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discovering_influencers_twitter_ojsp2023

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Name		Name	Last commit message	Last commit date
twitter_data_ff		twitter_data_ff
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
belief_from_text_ben.py		belief_from_text_ben.py
belief_from_text_jour.py		belief_from_text_jour.py
belief_from_text_musk.py		belief_from_text_musk.py
ben_run.py		ben_run.py
date_search.txt		date_search.txt
date_search_2017.txt		date_search_2017.txt
date_search_2017_extended.txt		date_search_2017_extended.txt
date_search_hourly_maker.py		date_search_hourly_maker.py
elon_musk_run.py		elon_musk_run.py
get_network_ben.py		get_network_ben.py
get_network_jour.py		get_network_jour.py
get_network_musk.py		get_network_musk.py
get_tweets_ben.py		get_tweets_ben.py
get_tweets_jour.py		get_tweets_jour.py
get_tweets_musk.py		get_tweets_musk.py
jour_run.py		jour_run.py

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twitter data and code

The data and the code for the Twitter experiments in the paper:

V. Shumovskaia, M. Kayaalp, M. Cemri, and A. H. Sayed, “Discovering influencers in opinion formation over social graphs,” IEEE Open Journal of Signal Processing, pp. 1–20, 2023.

To get the figures in the paper, run these scripts:

python3 elon_musk_run.py

python3 jour_run.py

python3 ben_run.py

To run the sentiment analysis algorithm, please download the language model at https://drive.google.com/file/d/14bAG2IattBsrdqAjRbeBGU6XyHJCAEu_/view?usp=sharing and place it in utils folder.

Find experiments on synthetic data in the following repo asl-epfl/discovering_influencers_social_interactions_icassp2023_ojsp2023.

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