…nobody knows exactly how many social bots populate social media, or what share of content can be attributed to bots—estimates vary wildly and we might have observed only the tip of the iceberg. These are important questions for the research community to pursue, and initiatives such as DARPA’s SMISC bot detection challenge, which took place in the spring of 2015, can be effective catalysts of this emerging area of inquiry.
Bot behaviors are already quite sophisticated: they can build realistic social networks and produce credible content with human-like temporal patterns. As we build better detection systems, we expect an arms race similar to that observed for spam in the past. The need for training instances is an intrinsic limitation of supervised learning in such a scenario; machine learning techniques such as active learning might help respond to newer threats. The race will be over only when the effectiveness of early detection will sufficiently increase the cost of deception.
The future of social media ecosystems might already point in the direction of environments where machine-machine interaction is the norm, and humans navigate a world populated mostly by bots. We believe there is a need for bots and humans to be able to recognize each other, to avoid bizarre, or even dangerous, situations based on false assumptions of human interlocutors.