Chatbots Explain Themselves: Designers' Strategies for Conveying Chatbot Features to Users

Abstract

Recently, text-based chatbots had a rise in popularity, possibly due to new APIs for online social networks and messenger services, and development platforms that help dealing with all the necessary Natural Language Processing. But, as chatbots use natural language as interface, their users may struggle to discover which sentences the chatbots will understand and what they can do. Because of that it is important to support their designers in deciding how to convey the chatbots’ features, as this might determine whether the user will continue chatting or not. In this work, our goal is to analyze the communicative strategies used by popular chatbots when conveying their features to users. We used the Semiotic Inspection Method (SIM) for that end, and as a result we were able to identify a series of strategies used by the analyzed chatbots for conveying their features to users. We then consolidate these findings by analyzing other chatbots. Finally, we discuss the use of these strategies, as well as challenges for designing such interfaces and limitations of using SIM on them.