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| Data |[Dataverse](https://dataverse.harvard.edu/privateurl.xhtml?token=58246dfc-bdf8-454d-8edc-60d5918dedfc)|
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This repository is part of a broader investigation of the hidden engagement on Facebook. More information about the project can be found [here](https://github.com/ScholCommLab/facebook-hidden-engagement).
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---
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This repository contains all figures and tables present in the manuscript for "How much research shared on Facebook is hidden from public view?". Output files can be found in:
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-`prepare_data.py` - data preprocessing
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-`analysis.py` - data analysis and outputs
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## Inital Data Collection
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This article is part of a broader investigation of the hidden engagement on Facebook. More information about the project can be found [here](https://github.com/ScholCommLab/facebook-hidden-engagement).
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## Initial Data Collection
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The data used in this paper was collected using our own methods. The data collection method is described in [Enkhbayar and Alperin (2018)](https://arxiv.org/abs/1809.01194). Code & instructions can be found [here](https://github.com/ScholCommLab/fhe-plos).
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The data used in this paper was collected using our own methods. The data collection method is described in [Enkhbayar and Alperin (2018)(https://arxiv.org/abs/1809.01194)]. Code & instructions can be found [here](https://github.com/ScholCommLab/fhe-plos).
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## Reproduce results
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```pip install -r requirements.txt```
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1. Clone this repository and cd into it
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1. Clone this repository and cd into the scripts folder
All the data is hosted on dataverse: [Dataverse repository](https://dataverse.harvard.edu/privateurl.xhtml?token=58246dfc-bdf8-454d-8edc-60d5918dedfc)
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Using the helper script provided, you can download all files into the respective locations.
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Using the helper script provided, you can download all files into the respective locations. Make the script executable and ensure that you have `wget` installed.
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```download_data.sh```
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```
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chmod +x download_data.sh
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./download_data.sh
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```
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3. Preprocess data
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Run the preprocessing script to apply transformations on the input dataset.
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Run the preprocessing script to apply transformations on the input dataset. This step creates the file `data/articles.csv`
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```python process_data.py```
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```python analysis.py```
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Optionally, you can also open the analysis notebook with Jupyter to explore the dataset.
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Optionally, you can also open the notebook `analysis.ipynb` with Jupyter to explore the dataset and results.
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