NEURAL NETWORK BASED

VISUALIZATION OF COLLABORATIONS

IN A CITIZEN SCIENCE PROJECT

Alessandra M. M. Morais, Rafael D. C. Santos,

M. Jordan Raddick

Motivation: Citizen Science

Neural network based visualization of

collaborations in a citizen science project

3

Citizen Science

Use the power of volunteers to gather or process data.

Using idle computer time.

Collecting data.

Using human intelligence.

Not a new concept, but the web made several

interesting projects possible.

4

Citizen Science Galaxy Zoo

Volunteers classify images of galaxies.

www.galaxyzoo.org

Part of the Zooniverse www.zooniverse.org

5

Citizen Science Galaxy Zoo

150.000 volunteers.

More than 80.000.000 classifications.

60% of the volunteers classified

6

Citizen Science

One important issue: data quality.

More collaborators more data better quality?

Better collaborators better quality?

How to identify different types of collaborators

Non-intrusively.

Without positive or negative reinforcement.

Log analysis.

How to identify and motivate certain categories of

users?

7

Previous Results

Morais, A. M. M.; Raddick, J.; Santos, R. D.

C.; Visualization and characterization of users

in a citizen science project; SPIE Defense,

Security, and Sensing, 2013

The Self-Organizing Map and

Visualization

Neural network based visualization of

collaborations in a citizen science project

9

Kohonens SOM

Neural network for unsupervised learning.

Projection of multidimensional data into a lower-

dimensional lattice.

Quantization: one neuron will be associated/associable

with several data vectors.

Projection: data vectors close in the original

multidimensional space will be close in the lattice.

10

The Basic Algorithm

11

The Basic Algorithm

12

SOM and Visualization

We can use the lattice to visualize a large amount of

multidimensional data.

Must choose a proper representation for the neurons.

Must take advantage of quantization and projection.

13

SOM and Visualization

Icons, Features and Results

Neural network based visualization of

collaborations in a citizen science project

15

Icons

Parallel Coordinates will be used to visualize the users.

Simple, uncluttered icons with few dimensions (few attributes).

Each icon represents a prototype vector and the set of data

vectors assignable to that prototype vector.

16

Features

Main features:

Participation range p: number of days between first and last

recorded interaction.

Participation count d: number of days of activity.

Maximum classification max in a day.

Total classifications total.

Average of classifications per user average.

Considered only the first 600 days of the participation.

17

Features

Features:

a1: p/600 1: long term

a2: d/p 1: frequent during participation

a3: d/600 1: frequent during project

a4: max/total 1: all in a day

a5: total/average 1: close to average user classif.

a6: d visual complement

a7: log10(total) how many classifications

18

Visualizing Volunteers Activity

Activities General View Seven Attributes

19

Visualizing Volunteers Activity

Activities General View Seven Attributes

Curious: very short

activity interval, very

active in this interval,

did not contribute much.

20

Visualizing Volunteers Activity

Activities General View Seven Attributes

Potentials: contributed

sporadically but

significantly.

21

Visualizing Volunteers Activity

Activities General View Seven Attributes

Dedicated: contributed

frequently, contributed

a lot.

22

Visualizing Volunteers Activity

25% or less of correct classifications

23

Visualizing Volunteers Activity

75% or more of correct classifications

24

Sessions and Accuracy

Other visualization example:

a1: number of sessions

a2: average session length in seconds

a3: average number of classifications per session

a4: percentage of correct classifications

Session is defined by periods of inactivity (180 seconds)

25

Visualizing Volunteers Accuracy

Session data and correct classifications

Conclusions

Neural network based visualization of

collaborations in a citizen science project

27

Conclusions

Visualization can give insight on data, but

Many methods, many parameters.

Very hard to find a Aha! solution.

Guided visualization for exploratory analysis very useful.

Kohonens Self-Organizing Map is able to do visual, almost-

fuzzy clustering of multidimensional data.