Neural Response Prediction for Social Media Content: A Survey-Based Framework Using Brain Predictive Models
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| Autores principales: | , , , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2026
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| _version_ | 1866901621916565504 |
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| author | Dr. Prashant Wakhare Krushi Soni Samrat Shetty Sohan Valse |
| author_facet | Dr. Prashant Wakhare Krushi Soni Samrat Shetty Sohan Valse |
| contents | <p>The rapid growth of social media platforms has created a strong need for tools that can predict user engagement before content is published. Traditional metrics such as likes, shares, and comments only provide post-publication feedback and fail to capture the subconscious neural processes that influence user behavior.</p> <p>This paper presents a comprehensive survey of twenty-three research studies across four key domains: fMRI-based deep learning models, neuromarketing, emotion recognition from neural signals, and machine learning-based engagement prediction. The study analyzes methodologies, datasets, and findings from each domain to evaluate the feasibility of predicting neural responses to digital content.</p> <p>The findings suggest that modern brain predictive models, such as TRIBE v2, combined with deep learning techniques, enable high-resolution prediction of neural activity associated with attention, emotion, and reward. Additionally, neuromarketing studies demonstrate that neural responses to content occur within milliseconds and strongly correlate with engagement outcomes.</p> <p>Despite significant progress, a major research gap exists in integrating neural prediction, interpretation, and real-world content evaluation into a unified system. This paper highlights this gap and proposes future directions, including applications in social media optimization, real estate, e-commerce, and ethical frameworks for neural data usage.</p> <p>Overall, the study concludes that neural engagement prediction is no longer a theoretical concept but an achievable system, shifting content evaluation from reactive analytics to proactive prediction.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19795545 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Neural Response Prediction for Social Media Content: A Survey-Based Framework Using Brain Predictive Models Dr. Prashant Wakhare Krushi Soni Samrat Shetty Sohan Valse Neural Response Prediction Brain Predictive Models fMRI Decoding Neuromarketing Social Media Engagement Deep Learning EEG Emotion Recognition Cognitive Engagement Content Analytics TRIBE v2 <p>The rapid growth of social media platforms has created a strong need for tools that can predict user engagement before content is published. Traditional metrics such as likes, shares, and comments only provide post-publication feedback and fail to capture the subconscious neural processes that influence user behavior.</p> <p>This paper presents a comprehensive survey of twenty-three research studies across four key domains: fMRI-based deep learning models, neuromarketing, emotion recognition from neural signals, and machine learning-based engagement prediction. The study analyzes methodologies, datasets, and findings from each domain to evaluate the feasibility of predicting neural responses to digital content.</p> <p>The findings suggest that modern brain predictive models, such as TRIBE v2, combined with deep learning techniques, enable high-resolution prediction of neural activity associated with attention, emotion, and reward. Additionally, neuromarketing studies demonstrate that neural responses to content occur within milliseconds and strongly correlate with engagement outcomes.</p> <p>Despite significant progress, a major research gap exists in integrating neural prediction, interpretation, and real-world content evaluation into a unified system. This paper highlights this gap and proposes future directions, including applications in social media optimization, real estate, e-commerce, and ethical frameworks for neural data usage.</p> <p>Overall, the study concludes that neural engagement prediction is no longer a theoretical concept but an achievable system, shifting content evaluation from reactive analytics to proactive prediction.</p> |
| title | Neural Response Prediction for Social Media Content: A Survey-Based Framework Using Brain Predictive Models |
| topic | Neural Response Prediction Brain Predictive Models fMRI Decoding Neuromarketing Social Media Engagement Deep Learning EEG Emotion Recognition Cognitive Engagement Content Analytics TRIBE v2 |
| url | https://doi.org/10.5281/zenodo.19795545 |