[Notes] Fabio Ciravenga at #SSSW2013
Make a model of what is happening.
WeSenseIt \\\\- citizen water observations.
River belongs to citizens, not authorities.
Physical sensors (hard layer) are expensive and brittle.
So use people instead (soft layer, social).
Give people small sensors. Phones.
Then you just need software for information management.
- capture.
- integrate and correlate data.
- share.
Can't rely on phones.
Old people in Doncaster.
Give them easy sensors instead.
- camera
- humidity
- position GPS
- water depth, velocity
- rainfall via accelerometer
- could coverage via luminocity
Costs about EUR 80.
Open Source & hackable.
Not expected to substitute professional sensors, but a way to crowdsource information you would never get.
In Delft
Give people flood preparation advice and record who ticks things off, to build a picture of who/how/when preparations take place.
The Floow Ltd
"Commercialises data solution for telematic insurance."
World divided 10x10m squares, sense things everywhere.
Traffic risks.
Sensors tell you people are going somewhere, not why.
That's what social media can tell you.
Monitoring development of a house fire via Twitter.
Seeing events through the eyes of the community.
Social streams:
- High volume
- Duplicated, incomplete, imprecise, incorrect
- Time sensitive / short term
- Informal
- Only 140 characters
- Spam
Large music festival. Monitor geolocated messages, trends, topics and relations.
Most 'critical' events were management issues.
Developing system to warn you automatically about things to pay attention to.
Look/listen for event within 72 hours. 10 minutes to find out what it was.
\\\\- Simulation of station bombing.
Minute by minute description of event.
1.5 billion messages.
- Linguistic issues
- Alternative language
- Negatives
- Conditional statements
- Hope/prayer statements
- Irony/sarcasm
- Ambiguity
- Unreliable capitalisation
- Data sparsity
Four things when monitoring:
- What
- Identify, classify, cluster
- Events and sub-events
- Involved entities
- Identify, classify, cluster
- Who
- Human or not?
- Bots can be beneign, but many are a serious risk.
- Bots that pretend to be humans.
- When
- Where
Big problem - people tweet crap!
People don't realise when people nearby are in danger.
Deception on social media
False crowdsourcing political support on social networks.
Smear campaigns using bots.
Bots to foster / prevent social unrest.
Identifying bots
23 behavioural features.
Feature set is open.
Recognise 90% of bots - more than humans can do.
Very small amount of tweets are geolocated, it's useless.
Have to use the text.
Timestamp is not necessarily correct.
Issues in events
No infrastructure (eg. at music festivals).
Phone signal issues, phone charging issues.
Most tweets from outside event.
Conclusions
Need to convince citizens that authorities are not spying on them.
Need to convince authorities that citizens are not all criminals.
Privacy and legality issues.
Creating a company on this research would be unethical.
Need to pass the right message. Full disclosure. Non-intrusive use of tweet
content.
What happens when authorities demand this technology for privacy-invading stuff.
Have to be careful with what you publish.
Always assume the bad guys have thought of what you thought of.
Always be in a situation where you can destroy your data at short notice.
Bit legal barrage behind them. Know what they are/aren't allowed, know what
they do/don't have to do.
Start leading a blameless life.
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