Data Science for Social Good and Ushahidi

Project Update - July 11, 2013

The Eric & Wendy Schmidt

Data Sciencefor Social GoodSummer Fellowship 2013

www.dssg.io | dssg-ushahidi@googlegroups.com

Ushahidi Workflow

Ushahidi Workflow + DSSG

Data Sets

23,000 reports from 20 datasets

• 22% English

• 35% non-English

• 43% mixed languages

Each report includes text, category, location, sometimes more data

Data SetsAdditional unusable datasets for various reasons (e.g. overly formulaic language)

What is the quality of the existing "gold standard" annotation?

Working on translations of non-English texts

Afghanistan election(peaceful)

Kenyan election(less peaceful)

Data Set Differences

Current Task Status [July 11]

1) Suggest categories.......................

2) Extract named entities...................

(especially locations)

3) Detect language............................

4) Detect (near-)duplicate reports.....End of presentation has more extensive technical details

Toy Demo

http://ec2-54-218-196-140.us-west-2.compute.amazonaws.com/home

Note this is ONLY a basic "toy" user interface to demonstrate the current prototype functionality.

Our plan is to deliver an open-source code library,which Ushahidi will incorporate into the existing user interface.

If link doesn't work -- just look at the screenshots in the next slides. :)

Demo: Example #1

Demo: Example #2

Secondary Project Ideas

1. Detect private info to strip

2. Urgency assessment

3. Filtering irrelevant reports (not strictly spam)

4. Automatically proposing new [sub-]categories

5. Cluster similar (non-identical) reports

6. Hierarchical topic modelling / visualization

Evaluation Plans• Tap into Ushahidi and crisis mapping communities

for feedback

• Simulate past event with our system

• Success metrics:o Increased annotator speedo Increased annotator categorization accuracyo Decreased annotator frustration/tediumo Increased citizen web report speed

Feedback welcome!Contact us at dssg-

ushahidi@googlegroups.com

We would love your input!

See next 4 slides for technical details on our 4 tasks...

or skip if you're happy to stay unaware... :)

1) Suggest categoriesCurrently:

• Simple bag-of-words unigram features

• 1-vs.-all classification (scikit-learn)

• Little categories fewer big categories

• Performance uninspiring :(

Future:

Bigrams... word frequency filter...

balancing positive/negative examples...

topic modeling... hierarchical categories...

2) Extract named entities

Currently:

• NLTK's Named Entity Recognizer

• Eval: pretty good

Future:

• Train location-recognizer on datasets

• Merge types for non-location NEs

• Remember previously-confirmed NEs

3) Detect Language

Currently:

• Existing packages (Bing, python, ...)

Future:

• Evaluate quality

• Allow event-specific language bias

4) Near-Duplicate Detection

Currently:

• SimHash compares distances of message text hashes efficiently

Future:

• Evaluate quality more rigorously

• Explore other methods