Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran.
Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America.
PLoS One. 2018 Jul 11;13(7):e0198660. doi: 10.1371/journal.pone.0198660. eCollection 2018.
Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others' posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood. In this study, we analyzed variation in image content with user personality across three interaction types (posts, likes and profile images) and two platforms, using a unique data set of users who are active on both Twitter and Flickr. Usage patterns on these two social media platforms revealed different aspects of users' personality. Cross-platform data fusion is thus shown to improve personality prediction performance.
评估不同社交媒体平台的预测价值对于了解用户在多个平台上自我展示的变化非常重要。大多数社交媒体平台允许用户以多种方式进行互动：发布内容到平台、点赞他人的帖子，或建立用户资料。虽然之前的研究提供了关于语言使用在不同平台上的差异的见解，但对图像使用的差异了解得较少。在这项研究中，我们使用了一个独特的在 Twitter 和 Flickr 上都活跃的用户数据集，分析了用户个性在三种交互类型（帖子、点赞和个人资料图片）和两个平台之间的图像内容变化。这两个社交媒体平台的使用模式揭示了用户个性的不同方面。因此，跨平台数据融合被证明可以提高个性预测性能。
