TY - GEN
T1 - Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media
AU - Alghamdi, Zeyad
AU - Kumarage, Tharindu
AU - Karami, Mansooreh
AU - Alatawi, Faisal
AU - Mosallanezhad, Ahmadreza
AU - Liu, Huan
N1 - Publisher Copyright:
© the authors, 2023. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - It is crucial to detect and manage stress as early as possible before it becomes a severe mental and physical health problem. Some authors even introduce stress as a “silent killer” to emphasize the significance of early stress management. Traumatic global events such as COVID-19 have amplified stress throughout online communities and it is quite common to see that social media users often vent about their problems or situations online. The ability to detect a person's stress from their posts on social media platforms like Reddit or Twitter in a timely manner can help early stress management and consequently counters mental health conditions. In order to detect stress from social media posts, we must obtain the characteristics that signal a user's stress. Which motivates us to study how salient features influence stress detection. On social media, text-based methods of communication predominantly overtake verbal forms, which makes these platforms a convenient rich medium with an extensive amount of text content to analyze a user's thoughts and emotions. We present a novel approach that helps improve stress detection on social media textual content with sentiment, emotion, and toxicity features. We design our framework based on multiple Transformer-based state-of-the-art sentiment, emotion, and toxicity analysis tools and models for feature extraction and discuss the stress detection tasks' interpretability via inspecting multiple dimensions. For the evaluation, we use publicly available and high-quality datasets where the social media posts are real, carefully selected and labeled. Our experiments show the influence of the proposed new feature dimensions on stress detection by comparing the state-of-the-art baselines and suggesting future directions in stress detection on social media. Furthermore, our extensive feature correlation analysis highlights different aspects, such as 1) Positive and Negative sentiment, 2) Joy, Sadness, and Fear emotions, and 3) Obscene and Insult toxicity as governing factors in improving stress detection performance.
AB - It is crucial to detect and manage stress as early as possible before it becomes a severe mental and physical health problem. Some authors even introduce stress as a “silent killer” to emphasize the significance of early stress management. Traumatic global events such as COVID-19 have amplified stress throughout online communities and it is quite common to see that social media users often vent about their problems or situations online. The ability to detect a person's stress from their posts on social media platforms like Reddit or Twitter in a timely manner can help early stress management and consequently counters mental health conditions. In order to detect stress from social media posts, we must obtain the characteristics that signal a user's stress. Which motivates us to study how salient features influence stress detection. On social media, text-based methods of communication predominantly overtake verbal forms, which makes these platforms a convenient rich medium with an extensive amount of text content to analyze a user's thoughts and emotions. We present a novel approach that helps improve stress detection on social media textual content with sentiment, emotion, and toxicity features. We design our framework based on multiple Transformer-based state-of-the-art sentiment, emotion, and toxicity analysis tools and models for feature extraction and discuss the stress detection tasks' interpretability via inspecting multiple dimensions. For the evaluation, we use publicly available and high-quality datasets where the social media posts are real, carefully selected and labeled. Our experiments show the influence of the proposed new feature dimensions on stress detection by comparing the state-of-the-art baselines and suggesting future directions in stress detection on social media. Furthermore, our extensive feature correlation analysis highlights different aspects, such as 1) Positive and Negative sentiment, 2) Joy, Sadness, and Fear emotions, and 3) Obscene and Insult toxicity as governing factors in improving stress detection performance.
KW - Emotional Analysis
KW - Natural Language Processing
KW - Social Media
KW - Stress Detection
KW - Toxicity Analysis
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M3 - Conference contribution
AN - SCOPUS:85163159308
T3 - Proceedings of the 10th European Conference on Social Media, ECSM 2023
SP - 42
EP - 51
BT - Proceedings of the 10th European Conference on Social Media, ECSM 2023
A2 - Lupa-Wojcik, Iwona
A2 - Czyzewska, Marta
PB - Academic Conferences Limited
T2 - 10th European Conference on Social Media, ECSM 2023
Y2 - 18 May 2023 through 19 May 2023
ER -