This repository contains the data and code of the paper "Message Distortion in Information Cascades" (which you can read clicking here):
@inproceedings{horta_ribeiro_message_2019,
author={Ribeiro, Manoel Horta and Gligori\'c, Kristina and West, Robert},
title={Message Distortion in Information Cascades},
booktitle={Proceedings of the 2019 World Wide Web Conference},
year={2019},
}Check out the accompanying website which allows you to visualize the data.
You may find the data in the data folder ./data (duh).
We make the data available in two formats: a .csv and .graphml.
The latter is the format used in the analysis of the data (for convenience).
| Field | Null in Root | Description |
|---|---|---|
| node_id | No | Unique identifier of the node. |
| level | No | Summarization level: (0: original abstract, 1: ~1024 chars, 2: ~512 chars, 3: ~256 chars, 4: ~124 chars, 5: ~64 chars) |
| branch | No | Source note used in this information cascade. |
| question | No | node_id of the text used as reference for the summarization, in case of root nodes, it is the same as node_id |
| Topic | No | Topic of the paper summarized (breast, cardio, immunization, diet) |
| Answer | No | Original abstract in the case of the root, summary otherwise. |
| Age | Yes | Age range of the worker which summarized the paper (18-24/24-39/40-60/60+) |
| Education | Yes | Education level of the worker which summarized the paper (Some High School, High School, Some College, College) |
| Gender | Yes | Gender of the worker which summarized the paper (male, female) |
| Qualification | Yes | Performance on qualification test (float, 0-1) |
| WorkerId | Yes | Unique worker identifier as provided by amazon mt |
| Doggos_crowd | No | Dictionary containing the values for facts in each category. {"Coarse":{"Coarse_category1":["Val1", "Val2", ...] ...}, "Fine":{"Fine_category1":["Val1"} ...} |
| Doggos_text | No | Dictionary containing the text for facts of each sub category. Null in non-root. {"Fine_category1": "Text1", "Fine_category2": "Text2", ... } |
| Tagging | No | In the csv files, this is a Dictionary, similar to doggos crowd, containing the keyphrases associated with each subcategory. {"Coarse": {"keyword1": {"Course_category1", ...}, ... }, "Fine": {"keyword1": {"Fine_category1", ...}, ... }. For the graphml files, this is actually a python object with this dictionary, and a bunch of helper functions to calculate the difference in keywords across hops. |
All the analysis performed may be found in the analyses.ipynb notebook.
To install all requirements simply run
pip install -r requirements.txt