Summary

In this chapter, we described approaches and techniques for detecting bot-generated fake news. With the rising prowess of artificial intelligence and the widespread availability of language models, attackers are using automated text generation to run bots on social media. These sock-puppet accounts can generate real-looking responses, posts, and, as we saw, even news-style articles. Data scientists in the security space, particularly those working in the social media domain, will often be up against attackers who leverage AI to spew out text and carpet-bomb a platform.

This chapter aims to equip practitioners against such adversaries. We began by understanding how text generation exactly works and created our own dataset for machine learning experiments. We then used a variety of features (hand-crafted, TF-IDF, and word embeddings) to detect the bot-generated text. Finally, we used contextual embeddings to build improved mechanisms.

In the next chapter, we will study...