基于Twitter的性别认同使用变形器
Zahra Movahedi Nia1,2, Ali Ahmadi3,4, Bruce Mellado1,5
1Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Canada.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
这项研究引入了一个基于变压器的模型,可以从社交媒体上的图像和推特中预测用户的性别,通过访问私人人口统计数据来增强健康研究. 多式联网方法实现了高精度,超越现有方法.
科学领域:
- 计算社会科学 计算社会科学
- 机器学习用于健康研究
- 自然语言处理和计算机视觉用于社交媒体分析.
背景情况:
- 社交媒体数据为健康研究提供了宝贵的见解,包括心理健康和社会经济不平等.
- 用户人口统计数据,如性别,对于更深入的分析至关重要,但通常是私人的,不可用.
- 从社交媒体上进行性别预测的现有方法是有限的,特别是当用户不提供明确的性别信息或可识别图像时.
研究的目的:
- 开发和评估一种多式联网深度学习模型,用于从社交媒体上的图像和文本 (推文) 中预测用户的性别.
- 为了比较各种基于变压器的模型在性别预测中的图像和文本分类任务的性能.
- 在社交媒体研究中证明将图像和文本数据结合在一起的好处,以便在社交媒体研究中更准确,更强大的性别识别.
主要方法:
- 微调变压器模型,包括视觉变压器 (ViT),LeViT和Swin变压器,用于使用Kaggle和PAN-18数据集中的个人资料图像和用户发布的图像进行性别分类.
- 微调变压器模型,如BERT,RoBERTa和ELECTRA,用于基于用户推特的性别预测.
- 使用曼-惠特尼U测试评估图像和文本分类模型的意义,并将模型结合起来以提高准确性.
主要成果:
- 在图像数据 (Kaggle和PAN-18) 和文本数据 (tweet) 上微调的变压器模型显示出显著的性别预测能力.
- 结合的多式联络方法显著提高了比单个图像或文本模型的准确性.
- 多式联络模型的整体准确度很高 (88.11%在Kaggle上,89.24%在PAN-18上),超过了最先进的方法.
结论:
- 综合图像和文本分析的多式联络方法有效地从社交媒体数据中预测用户的性别.
- 基于变压器的模型在性别识别的图像和文本分类方面表现强.
- 这种方法为需要人口统计数据,特别是性别数据的研究人员提供了有价值的工具,以推进与健康相关的社交媒体研究.
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