Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population -- a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. Across four tasks of predicting U.S. county population health statistics from Twitter, we find standard restratification techniques provide no improvement and often degrade prediction accuracies. The core reasons for this seems to be both shrunken estimates (reduced variance of model predicted values) and sparse estimates of each population's socio-demographics. We thus develop and evaluate three methods to address these problems: estimator redistribution to account for shrinking, and adaptive binning and informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods significantly outperforms the standard restratification approaches. Combining approaches, we find substantial improvements over non-restratified models, yielding a 53.0% increase in predictive accuracy (R^2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks

Codificar la salut mental. Darrere la predicció i detecció de salut mental amb intel·ligència artificial

La intel·ligència artificial (IA) s'incorpora de forma creixent en sistemes de predicció i detecció a la salut mental, un àmbit sanitari tradicionalment desatès, infrarepresentat i d'accés dificultós. Tot i ser una opció prometedora, l'estudi de la producció de la IA en el camp de la salut mental segueix sent incipient, així com els estudis que es centren en com aquesta tecnologia concep la salut mental. Així, per conèixer la noció de salut mental darrera el procés de creació d'aquests sistemes, aquest treball ha recopil·lat els discursos d'investigadors/es de ciència computacional que treballen per a la detecció i predicció de salut mental amb IA. A través d'aquesta anàlisis qualitativa, hem pogut identificar un posicionament comú respecte els objectius de la IA, la valoració de l'interdisciplinarietat de la salut mental, la noció de subjecte i, sobretot, la concepció de salut mental.La inteligencia artificial (IA) se incorpora de forma creciente en sistemas de predicción y detección a la salud mental, un ámbito sanitario tradicionalmente desatendido, infrarrepresentado y de dificultoso acceso. A pesar de ser una opción prometedora, el estudio de la producción de la IA en el campo de la salud mental sigue siendo incipiente, así como los estudios que se centran en cómo esta tecnología concibe la salud mental. Así, para conocer la noción de salud mental detrás del proceso de creación de estos sistemas, este trabajo ha recopilado los discursos de investigadores/as de ciencia computacional que trabajan para la detección y predicción de salud mental con IA. A través de este análisis cualitativo, hemos podido identificar un posicionamiento común respecto a los objetivos de la IA, la valoración de la interdisciplinariedad de la salud mental, la noción de sujeto y, sobre todo, la concepción de salud mental.Artificial intelligence (AI) is increasingly being incorporated into mental health prediction and detection systems, an area of healthcare that has traditionally been neglected, underrepresented, and difficult to access. Despite being a promising option, research of AI for mental health remains incipient, and few attempts are evident that focus on how this technology conceives mental health. Thus, in order to understand the notion of mental health behind the process of creating these systems, we developed an analysis of the discourses of computer science researchers working for the detection and prediction of mental health with AI. In this qualitative study, we identify a common position regarding the objectives of AI, the assessment of the interdisciplinarity of mental health, the notion of the subject/patient and, above all, the conception of mental health

Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Using Computational Psychology to Profile Unhappy and Happy People

Social psychology has a long tradition of studying the personality traits associated with subjective well-being (SWB). However, research often depends on a priori but unempirical assumptions about how to (a) measure the constructs, and (b) mitigate confounded associations. These assumptions have caused profligate and often contradictory findings. To remedy, I demonstrate how a computational psychology paradigm—predicated on large online data and iterative analyses—might help isolate more robust personality trait associations. At the outset, I focussed on univariate measurement. In the first set of studies, I evaluated the extent researchers could measure psychological characteristics at scale from online behaviour. Specifically, I used a combination of simulated and real-world data to determine whether predicted constructs like big five personality were accurate for specific individuals. I found that it was usually more effective to simply assume everyone was average for the characteristic, and that imprecision was not remedied by collapsing predicted scores into buckets (e.g. low, medium, high). Overall, I concluded that predictions were unlikely to yield precise individual-level insights, but could still be used to examine normative group-based tendencies. In the second set of studies, I evaluated the construct validity of a novel SWB scale. Specifically, I repurposed the balanced measure of psychological needs (BMPN), which was originally designed to capture the substrates of intrinsic motivation. I found that the BMPN robustly captured (a) dissociable experiences of suffering and flourishing, (b) more transitive SWB than the existing criterion measure, and (c) unique variation in real-world outcomes. Thus, I used it as my primary outcome. Then, I focussed on bivariate associations. The third set of studies extracted pairs of participants with similar patterns of covarying personality traits—and differing target traits—to isolate less-confounded SWB correlations. I found my extraction method—an adapted version of propensity score matching—outperformed even advanced machine learning alternatives. The final set of studies isolated the subset of facets that had the most robust associations with SWB. It combined real-world surveys with a total of eight billion simulated participants to find the traits most prevalent in extreme suffering and flourishing. For validation purposes, I first found that depression and cheerfulness—the trait components of SWB—were highly implicated in both suffering and flourishing. Then, I found that self-discipline was the only other trait implicated in both forms of SWB. However, there were also domain-specific effects: anxiety, vulnerability and cooperation were implicated in just suffering; and, assertiveness, altruism and self-efficacy were implicated in just flourishing. These seven traits were most likely to be the definitive, stable, drivers of SWB because their effects were totally consistent across the full range of intrapersonal contexts.Gates Cambridge Scholarshi