosis and treatment of PPD, and ultimately design interventions to improve detection rates and treatment.^

Macro-epibenthic communities, bathymetry and enviromental information at the tip of the Antarctic Peninsula

The Southern Ocean ecosystem at the Antarctic Peninsula has steep natural environmental gradients, e.g. in terms of water masses and ice cover, and experiences regional above global average climate change. An ecological macroepibenthic survey was conducted in three ecoregions in the north-western Weddell Sea, on the continental shelf of the Antarctic Peninsula in the Bransfield Strait and on the shelf of the South Shetland Islands in the Drake Passage, defined by their environmental envelop. The aim was to improve the so far poor knowledge of the structure of this component of the Southern Ocean ecosystem and its ecological driving forces. It can also provide a baseline to assess the impact of ongoing climate change to the benthic diversity, functioning and ecosystem services. Different intermediate-scaled topographic features such as canyon systems including the corresponding topographically defined habitats 'bank', 'upper slope', 'slope' and 'canyon/deep' were sampled. In addition, the physical and biological environmental factors such as sea-ice cover, chlorophyll-a concentration, small-scale bottom topography and water masses were analysed. Catches by Agassiz trawl showed high among-station variability in biomass of 96 higher systematic groups including ecological key taxa. Large-scale patterns separating the three ecoregions from each other could be correlated with the two environmental factors, sea-ice and depth. Attribution to habitats only poorly explained benthic composition, and small-scale bottom topography did not explain such patterns at all. The large-scale factors, sea-ice and depth, might have caused large-scale differences in pelagic benthic coupling, whilst small-scale variability, also affecting larger scales, seemed to be predominantly driven by unknown physical drivers or biological interactions.

Discovering latent affective dynamics among individuals in online mental health-related communities

Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of 'sensor', we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.