docligot / conspiracy-detection-r

Troglodyte Networked Conspiracy Database

Conspiracy Detection Graph Analysis on R

"Conspiracy", definition

• An agreement to perform together an illegal, wrongful, or subversive act.
• A group of conspirators
• A joining or acting together, as if by sinister design

The Problem

• Detecting conspiracies behind disinformation is a natural but difficult objective of forensic research
• Fake news and disinformation channels are often perpetrated by a small and related series of agents
• Researchers are hard-pressed to find common links and relationships between disinformation channels

Solution: Troglodyte

Troglodyte is an online and searchable database that automatically matches sources of disinformation and establishes causal links.

Using Graph Theory to Unearth Conspiracies

• Fake news agents act like digital marketers and also use similar tools that webmasters use
• By detecting common vectors such as IP, usernames, adsense, analytics, text, and images, fake news sites can be clustered together to detect a conspiracy of agents.

Detection Example

Here's a cluster of 80 sites that were joined together using the methodology to detect conspiracy.

R Code Example

Network analysis was performed using various R libraries:

• library(tidyverse)
• library(network)
• library(igraph)
• library(visNetwork)
• library(networkD3)
• library(tidygraph)
• library(ggraph)

Sample executions of the above libraries are provided in this repository adapted from Jesse Sadler's work.

Creating Network Visualizations on R

Building networks require 2 datasets: nodes (entities to connect) and edges (connections between the entities). The connections can represent any concept. In this sample the connections are travel (edges) between locations (nodes). This represents the analysis performed to detect common disinformation agents.

Sample nodes:

id	label
1	Antwerp
2	Haarlem
3	Dordrecht
4	Venice
5	Lisse
6	Het Vlie
7	Hamburg
8	Emden
9	Amsterdam
10	Delft
11	The Hague
12	Middelburg
13	Bremen

Sample edges:

from	to	weight	width
9	13	1	1.2
1	10	68	14.6
1	2	5	2
1	12	1	1.2
1	11	2	1.4
3	2	1	1.2
8	13	1	1.2
2	13	2	1.4
2	10	26	6.2
2	12	1	1.2
2	11	1	1.2
7	13	1	1.2
6	13	1	1.2
5	10	1	1.2
4	2	2	1.4

Nodes and edges can be visualized using any one of the above libraries. Here's a sample using visNetwork. (This is a code snippet, refer to full code sample in the R file in the repo.)

library(visNetwork)
visNetwork(nodes, edges)

Spinglass algorithm to detect agent communities

Once network connections are determined, community detection can be performed to identify possible agent clusters. (This is a code snippet, refer to full code sample in the R file in the repo.)

groups <- spinglass.community(routes_igraph)
nodes_data <- nodes
nodes_data$community <- groups$membership
nodes_data

Result of Spinglass groupings:

id	label	community
1	Antwerp	3
2	Haarlem	2
3	Dordrecht	2
4	Venice	2
5	Lisse	3
6	Het Vlie	1
7	Hamburg	1
8	Emden	1
9	Amsterdam	1
10	Delft	3
11	The Hague	2
12	Middelburg	2
13	Bremen	1

Contact Us

Troglodyte was the 2019 Grand Prize Winner of the Break The Fake Hackathon sponsored by YSEALI. The original team consisted of:

• Dominic Ligot, Data Analysis and Platform Development
• Carlos Nazareno, Disinformation Research
• Nikko Torcita, Operations Model

We welcome collaborators. Contact us via Linked-IN, or email us at [e-mail].

License

This work is provided under the terms of Creative Commons Attribution 4.0 International (CC-BY-4.0) license.