在线检测家庭暴力幸存者的信息搜索帖子:为广东语进行上下文代码混合
Zhai Yu1, Lillian Tian2, Emmanuele Chersoni1
1Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究开发了一种自动化系统,用于检测在广东社交媒体上寻求帮助的家庭暴力 (DV) 幸存者. 该系统实现了0.84准确度,改善了对低资源语言社区的支持.
科学领域:
- 自然语言处理自然语言处理.
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 家庭暴力 (DV) 幸存者越来越多地使用社交媒体寻求支持.
- 在广东社交媒体上检测DV幸存者的需求是具有挑战性的,因为语言资源的限制和主题的代表性不足.
- 手动识别DV幸存者的信息需求是劳动密集型和耗时的.
研究的目的:
- 开发一个自动化系统来检测DV幸存者的信息搜索行为在广东社交媒体.
- 提高识别需要支持的DV幸存者的效率和准确性.
- 解决在线DV支持中低资源语言所带来的挑战.
主要方法:
- 使用轻量级的大型语言模型 (LLM) 与指令调策略.
- 实现了上下文代码混合 (ICM),以处理社交媒体帖子中的语言变化.
- 开发了一个自动化系统来过和合成来自广东网站的帖子.
主要成果:
- 自动化系统在上下文学习中实现了0.84的准确度得分.
- 在语境代码混合 (ICM) 性能超过单语言指令.
- 较高的代码混合比率与低资源语言中DV幸存者的更好的检测结果相关.
结论:
- 在LLM上使用ICM的自动化系统可以有效地检测DV幸存者在广东社交媒体上的信息需求.
- 这种方法提供了一种更有效和保护隐私的方法来识别处于风险中的个人.
- 这些发现突显了LLM在支持低资源语言环境中的弱势群体方面的潜力.
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