On the Presence of Abusive Language in Mis/Disinformation

Matos, Breno de Sousa; Lima, Rennan C.; Almeida, Jussara M.; Gonçalves, Marcos André; Santos, Rodrygo L. T.

doi:10.1007/978-3-031-19097-1_18

Cited by 2 publications

(1 citation statement)

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“…In recent years, pre-trained transformer-based models have been used to extract sentiment from textual content. For example, Cardiff-NLP's model (Barbieri et al, 2020) is one of the cutting-edge NLP tools for sentiment extraction in social media content as stated by Camacho-Collados et al (2022) and Matos et al (2022). Besides, this model has been implemented across multiple domains and related tasks, such as finance (Choya et al, 2022) and opinion polarity analysis (Medina et al, 2022).…”

Section: Sentiment Analysis In Stress Detectionmentioning

confidence: 99%

Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media

Alghamdi

Kumarage

Karami

et al. 2023

ECSM

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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.

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Section: Sentiment Analysis In Stress Detectionmentioning

confidence: 99%