arXiv：cs.LG· Ritwik Singh, Mayukh Majumdar, Subodha Kumar·· 13 小时前
用机器学习预测社交媒体互动量：LightGBM 解释 81.8% 方差
Predicting Social Media Engagement using Machine Learning
AI 导读
研究用 Facebook 家具企业图片帖的视觉、文本和时间特征，评估 Random Forest、LightGBM、XGBoost 等模型对社交媒体互动量的预测能力。主模型 LightGBM 解释了 81.8% 的互动得分方差，为基线模型的 2.3 倍；捕捉帖子偏离品牌常规程度的品牌相对偏差特征是最强预测因子，占总重要性的 51.3%。
正文
Abstract:Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-quality posts, companies can strengthen their engagement metrics and increase their follower count. While a growing body of research has examined social media engagement, fewer studies have jointly examined the visual, textual, and temporal features of image posts, even though these features collectively determine the performance of content on social media. To understand the important drivers of social media engagement, we collect image posts of furniture firms on Facebook and extract visual, temporal, and textual features from them using text and image analytics methods. We evaluate several machine learning models--including Random Forest, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)--to assess the drivers and the prediction power of social media engagement using the features from our data. Our primary model, LightGBM, explains 81.8% of the variance in the engagement score, 2.3 times that of our baseline model. We observed that brand-relative deviation features, which capture how far a post departs from its brand's norms, are the strongest predictors, accounting for 51.3% of total importance. Our research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.
| Comments: | 10 pages, 2 tables, 1 figure. Accepted at 19th International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2026) as Working/Poster Papers (non-archival proceedings) |
| Subjects: | Social and Information Networks (cs.SI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.16082 [cs.SI] |
| (or arXiv:2609.16082v2 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16082 arXiv-issued DOI via DataCite |
Submission history
From: Mayukh Majumdar [view email]
[v1]
Mon, 14 Sep 2026 00:12:26 UTC (583 KB)
[v2]
Thu, 8 Oct 2026 21:00:42 UTC (580 KB)
来源：arXiv：cs.LG · arxiv.org