Denial, acceptance and intervention in society regarding female workplace bullying - A mental health study on social media
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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
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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
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