https://github.com/ichalkiad/streamlitclimategpcausality
An interactive app for an exploratory case study on statistical causality relationships in the climate change-related Twitter debate during 2022.
https://github.com/ichalkiad/streamlitclimategpcausality
Last synced: 2 months ago
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An interactive app for an exploratory case study on statistical causality relationships in the climate change-related Twitter debate during 2022.
- Host: GitHub
- URL: https://github.com/ichalkiad/streamlitclimategpcausality
- Owner: ichalkiad
- License: mit
- Created: 2023-10-05T09:29:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-06T08:33:28.000Z (over 1 year ago)
- Last Synced: 2025-02-08T09:24:15.147Z (4 months ago)
- Language: Python
- Size: 5.37 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Gaussian Process-based statistical causal relationships in the 2022 climate Twittersphere
[An interactive app for an exploratory case study on statistical causality relationships in the climate change-related Twitter debate during 2022](https://climategpcausality.streamlit.app/).
Using the daily Tweets in 2022 that were pertinent to climate change [5], a text-based sentiment time-series signal was constructed using the methodology of [3,4]. We subsequently assessed, using the Gaussian Process-based framework of [1,2], the statistical causal relationships between the daily sentiment signal and the signal of the daily number of Tweets from two user communities (pro-climate and climate change denialists, [5]). The existence of causal relationships was investigated in the mean, as well as the mean and covariance of the Gaussian Process, using time-series lags of 1,3 and 5 days when fitting the GP model.
Technical references:
1. [Zaremba AB, Peters GW. Statistical Causality for Multivariate Nonlinear Time Series via Gaussian Process Models. Methodology and Computing in Applied Probability. 2022;24(4):2587-632.](https://doi.org/10.1007/s11009-022-09928-3)
2. [Zaremba AB. Assessing causality in financial time series. UCL (University College London); 2022.](https://discovery.ucl.ac.uk/id/eprint/10143981)
3. [Chalkiadakis, I., Yan, H., Peters, G.W. and Shevchenko, P.V., 2021. Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments. PLoS One, 16(6), p.e0253381.](https://doi.org/10.1371/journal.pone.0253381)
4. [Chalkiadakis IM. Statistical natural language processing and sentiment analysis with time-series: embeddings, modelling and applications. Heriot-Watt University, School of Engineering and Physical Sciences; 2022.](http://hdl.handle.net/10399/4594)
Context and data reference:
5. [David Chavalarias, Paul Bouchaud, Victor Chomel, Maziyar Panahi. The new fronts of denialism and climate skepticism: Two years of Twitter exchanges under the macroscope. 2023. ⟨hal-04103183v2⟩](https://hal.science/hal-04103183v2)