发表机构
Interlake High School; Knauss School of Business, University of San Diego; Fox School of Business, Temple University（因特莱克高中; 圣地亚哥大学克劳斯商学院; 天普大学福克斯商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过分析Facebook家具企业图片帖子的视觉、文本和时间特征，运用多种机器学习模型，量化了这些特征对社交媒体参与度的影响，并为组织提供了优化内容策略的建议。
AI 中文摘要
社交媒体平台因其庞大的用户基础和便捷的访问方式，成为传播信息的流行渠道。企业也将社交媒体作为广告过程中的重要组成部分。通过创建高质量的帖子，企业可以增强其参与度指标并增加粉丝数量。尽管已有越来越多的研究考察了社交媒体参与度，但很少有研究同时考察图片帖子的视觉、文本和时间特征，尽管这些特征共同决定了内容在社交媒体上的表现。为了理解社交媒体参与度的重要驱动因素，我们收集了Facebook上家具企业的图片帖子，并使用文本和图像分析方法从中提取视觉、时间及文本特征。我们评估了多种机器学习模型——包括随机森林、轻量梯度提升机（LightGBM）和极端梯度提升（XGBoost）——以利用我们数据中的特征来评估社交媒体参与度的驱动因素和预测能力。我们的研究量化了这些特征与互动之间的关联程度，并提供了组织可能考虑的建议。
英文摘要
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 research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.
Comments10 pages, 2 tables, 1 figure
