用社交媒体数据精准预测体育赛事收视率，助力广告变现。
Buzz to Broadcast: Predicting Sports Viewership Using Social Media Engagement
- 基于社交平台发帖、评论等数据构建回归模型
- 预测误差仅127万观众，决定系数达0.99
- 适合体育营销与广告投放团队参考
准确预测体育赛事收视率对优化广告销售和收入预估至关重要。社交媒体平台（如Reddit）提供了大量用户生成内容，反映观众参与度与兴趣。本文提出一种基于回归的方法，利用社交指标（包括发帖数、评论数、评分及TextBlob、VADER情感分析）预测体育赛事收视人数。通过聚焦主流体育子版块、引入类别特征并按运动类型处理异常值，模型在完整数据集上达到R²=0.99，平均绝对误差（MAE）为127万观众，均方根误差（RMSE）为233万观众。结果表明该模型能有效捕捉观众行为模式，具有显著的赛前收入预测与精准广告投放潜力。
原文摘要 · Abstract (English)
Accurately predicting sports viewership is crucial for optimizing ad sales and revenue forecasting. Social media platforms, such as Reddit, provide a wealth of user-generated content that reflects audience engagement and interest. In this study, we propose a regression-based approach to predict sports viewership using social media metrics, including post counts, comments, scores, and sentiment analysis from TextBlob and VADER. Through iterative improvements, such as focusing on major sports subreddits, incorporating categorical features, and handling outliers by sport, the model achieved an $R^2$ of 0.99, a Mean Absolute Error (MAE) of 1.27 million viewers, and a Root Mean Squared Error (RMSE) of 2.33 million viewers on the full dataset. These results demonstrate the model's ability to accurately capture patterns in audience behavior, offering significant potential for pre-event revenue forecasting and targeted advertising strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。