82 research outputs found
Large Language Models Can Infer Psychological Dispositions of Social Media Users
As Large Language Models (LLMs) demonstrate increasingly human-like abilities
in various natural language processing (NLP) tasks that are bound to become
integral to personalized technologies, understanding their capabilities and
inherent biases is crucial. Our study investigates the potential of LLMs like
ChatGPT to infer psychological dispositions of individuals from their digital
footprints. Specifically, we assess the ability of GPT-3.5 and GPT-4 to derive
the Big Five personality traits from users' Facebook status updates in a
zero-shot learning scenario. Our results show an average correlation of r = .29
(range = [.22, .33]) between LLM-inferred and self-reported trait scores.
Furthermore, our findings suggest biases in personality inferences with regard
to gender and age: inferred scores demonstrated smaller errors for women and
younger individuals on several traits, suggesting a potential systematic bias
stemming from the underlying training data or differences in online
self-expression
Rethinking privacy in the age of psychological targeting
"Psychological targeting" is the practice of predicting people's psychological profiles from their digital footprints (e.g. their Facebook profiles, transaction records or Google searches) in order to influence their attitudes, emotions or behaviours with the help of psychologically informed interventions. For example, knowing that a person is extroverted makes it possible to personalise recommendations in a way that aligns with their [...
The Sharp Spikes of Poverty:Financial Scarcity Is Related to Higher Levels of Distress Intensity in Daily Life
Although income is an important predictor of life satisfaction, the precise forces that drive this relationship remain unclear. We propose that financial resources afford individuals a path to reducing the distressing impact of everyday hassles, thereby increasing one's life satisfaction. More specifically, we hypothesize that financial scarcity is associated with greater distress intensity in everyday life. Furthermore, we propose that lower perceived control helps explain why financial scarcity predicts higher distress intensity and lower life satisfaction. We provide evidence for these hypotheses in a 30-day daily diary study (522 participants, 13,733 observations). A second study (N = 376) further suggests that, although everyone relies on social support to ease stress, financial scarcity shrinks the sense one can use economic resources to reduce the adverse impact of daily hassles. Although money may not necessarily buy happiness, it reduces the intensity of stressors experienced in daily life-and thereby increases life satisfaction
Context-Aware Prediction of User Engagement on Online Social Platforms
The success of online social platforms hinges on their ability to predict and
understand user behavior at scale. Here, we present data suggesting that
context-aware modeling approaches may offer a holistic yet lightweight and
potentially privacy-preserving representation of user engagement on online
social platforms. Leveraging deep LSTM neural networks to analyze more than 100
million Snapchat sessions from almost 80.000 users, we demonstrate that
patterns of active and passive use are predictable from past behavior
(R2=0.345) and that the integration of context information substantially
improves predictive performance compared to the behavioral baseline model
(R2=0.522). Features related to smartphone connectivity status, location,
temporal context, and weather were found to capture non-redundant variance in
user engagement relative to features derived from histories of in-app
behaviors. Further, we show that a large proportion of variance can be
accounted for with minimal behavioral histories if momentary context
information is considered (R2=0.44). These results indicate the potential of
context-aware approaches for making models more efficient and
privacy-preserving by reducing the need for long data histories. Finally, we
employ model explainability techniques to glean preliminary insights into the
underlying behavioral mechanisms. Our findings are consistent with the notion
of context-contingent, habit-driven patterns of active and passive use,
underscoring the value of contextualized representations of user behavior for
predicting user engagement on social platforms
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
Latent human traits in the language of social media: An open-vocabulary approach
Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data-language use-at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use
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Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries
Background
Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).
Methods
To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).
Results
The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.
Conclusions
We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being
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