Arts and Science of Digital Mental Health Support

Tania, Marzia Hoque; Andreev, Ilya; Temdee, Punnarumol

doi:10.1109/ectidamtncon48261.2020.9090741

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…A wide range of technology-enabled support has been investigated in the literature, starting from simple tools such as traditional SMS text messages [ 30 ] to cutting-edge technology such as AI-enabled chatbots [ 31 ]. Mental health and well-being apps have millions of installations worldwide [ 32 ], which makes the demand for such apps evident; however, very few studies have been conducted to verify and validate the capabilities of these apps to bring positive changes in users’ mental well-being.…”

Section: Introductionmentioning

confidence: 99%

Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work

et al. 2022

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Background Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers’ need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace. Objective Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers’ emotions toward the workplace. Methods This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis. Results A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well. Conclusions The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health.

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Section: Introductionmentioning

confidence: 99%