Although there is increasing attention for the interrelationship between obsessive–compulsive personality disorder (OCPD) and obsessive–compulsive disorder (OCD), their shared characteristics in terms of childhood trait antecedents remain understudied. Perfectionism may be a viable candidate trait antecedent, given its role in the clinical manifestation of both OCPD and OCD in adulthood, and the evidence that perfectionism reflects a dispositional tendency observable from childhood onwards. However, little is known about childhood trajectories of perfectionism with prospective links to later OCPD versus OCD. Using latent growth curve modeling, this study explored the baseline and growth of childhood perfectionism in 485 community and referred children (55.5% girls, 7.17–14.78 years old, Mage = 10.74, SD = 1.50) across three waves. Adolescent OCPD traits and OCD symptoms were measured in Wave 4. An overall decreasing trend of perfectionism from childhood through adolescence appeared, without inter-individual differences in growth. Individual differences in baseline levels of childhood perfectionism were significant, and equally predicting adolescent OCPD and OCD outcomes. At a more specific level, childhood perfectionism predicted most strongly the rigid perfectionism component of OCPD, and the orderliness/cleanliness/perfectionism and obsession domain of OCD. This demonstrates the value of childhood perfectionism for understanding differential outcomes of adolescent OCPD traits and OCD symptoms.

Wellbeing is an important concept that concerns researchers, policy makers, and the broader general public. The measurement of individuals’ wellbeing levels has predominantly been done through self-reports (e.g., survey questionnaires), which is time-consuming for respondents and costly. Alternatively, wellbeing can be measured in real-time by automatically analysing the language expressed on social media platforms (e.g., Facebook, Twitter, Weibo), through social media language text mining (SMTM). The application of this method for the measurement of wellbeing is relatively new, therefore the validity of SMTM for wellbeing is still being established. We present a systematic review based on 45 studies, and a meta-analysis on 32 effect sizes from a subset of 18 studies reporting correlations between SMTM wellbeing and survey-based ground truth measures. Our qualitative synthesis of the reviewed studies provided insights into current patterns in the literature including (1) most studies were conducted in English speaking samples, (2) Twitter was the most popular social media platform for data collection, (3) closed vocabulary dictionary methods driven and word-level methods of analysis were equally preferred, (5) satisfaction with life was the most popular ground-truth measure across the studies. In addition to this, our qualitative synthesis provided support for the face validity of SMTM for wellbeing by comparing/highlighting the similarities between the broader survey-based wellbeing literature and the findings of the SMTM-based wellbeing studies. Our meta-analysis found that SMTM shows convergent validity with traditional wellbeing measures (r = .54, 95% CI [.37, .67] for location level studies, and r = .33, 95% CI [.25, .40] for individual-level assessments).SMTM is a promising and growing method, but researchers should be aware of its current pitfalls such as the non-representativeness of the samples acquired through social media platforms. We provide recommendations for future SMTM studies in the context of wellbeing.

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Wellbeing is predominantly measured through surveys but is increasingly measured by analysing individuals' language on social media platforms using social media text mining (SMTM). To investigate whether the structure of wellbeing is similar across both data collection methods, we compared networks derived from survey items and social media language features collected from the same participants. The dataset was split into an independent exploration (n = 1169) and a final subset (n = 1000). After estimating exploration networks, redundant survey items and language topics were eliminated. Final networks were then estimated using exploratory graph analysis (EGA). The networks of survey items and those from language topics were similar, both consisting of five wellbeing dimensions. The dimensions in the survey‐ and SMTM‐based assessment of wellbeing showed convergent structures congruent with theories of wellbeing. Specific dimensions found in each network reflected the unique aspects of each type of data (survey and social media language). Networks derived from both language features and survey items show similar structures. Survey and SMTM methods may provide complementary methods to understand differences in human wellbeing.