Mapping community resilience in Wales using open data
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A peer-training workshop funded by the Bristol Doctoral College
Are music streaming behaviours associated with wellbeing?
An interactive public exhibit about machine learning for mental health
Views on social media and its linkage to longitudinal data from two generations of a UK cohort study
Published in Wellcome Open Research, 2020
Focus groups exploring cohort study participant views on linking their social media data.
Recommended citation: Di Cara NH, Boyd A, Tanner AR et al. (2020). "Views on social media and its linkage to longitudinal data from two generations of a UK cohort study ." Wellcome Open Research. 5:44. https://wellcomeopenresearch.org/articles/5-44
Authors: Oliver Davis, Andrew Boyd, Alastair Tanner, Nina Di Cara, Luke Sloan, Tarek Al Baghal, Lisa Calderwood, Claire Haworth
An invited talk with Tiffany Massey (EY) on our submission to the 2019 Data Challenge, hosted by the University of Bristol’s Jean Golding Institute and the Office for National Statistics (ONS). We won the challenge with our approach that used several types of models to consider and report on the impact that changing communities have on local loneliness estimations. You can read more about this in my blog.
Presenting at the CAMH’s research seminar about the results of my recent scoping review on methods for inferring mental health on Twitter.
Designed and delivered an introduction to Python workshop for peers at the annual conference for our doctoral training centre.
Introduction to the methodology and study design behind the ‘Mood Music - Inferring Wellbeing from Spotify’ project.
A talk about the implications of labelling mental health states on social media, and particularly about the bias in the Twitter API.
I spoke on the panel about the ways in which we are using social media to understand mental health, including the ways that researcher decisions impact our modelling processes and what ground truth in mental health looks like from a longitudinal persepctive.