Poster
in
Workshop: 3rd Workshop on practical ML for Developing Countries: learning under limited/low resource scenarios

AI-powered understanding of family planning behavioural change using the Fogg Model

Wuraola Oyewusi ⋅ Olubayo Adekanmbi ⋅ Mary Ide Salami ⋅ David Oden ⋅ Amina Mardiyyah Rufai ⋅ Emmanuel Akeweje ⋅ Dominique Meekers ⋅ Olaniyi Olutola ⋅ Chidinma Onuoha ⋅ William Sambisa ⋅ Masduk Abdulkarim

2022 Poster
in
Workshop: 3rd Workshop on practical ML for Developing Countries: learning under limited/low resource scenarios

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Abstract

In this work, we share our methodology and findings from applying named entity recognition (NER) using machine learning, to identify behavioural patterns in transcribed family planning client call centre data in Nigeria based on the Fogg’s model. The Fogg Behaviour Model (FBM) describes the interaction of three key elements Motivation (M), Ability (A), and a Prompt (P) and their interaction to produce behavioural change. This work is part of a larger project that is focused on practical application of artificial intelligence to analyse and derive insight from large scale data call centre data. The entity recognition model called Fogg Model Entity Recognition(FMER) was trained using spaCy, an open source software library for advanced natural language processing on a total of 11510 words and F1 score of 98.5%.

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