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Awesome Open Source

reddit-detective: Play detective on Reddit

Python version Neo4j version Maintenance GitHub license Documentation Status

Quality Gate Status Maintainability Rating Reliability Rating Technical Debt

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/ for detailed information

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(

# Create driver instance
driver = GraphDatabase.driver(
    auth=("your_username", "your_password")

# Create network graph
net = RedditNetwork(
            # 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.add_karma(api)  # Shows karma as a property of nodes, optional

Output (in Neo4j): Result

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 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)


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/ for detailed information

from 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)


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 ="Some cypher code")

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)


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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network (478
graph (404
data (396
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