The article sets theoretical ground for the content and structure of the “altruistic investment” networking strategy as one of the strategies of individual social behavior. Altruistic investment is based, firstly, on the value of a benevolent attitude toward all people; secondly, the desire of the individual to improve the “social situation” for all its participants and thirdly, it involves conscious action for the sake of the common good, namely, investing resources in maintaining positive group norms and protecting justice. The study used the modified social dilemmas, “Public good dilemma” and “Dictator game under third-party punishment”. Using structural modeling in a sample of 362 people, it was found that the empirical data correspond to the a priori model of altruistic investment in the group’s social capital. The structure of the networking strategy of the individual contains the value, motivational and be havioral components. The role of altruistic investment in the formation of the social capital of the group is shown.

Background. Gender inequality continues to reproduce itself in hidden and ambivalent forms and leads to invisible barriers in women’s careers and lives. The authors were interested in how social perceptions of gender differences would relate to the maintenance of gender inequality in various spheres of life. Objective. The purpose of the presented research was to study social perceptions of gender differences in relation to the subjective significance of the gender inequality issue. Design. The study was conducted via an online survey throughout FebruarySeptember of 2019. The sample included 106 people aged 18 to 68 (M=30.2, σ=10.5), 49% of respondents were women. The authors have developed and tested a questionnaire assessing the adherence to ideas regarding evident gender differences in various spheres of life. The reliability of all scales of the questionnaire has been tested. Respondents also completed a questionnaire identifying their perceptions of gender inequality and shared their life experience with respect to this phenomenon in the form of free description. Results. The following two latent factors reflecting different aspects of gender perceptions have been identified: “Career Inequality” and “Differences in Social Spheres”. Indicators of the subjective significance of gender inequality (which include gender awareness, frequency of gender inequality witnessing, personal experience of gender discrimination and the emotional significance of this experience) were positively correlated with perceptions of career inequalities (these support ideas regarding gender differences when it comes to opportunities for professional realization) and negatively correlated with perceptions of differences within social spheres (these support ideas regarding the existence of essential gender differences within the family, politics and everyday life). Conclusion. Articulation of personal experiences of gender inequality is associated with social perceptions of the absence of essential gender differences in various social domains (egalitarianism) and sensitivity to gender inequality with regards to career opportunities.

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Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales.