Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter

TL;DR

This paper explores simple text modification techniques to evade NLP models like stance detection and geotagging on Twitter, revealing effective methods to protect user privacy against automated detection.

Contribution

It demonstrates that inserting typos can significantly reduce stance detection accuracy, and interacting with different users can hinder geotagging models, offering practical privacy-preserving strategies.

Findings

Typos decrease BERT-based stance detection accuracy.

Paraphrasing does not significantly affect stance detection.

User interaction reduces geotagging model performance by nearly 50%.

Abstract

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%.