MyMigrationBot: A Cloud-based Facebook Social Chatbot for Migrant Populations
arXiv:2208.13005 · doi:10.15439/978-83-965897-2-9
Abstract
We present the design, implementation and evaluation of a new cloud-based social chatbot called MyMigrationBot, that is deployed to Facebook. The system asks and answers questions related to user's personality traits and person-job competency fit to give feedback, and potentially support migrant populations. The chatbot's response database is based on reputable socio-psychological tools and can be customised. The system's backend is written with Node.js, deployed to AWS and Twilio, and joined with Facebook through Graph and Messenger APIs. To our knowledge this is the first multilingual social chatbot deployed to Facebook and designed to research and support migrant populations with feedback in Europe. It does not have personality like other bots, but it can study and feedback on migrants' personality and on other customised questionnaires e.g., job-competency fit. The aim of a social chatbot in our research project is to help engage migrants with social research using feedback information tailored to them. It can help migrants to get knowledge about their psycho-social resources and therefore to facilitate their integration process into a receiving labour market. We evaluated the chatbot on a group of 53 people, incl. 23 migrants, and we present the results.
9 pages, 5 figures, 17th Conference on Computer Science and Intelligence Systems (FedCSIS 2022)
References in corpus (13)
- Bootstrap your own latent: A new approach to self-supervised Learning
- Efficient Lifelong Learning with A-GEM
- A Berkeley View of Systems Challenges for AI
- Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
- An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web
- Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- Communication Efficient Federated Learning over Multiple Access Channels
- Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
- FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
- A Survey of Asynchronous Programming Using Coroutines in the Internet of Things and Embedded Systems
- Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR
- Zero-Shot Audio Classification via Semantic Embeddings
- A Combined Dependability and Security Approach for Third Party Software in Space Systems