Gathers all the information you have mined and creates a proximity matrix. It also creates two files EdgesW.csv and NodeW.csv, which can be used to create your .net file and visualize your network using Infomap.
Prints dictionaries on a file with user ID, following and followers. Following are requested first, then followers. To use, run 'get_data_follow.sh'.
Prints all the tweets and retweets that are tweeted from the moment you run the program. The tweets are filtered by certain keywords.
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Open data/keysAndTokens.json and edit the file
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Add a new dictionary with your credentials and set an username
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Run on_dates_data.py
https://www.mapequation.org/infomap/
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Shaham. (2018, December 12). Generating A Twitter Ego-Network & Detecting Communities. Retrieved from https://towardsdatascience.com/generating-twitter-ego-networks-detecting-ego-communities-93897883d255
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Moujahid, A. (2014, July 21). An Introduction to Text Mining using Twitter Streaming API and Python. Retrieved from http://adilmoujahid.com/posts/2014/07/twitter-analytics/
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Blondel, Vincent D et al. “Fast Unfolding of Communities in Large Networks.” Journal of Statistical Mechanics: Theory and Experiment 2008.10 (2008): P10008. Crossref. Web.
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Bohlin, L., Edler, D., Lancichinetti, A., & Rosvall, M. (2014). Community detection and visualization of networks with the map equation framework. In Measuring Scholarly Impact (pp. 3-34). Springer, Cham.