scripts

Introduction

In this case study, we aims to estimate which cashtag of $SHIB, $FLOKI, and $AAPL is more amplified by bot-like accounts.

We will first query tweets mentioning these cashtags from Twitter, then query Botometer Pro API to conduct bot detection.

Instructions

Set keys

Before collecting the data, you need to set up your Twitter developer app key and RapidAPI key. Make a copy of keys_template.json using the following command:

cp keys_template.json keys.json

Put your keys in the corresponding locations in keys.json.

Collect tweets

First, we collect the tweets mentioning these cashtags. This can be achieved by the script collect_tweets.py. We need to pass three parameters to it: the cashtag, number of tweets needed, and the path to the file where we want to store the tweets.

We can use the following commands:

python collect_tweets.py '$floki' 2000 ../data/floki_tweets.jsons
python collect_tweets.py '$shib' 2000 ../data/shib_tweets.jsons
python collect_tweets.py '$aapl' 2000 ../data/aapl_tweets.jsons

Extract user ids

Second, we need to extract the unique user ids so that we can perform bot detection later. This can be achieved by the script extract_user_ids.py. It takes two parameters: the input file that contains the tweets we just collected and the output file that contains the unique user ids.

We can use the following commands:

python extract_user_ids.py ../data/floki_tweets.jsons ../data/floki_user_id.csv
python extract_user_ids.py ../data/shib_tweets.jsons ../data/shib_user_id.csv
python extract_user_ids.py ../data/aapl_tweets.jsons ../data/aapl_user_id.csv

Bot detection

Third, we can perform bot detection. This can be done by the script bot_detection.py. It needs two parameters: the input file that contains the user ids to check and the output file where that we want to store the results.

We can use the following commands:

python bot_detection.py ../data/floki_user_ids.csv ../data/floki_bot_scores.jsons
python bot_detection.py ../data/shib_user_ids.csv ../data/shib_bot_scores.jsons
python bot_detection.py ../data/aapl_user_ids.csv ../data/aapl_bot_scores.jsons

Note that bot detection might take longer than the previous steps.

Data analysis

For data analysis, please refer to the jupyter notebook under folder /exps.

Note that we are unable to share the raw tweets we collected due to Twitter's policy. To reproduce the analysis, you will need to collect data by yourself and the results of the analysis will likely change.