Play detective on Reddit
Project description
reddit-detective: Play detective on Reddit
pip install reddit_detective
reddit-detective represents reddit in a graph structure using Neo4j.
Created to help researchers, developers and people who are curious about how Redditors behave.
Helping you to:
- Detect political disinformation campaigns
- Find trolls manipulating the discussion
- Find secret influencers and idea spreaders (it might be you!)
- Detect "cyborg-like" activities
- "What's that?" Check
reddit_detective/analytics/metrics.py
for detailed information
- "What's that?" Check
Installation and Usage
- Install Neo4j 4.1.0 here
- Neo4j uses Cypher language as its query language. Knowing Cypher dramatically increases what you can do with reddit-detective Click here to learn Cypher
- Install reddit-detective with
pip install reddit_detective
- Note: Version 0.1.2 is broken, any other version is fine
Code Samples
Creating a Reddit network graph
import praw from neo4j import GraphDatabase from reddit_detective import RedditNetwork, Comments from reddit_detective.data_models import Redditor # Create PRAW client instance api = praw.Reddit( client_id="yourclientid", client_secret="yourclientsecret", user_agent="reddit-detective" ) # Create driver instance driver = GraphDatabase.driver( "url_of_database", auth=("your_username", "your_password") ) # Create network graph net = RedditNetwork( driver=driver, components=[ # Other relationship types are Submissions and CommentsReplies # Other data models available as components are Subreddit and Submission Comments(Redditor(api, "BloodMooseSquirrel", limit=5)), Comments(Redditor(api, "Anub_Rekhan", limit=5)) ] ) net.create_constraints() # Optional, doing once is enough net.run_cypher_code() net.add_karma(api) # Shows karma as a property of nodes, optional
Output (in Neo4j):
Finding interaction score
# Assuming a network graph is created and database is started # Interaction score = A / (A + B) # Where A is the number of comments received in user's submissions # And B is the number of comments made by the user from reddit_detective.analytics import metrics score = metrics.interaction_score(driver, "Anub_Rekhan") score_norm = metrics.interaction_score_normalized(driver, "Anub_Rekhan") print("Interaction score for Anub_Rekhan:", score) print("Normalized interaction score for Anub_Rekhan:", score_norm)
Output:
Interaction score for Anub_Rekhan: 0.375
Normalized interaction score for Anub_Rekhan: 0.057324840764331204
Finding cyborg score
# Assuming a network graph is created and database is started # For a user, submission or subreddit, return the ratio of cyborg-like comments to all comments # A cyborg-like comment is basically a comment posted within 6 seconds of the submission's creation # Why 6? Can't the user be a fast typer? # See reddit_detective/analytics/metrics.py for detailed information from reddit_detective.analytics import metrics score, comms = metrics.cyborg_score_user(driver, "Anub_Rekhan") print("Cyborg score for Anub_Rekhan:", score) print("List of Cyborg-like comments of Anub_Rekhan:", comms)
Output:
Cyborg score for Anub_Rekhan: 0.2
List of Cyborg-like comments of Anub_Rekhan: ['q3qm5mo']
Running a Cypher statement
# Assuming a network graph is created and database is started session = driver.session() result = session.run("Some cypher code") session.close()
Upcoming features
- UserToUser relationships
- A relationship to link users with its only property being the amount of encounters
- Having ties with the same submission is defined as an encounter
- Create a wrapper for centrality metrics of Neo4j GDSC (Graph data science library)
Inspirations
List of works/papers that inspired reddit-detective:
authors: [Sachin Thukral (TCS Research), Hardik Meisheri (TCS Research),
Arnab Chatterjee (TCS Research), Tushar Kataria (TCS Research),
Aman Agarwal (TCS Research), Lipika Dey (TCS Research),
Ishan Verma (TCS Research)]
title: Analyzing behavioral trends in community driven
discussion platforms like Reddit
published_in: 2018 IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining (ASONAM)
DOI: 10.1109/ASONAM.2018.8508687
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