Residualized Factor Adaptation for Community Social Media Prediction Tasks

Mohammadzaman Zamani, H. Andrew Schwartz, Veronica Lynn, Salvatore Giorgi, Niranjan Balasubramanian


Abstract
Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for incorporating socio-demographic contexts.
Anthology ID:
D18-1392
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3560–3569
Language:
URL:
https://aclanthology.org/D18-1392
DOI:
10.18653/v1/D18-1392
Bibkey:
Cite (ACL):
Mohammadzaman Zamani, H. Andrew Schwartz, Veronica Lynn, Salvatore Giorgi, and Niranjan Balasubramanian. 2018. Residualized Factor Adaptation for Community Social Media Prediction Tasks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3560–3569, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Residualized Factor Adaptation for Community Social Media Prediction Tasks (Zamani et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1392.pdf