Code for The structure of online social networks modulates the rate of lexical change

Published in NAACL-HLT 2021 (Link).

Data

• List of subreddits: samples.csv
• Neologisms: neologisms.csv

Hardware

• The raw data is around 2TB;
• The generated graph data are around 300GB;
• The generated user and vocabulary lists, and immediate data may take up a few hundred GBs;
• We use 8~10 cores and 120 GB of memory to parallelize many of the computations. For example, extracting all graphs may take more than a week using a single core but can be completed in less than 2 days using 8 cores;
• It should take less than two weeks to complete all the computation in our case.

Steps

• Download data -> download_map.py
• Get all users and vocabulary -> get_words_users_map.py
• Intra-community graph construction -> graph_map.py
• Inter-community graph contruction -> intercomm_graph.py
• Generating random baseline graphs -> randomgraph_baseline.py
• Extract graph features -> graph_stats.py
• Poisson regression -> poisson_preprocessing.py and poisson_pred_pca.py
• Survival analysis -> survival_processing.py and survival_analysisi_pca.py

You can cite this article as

@inproceedings{zhu-jurgens-2021-structure,
    title = "The structure of online social networks modulates the rate of lexical change",
    author = "Zhu, Jian  and
      Jurgens, David",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.178",
    pages = "2201--2218"
}