Modeling the predictive power of emotion recognition (via CNN) on sports social media engagement

Sports Social Media (SM) has become an essential platform for clubs, leagues, and sponsors aiming
to promote sustainable practices and engage fans in an emotionally meaningful way. This study explores the predictive power of Convolutional Neural Networks (CNNs) in boosting SM communication by recognizing and interpreting emotional cues in images and video streams captured in sports contexts – live broadcasts, fan‑cams, and supporter‑generated content. CNNs contribute to tasks such as crowd sentiment analysis, content moderation, deepfake detection, and real‑time emotional feedback – all critical for ethical, emotionally intelligent sports marketing.

This research presents a neural network model designed to quantify the impact of CNN-based emotion
recognition on four key aspects of social media engagement: audience understanding, emotional content triggers, storytelling effectiveness, and responsiveness to social trends. Using a multilayer perceptron architecture trained on facial and webcam data, the model evaluates the relationship between CNN features and sustainability-focused communication outcomes. Findings indicate that CNN training iterations, facial image variability, and real-time webcam data are the most influential predictors of effective sports social media (SM), while emotional labeling and training data diversity play moderate roles.

The model demonstrates practical utility for neuromarketingdriven campaign personalization and provides a foundation for ethical, data-informed sustainability messaging.
Despite challenges related to privacy and bias, CNN-based AI systems hold significant potential to transform digital marketing strategies into adaptive, responsive, and emotionally intelligent frameworks.