论文标题
如果可以的话，请抓住我：欺骗立场检测和地理拍打模型，以保护个人在Twitter上的隐私
Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter
论文作者
论文摘要
自然语言处理的最新进展在文本分析和语言理解模型中产生了许多令人兴奋的发展。但是，这些模型也可以用于跟踪人们，引起严重的隐私问题。在这项工作中，我们调查了个人可以在使用社交媒体平台时避免被这些模型检测到的事情。我们将调查在两项暴露危险任务，立场检测和地理标记中进行。我们探索了各种用于修改文本的简单技术，例如用显着词，释义和添加虚拟社交媒体帖子插入错别字。我们的实验表明，基于BERT的模型的性能因错别字而被罚款以进行姿势检测，但不受释义的影响。此外，我们发现错别字对最先进的地理作品模型的影响很小，因为它们对社交网络的依赖增加了。但是，我们表明用户可以通过与不同用户互动来欺骗这些模型，从而将其绩效降低近50％。
The recent advances in natural language processing have yielded many exciting developments in text analysis and language understanding models; however, these models can also be used to track people, bringing severe privacy concerns. In this work, we investigate what individuals can do to avoid being detected by those models while using social media platforms. We ground our investigation in two exposure-risky tasks, stance detection and geotagging. We explore a variety of simple techniques for modifying text, such as inserting typos in salient words, paraphrasing, and adding dummy social media posts. Our experiments show that the performance of BERT-based models fined tuned for stance detection decreases significantly due to typos, but it is not affected by paraphrasing. Moreover, we find that typos have minimal impact on state-of-the-art geotagging models due to their increased reliance on social networks; however, we show that users can deceive those models by interacting with different users, reducing their performance by almost 50%.
