https://github.com/umitkaanusta/reddit-detective
Play detective on Reddit: Discover political disinformation campaigns, secret influencers and more
https://github.com/umitkaanusta/reddit-detective
analysis analytics api data database elt etl graph graph-database neo4j network politics reddit social social-media social-network
Last synced: 8 months ago
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Play detective on Reddit: Discover political disinformation campaigns, secret influencers and more
- Host: GitHub
- URL: https://github.com/umitkaanusta/reddit-detective
- Owner: umitkaanusta
- License: mit
- Created: 2020-10-20T15:10:49.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2023-12-07T02:15:03.000Z (almost 2 years ago)
- Last Synced: 2025-03-30T09:09:55.552Z (8 months ago)
- Topics: analysis, analytics, api, data, database, elt, etl, graph, graph-database, neo4j, network, politics, reddit, social, social-media, social-network
- Language: Python
- Homepage: https://reddit-detective.readthedocs.io
- Size: 269 KB
- Stars: 213
- Watchers: 6
- Forks: 15
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# reddit-detective: Play detective on Reddit
[](https://reddit-detective.readthedocs.io/en/latest/?badge=latest)
[](https://sonarcloud.io/dashboard?id=umitkaanusta_reddit-detective)
[](https://sonarcloud.io/dashboard?id=umitkaanusta_reddit-detective)
[](https://sonarcloud.io/dashboard?id=umitkaanusta_reddit-detective)
[](https://sonarcloud.io/dashboard?id=umitkaanusta_reddit-detective)
```
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
## Installation and Usage
- Install Neo4j 4.1.0 [here](https://neo4j.com/docs/operations-manual/current/installation/)
- Neo4j uses Cypher language as its query language.
Knowing Cypher dramatically increases what you can do with reddit-detective
[Click here to learn Cypher](https://neo4j.com/graphacademy/online-training/introduction-to-neo4j-40/)
- 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
**Note:** Due to limitations of the Reddit API, Each API call may have some delay, so using reddit-detective to collect large data might be unscalable for now. **Functionality to convert tabular Reddit data to network graph will be added.**
```python
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
```python
# 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
```python
# 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
```python
# Assuming a network graph is created and database is started
session = driver.session()
result = session.run("Some cypher code")
session.close()
```
## Upcoming features
- [ ] Convert any tabular Reddit data to Neo4j Graph with given instructions from the user
- [ ] 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**
- [ ] Add more paper-inspired metrics
- [ ] Create a wrapper for centrality metrics of Neo4j GDSC (Graph data science library)
## Inspiration
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
```