Denial, acceptance and intervention in society regarding female workplace bullying - A mental health study on social media
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.4.70Keywords:
workplace bullying, female bullying, natural language processing, Big Data, sentiment analysis, social computing, machine learning, female bullyingDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Awareness surrounding the #MeToo movement prompts a crucial question: How does society perceive female harassment? Acknowledging the broad nature of this inquiry, we refined our focus to examine society’s perception, specifically concerning workplace bullying of females. This paper dissects the topic of female workplace bullying, revealing distinct perspectives on denial, acceptance, and intervention held by mental health practitioners. Our study initially adopted a broad perspective, investigating society’s outlook on workplace bullying, which we subsequently narrowed down to female workplace bullying. Our preliminary findings unveiled (1) Society’s stance on this issue appeared divided between denial and acceptance, (2) Individuals affected by workplace bullying, particularly females, exhibited clear signs of negative psychological impact, and (3) Interestingly, discussions within society revolved around various intervention techniques aimed at mitigating these psychological effects. To delve deeper into the exploration of intervention techniques, we analyzed the most frequently mentioned hashtags. Consequently, these hashtags shed light on three primary characteristics associated with mental health practitioners: denial, acceptance, and intervention. Our research, employing a natural language processing (NLP) approach, identified these three characteristics as separate hashtags.Abstract
How to Cite
Downloads
Similar Articles
- V Vijayaraj, M. Balamurugan, Monisha Oberai, Machine learning approaches to identify the data types in big data environment: An overview , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Shaik Khaleel Ahamed, Neerav Nishant, Ayyakkannu Selvaraj, Nisarg Gandhewar, Srithar A, K.K.Baseer, Investigating privacy-preserving machine learning for healthcare data sharing through federated learning , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, The role of big data in transforming human resource analytics: A literature review , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V. Babydeepa, K. Sindhu, Piecewise adaptive weighted smoothing-based multivariate rosenthal correlative target projection for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Ravikiran K, Neerav Nishant, M Sreedhar, N.Kavitha, Mathur N Kathiravan, Geetha A, Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Archana G, Vijayalakshmi V, Improving classification precision for medical decision systems through big data analytics application , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
You may also start an advanced similarity search for this article.