论文标题
我知道您来自哪里：社交媒体来源对AI模型性能的影响
I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance
论文作者
论文摘要
如今，社交网络在人类日常生活中起着至关重要的作用，而不再纯粹与业余时间支出有关。实际上，与朋友和同事的即时沟通已成为我们日常互动的重要组成部分，从而增加了多种新的社交网络类型的出现。通过参与此类网络，个人生成了许多数据点，从不同的角度描述其活动，例如，可以进一步用于应用程序，例如个性化建议或用户分析。但是，尚未对不同的社交媒体网络对机器学习模型性能的影响进行全面研究。特别是，关于从多个社交网络建模的多模式数据建模的文献相对较少，这激发了我们在这项初步研究中更深入地研究该主题的文献。具体来说，在这项工作中，我们将研究来自不同社交网络的多模式数据的不同机器学习模型的性能。我们最初的实验结果表明，社交网络选择会影响性能，并且数据源的正确选择至关重要。
Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.
