在社交媒体上使用触发警告:X的文本分析研究
Abigail Paradise Vit1, Rami Puzis2
1Department of Information System, Max Stern Yezreel Valley College, Israel.
PloS one
|April 30, 2025
概括
社交媒体用户越来越多地使用各种内容的触发警告,但大多数潜在的痛苦帖子仍然缺乏它们. 这项研究分析了X (以前的Twitter) 上触发警告的流行率和类型.
科学领域:
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
- 在线安全 在线安全.
背景情况:
- 触发警告对于社交媒体上的内容调节至关重要,它会提醒用户潜在的令人不安的内容.
- 虽然人工智能有助于图像/视频警告,但文本内容往往缺乏自动触发警告,这给作者带来负担,并冒着用户暴露的风险.
- 了解用户定义的触发内容对于改善在线安全和支持至关重要.
研究的目的:
- 检查使用,类型和X (以前的Twitter) 上推文中的触发警告的流行情况.
- 分析与不同触发警告类别相关的情绪和情绪.
- 识别触发警告应用程序中的潜在敏感文本内容的漏洞.
主要方法:
- 使用大型语言模型 (LLM) 进行零射击学习,以对推特中的触发警告类型进行分类.
- 在带有触发警告的推文上进行了情感和情感分析.
- 分析了两个数据集:具有明确触发警告的推文和大量可能触发的推文.
主要成果:
- 随着时间的推移,X上的触发警告使用量有所增加,标记了更广泛的内容类别.
- 分析揭示了与各种触发警告类型相关的普遍情绪和情绪.
- 在X上很大一部分可能触发的内容缺乏任何形式的触发警告.
结论:
- 社交媒体用户对创建更安全的在线社区的意识和努力越来越大.
- 尽管用户主导的努力增加了,但在将触发警告应用于敏感文本内容方面仍然存在很大的差距.
- 需要进一步的研究和平台干预,以确保所有用户的全面内容安全.
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