Along with the rapidly increasing influence and importance of advertisements and publicity in social networking services (SNS), considerable efforts are being made to provide user-customized services through an understanding of SNS content. Studies on online purchasing patterns based on user attributes have also been conducted; however, these studies used either only experimental methods (e.g., surveys or ethnographic accounts) or simple user attributes (e.g., age, biological sex, and location) for computational user modeling. This paper, through interviews with professional marketers, identifies their needs to understand multifactorial SNS user (potential customers) attributes—gender (i.e., masculine, feminine, androgynous) and biological sex (i.e., male and female) characteristics—for marketing purposes. Based on 33,752 Instagram posts, we develop a deep learning-based, classification model merged with three modalities—image (i.e., VGG16 feature and gesture), text (i.e., linguistic, tag, sentence, and category), and activity (i.e., reply and day). Our model achieves a better performance in classifying three gender types in the male, female, and male + female cases than the traditional machine learning models. Our study results reveal the applicability of identifying gender characteristics from posts in the marketing field.

中文翻译：

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更好地定位消费者：从 Instagram 帖子中对多因素性别和生理性别进行建模

随着广告和宣传在社交网络服务（SNS）中的影响力和重要性的迅速增加，通过对SNS内容的理解来提供用户定制的服务正在做出相当大的努力。还进行了基于用户属性的在线购买模式研究；然而，这些研究仅使用实验方法（例如，调查或民族志帐户）或简单的用户属性（例如，年龄、生理性别和位置）进行计算用户建模。本文通过对专业营销人员的采访，确定他们需要了解多因素 SNS 用户（潜在客户）属性——性别（即男性、女性、雌雄同体）和生理性别（即男性和女性）特征——以用于营销目的。基于 33,752 个 Instagram 帖子，我们开发了一个基于深度学习的分类模型，融合了三种模式——图像（即 VGG16 特征和手势）、文本（即语言、标签、句子和类别）和活动（即回复和日期）。与传统的机器学习模型相比，我们的模型在男性、女性和男性 + 女性案例中对三种性别类型进行分类时取得了更好的性能。我们的研究结果揭示了从营销领域的帖子中识别性别特征的适用性。和男性+女性的案例比传统的机器学习模型。我们的研究结果揭示了从营销领域的帖子中识别性别特征的适用性。和男性+女性的案例比传统的机器学习模型。我们的研究结果揭示了从营销领域的帖子中识别性别特征的适用性。