Social Networks Analysis/NodeXL to analyze a website

profileselen7
SampleProject-BitcoinOTCSNA1.pptx

OTC

Business Context

Bitcoin OTC is an online platform where users can trade Bitcoin (BTC) or pay for services using BTC

All transactions are peer-to-peer

Trust established via rating scale (-10 to 10)

Important for users to identify other trusted users

Risk: Scammers could create shill accounts to self-rate

Questions/Source of Data

Questions:

Which users are colluding to increase ratings to scam legitimate BTC buyers?

Which users have a legitimate and trustworthy reputation?

Source of Data - Bitcoin OTC ‘Web of Trust’:

https://bitcoin-otc.com/viewratings.php

About 6K users rated in database by others

About 32K positive ratings and 3,400 negative ratings

Data Description

‘Web of Trust’ Transactional Ratings Data Example:

Interpreting data above:

Hobodave has limited positive ratings (1-2) with small transactions per notes

Wspnut and hobodave gave reciprical +10 (best) ratings. Possible shill rating.

Wspnut has apparently later defrauded rg and received a -10 (worst) rating.

Node Attributes/Links

Selected users with both extremes +10 and -10 scores in history

Directional nodes (Rater > Rated)

Nodes Legend:

Blue Dot : Rater has positive total score

Orange Dot: Rater has negative total score

Black Dot : Rater has neutral (0) total score

Edges Legend:

Green Line : Rating >5 (Great)

Blue Line : Rating 1-5 (Good)

Black Line : Rating 0 (Neutral)

Yellow Line : -1 to -5 (Poor)

Orange Line: -5 to -10 (Very Poor)

A

B

C

Exhibit A: Probably trustworthy - Mostly good ratings from good raters

Exhibit B: Looks like a potential shill fraud ring

Exhibit C: Use caution - a few transactions but suspicious -10 activity

Network Analyses

Shill Network

Good Raters Give -10

Exhibit B

Exhibit A

Exhibit C

Good raters giving very poor scores

Good raters (blue nodes) often giving moderately good feedback (blue or green edges 11 times). Only one -10 and one +10 interaction. More likely to be trustworthy than B or C...

Likely untrustworthy network at the center. Bad raters give overwhelmingly positive feedback, good raters give -10. Some in the center have only one +10 and one -10 (net 0)

Use caution as only good ratings for center node are from ‘bad’ raters. ‘Good’ raters give very poor reviews to the center. Also reciprocity of good feedback from ‘bad’ raters.

Key Findings/Implications for Stakeholders

Findings:

SNA can be used to investigate users in a trust rating database and identify networks of potential shill collusion

Also users could differentiate when a user is legitimate showing mostly positive ratings from ‘good’ trusted raters

Implications:

Without SNA, users could inadvertently “trust” scammers

Users can identify legitimate, trusted users

How about accounts associated with scammers - there are ten people are setting setup but are not trustworthy.