Abstract The productivity of software developers is influenced by various factors encompassing technical, organizational, and individual aspects. Among these factors, mental health has emerged as a critical element for sustaining performance and well-being in this context. This work primary focus lies in understanding developers’ perceptions and acceptance of metrics related to mental health and productivity. Firstly, a mapping study was conducted to map the existing knowledge in this area. A total of 178 papers were retrieved using a predefined search string. After applying strict inclusion and exclusion criteria, five secondary studies were selected and 99 factors influencing productivity and/or mental health in the workplace were identified. Secondly, a metrics catalog was developed based on these influencing factors, combining established indicators from literature with new metrics designed to monitor developers’ mental health and productivity. The catalog includes 12 metrics along with their respective measurement formulas. Thirdly, an assessment of this catalog was performed through a survey and structured interviews with industry professionals, gathering insights on the applicability and relevance of the proposed metrics. The survey was completed by 47 software developers, 22 of whom also participated in the interviews. Our results reveal that while developers largely recognized the value of the catalog, resistance emerged when these metrics were positioned as evaluative mechanisms in the workplace.

Social media platforms provide a direct, real-time channel for companies to engage with their audiences, making the management of corporate reputation on social media a pivotal factor for organizational success. Reputation is shaped by factors such as the relationship with the audience, crisis communication, public sentiment, and the topics most frequently associated with the company. Although reputation is often viewed as an intangible asset, it can be quantified and monitored through various metrics. Advances in AI and Pretrained Language Models (PLMs) have automated tasks such as sentiment analysis, sentiment strength assessment, and topic classification, creating new opportunities to manage reputation more efficiently. However, current PLM solutions typically address these tasks individually and require specialized training for each company, limiting scalability and flexibility. To address these challenges, we propose a novel system framework to assist public relations firms by leveraging PLMs for automatic classification. We evaluated this approach through experiments on four companies, comparing zero-shot and fine-tuned models in task-specific and multi-task configurations. Additionally, we explored the transfer of knowledge between client companies using fine-tuned models. Results indicate that multi-task, multi-company fine-tuned PLM models offer simpler system management with competitive performance compared to highly specialized models.

Major Depression Disorder (MDD) is a mental condition that causes severe impairments in a person's life. Early detection of such a condition is imperative, especially to detect cases where the person does not even know that they are affected by it. Social media provide a ubiquitous platform with self-generated data that Machine Learning models enhanced with Natural Language Processing mechanisms can extensively explore. Nonetheless, we advocate that previous Machine Learning approaches to early detection of MDD do not adequately model the problem at hand. Most assume that the isolated publications on social media are enough for early detection. This paper proposes a new problem formulation for this task using the multiple-instance learning paradigm. Furthermore, we include a theoretical and experimental analysis of the method on a dataset of Brazilian university students using the transformer and LSTM architectures. The results indicate that the proposed approach yields better accuracy and explanation capabilities than previous studies that do not rely on multiple-instance learning.

Depression has increased at alarming rates in the worldwide population. One alternative to finding depressed individuals is using social media data to train machine learning (ML) models to identify depressed cases automatically. Previous works have already relied on ML to solve this task with reasonably good F-measure scores. Still, several limitations prevent the full potential of these models. In this work, we show that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.

Smart cities initiatives have the potential to improve the life of citizens in a huge number of dimensions. One of them is the development of techniques and services capable of contributing to the enhancement of security public policies. Finding criminal patterns from historical data would arguably help in predicting and even preventing thefts and burglaries that continuously increase in urban centers worldwide. However, accessing such history and finding patterns across the interrelated crime occurrences data are challenging tasks, particularly to underdevelopment countries. In this paper, we address these problems by combining three techniques: we collect crime data from existing crowd-sourcing systems, we automatically induce patterns with relational machine learning, and we manage the entire process using scientific workflows. The framework developed under these lines is named CRiMINaL (Crime patteRn MachINe Learning). Experimental results conducted from a popular Brazilian source of data and a traditional relational learning system shows that CRiMINaL is a promising tool to induce interpretable models that can assist police departments on crime prevention.