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%.
翻译:最近自然语言处理的进展在文本分析和语言理解模型方面产生了许多令人兴奋的进展;然而,这些模型也可以用来跟踪人,从而带来严重的隐私问题。在这项工作中,我们调查个人在使用社交媒体平台时可以做些什么以避免这些模型发现这些模型。我们把调查建立在两种暴露风险的任务、姿态探测和地理标记上。我们探索了各种修改文本的简单技术,例如插入显著字词的伤寒、抛光和添加假社交媒体文章。我们的实验表明,基于BERT的模型由于打字而调整的定位探测功能显著下降,但不受抛光的影响。此外,我们发现,由于对社交网络的依赖程度的增加,打字机对最新地理标记模型的影响最小,但我们发现,用户可以通过与不同用户互动来欺骗这些模型,将其性能降低近50%。