Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis

Radwan, Ahmad; Amarneh, Mohannad; Alawneh, Hussam; Ashqar, Huthaifa I.; AlSobeh, Anas; Magableh, Aws Abed Al Raheem

doi:10.4018/ijwsr.338222

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…This issue has a significant impact on the user experience and can be challenging to deal with due to the ever-changing nature of new tactics and the increasing complexity of evolving spam. Therefore, the current DL methods are facing challenges in identifying social spam and keeping up with the ever-growing complexity of evolving spam [5]. The most proficient spam campaigns have a notable economic aspect that engages in questionable tactics to boost website traffic and generate false promotional results, leading to significant financial repercussions that present a multifaceted threat that impacts internet users globally, and search engine optimization (SEO) can be seen clearly, posing challenges for providers of these services and affecting search results too.…”

Section: Introductionmentioning

confidence: 99%

“…BERT and ELMo are two cutting-edge models that use pretrained representations to pick up on the semantic and syntactic information of words in different situations. This lets them learn from each other and improve their performance [5]. We apply these models to Twitter and YouTube data and compare them with older methods to find the best RNN architectures for classifying spam.…”

Section: Introductionmentioning

confidence: 99%

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Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

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As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis

Ying,

Pranolo,

Nuryana

et al. 2024

Telematics and Informatics Reports

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Understanding Public Attitudes Towards Human Papillomavirus Vaccination in Japan: Insights from Social Media Stance Analysis Using Large Language Models

Liu,

Niu,

Nagai-Tanima

et al. 2024

Preprint

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Background: Despite the reinstatement of proactive human papillomavirus (HPV) vaccine recommendations in 2022, Japan continues to face persistently low HPV vaccination rates, posing significant public health challenges. Misinformation, complacency, and accessibility issues have been identified as key factors undermining vaccine uptake. Objective: This study aims to analyze the evolution of public attitudes towards HPV vaccination in Japan by examining social media content. The goal is to understand how factors such as misinformation, public health events, and attitudes toward other vaccines, like COVID-19, influence vaccine hesitancy. Methods: We collected tweets related to HPV vaccine from 2011 to 2021. Traditional natural language processing (NLP) methods and large language models (LLMs) was utilized to perform stance analysis on collected data. The analysis included stance identification, time series analysis, topic modeling, and logic analysis. We framed our findings within the context of the WHO's 3Cs model: Confidence, Complacency, and Convenience. Results: Public confidence in the HPV vaccine fluctuated in response to government policies and media events, with misinformation playing a critical role in eroding trust. Complacency increased following the suspension of recommendations in 2013 but decreased as advocacy resumed in 2020. Accessibility (Convenience) was also found to be a key determinant of vaccination uptake. Increased confidence in HPV vaccines appeared to have a positive influence on confidence in other vaccines, such as COVID-19. Our findings suggest the need for targeted interventions focusing on addressing misinformation, improving accessibility, and maintaining consistent communication to enhance vaccine confidence. Conclusions: Our findings underscore the importance of targeted public health interventions to restore and maintain vaccine confidence in Japan. While vaccine confidence has shown a slow increase, sustained efforts are necessary to secure long-term improvements. Confidence in one vaccine may positively influence perceptions of other vaccines. Addressing misinformation, reducing complacency, and enhancing vaccine accessibility are key strategies to improve uptake. This study also demonstrates the utility of LLMs in offering a deeper understanding of public health attitudes. To effectively combat vaccine hesitancy and improve coverage, interventions must prioritize consistent communication, localized strategies, and an integrated approach to vaccine narratives. Keywords: HPV vaccine; vaccine confidence; large language model; stance analysis; topic modeling

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Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis

Cited by 11 publications

References 32 publications

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis

Understanding Public Attitudes Towards Human Papillomavirus Vaccination in Japan: Insights from Social Media Stance Analysis Using Large Language Models

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