Category: Advanced Cyber/IO

Communicating with Disaster Affected Communities

Would be great to see how this type of search data compares to data from Tweets. Take this analysis of tweets following the earthquake in Chile, for example.

RT @UNFPA: Professional systems are being replaced by consumer tools says @Google Crisis Response #commisaid

And as a result, crisis-affected communities are increasingly becoming digital as I note in this blog post.<

Closed systems closed data will be left behind and unused:crisis response is social and collaboration is empowering @CDACNetwork #commisaid

RT @catherinedem: Crisis response is #social – online social collaboration spikes during and after disaster @spangledrongo #commisaid

we don't empower communities by giving them info,they empower themselves by giving us info that we can act on-@komunikasikan #commisaid

RT @ushahidi: “In a crisis, the mobile internet stays most resilient, even more than SMS.” #commisaid Nigel Snoad

RT @jqg: Empower local communities to generate their own tools and figure out their own solutions #commisaid

Through #Mission4636 SMS system, radio presenter @carelpedre was able to communicate directly with affected people in Haiti #commisaid

Tools shouldn't own data. RT @whiteafrican: “It's not about the platform being open, it's about the data being open”- @jcrowley #commisaid

Cutting edge is to get the #crowd & the #algorithm to filter each other in filtering massive overload of information in a crisis #commisaid

So best of luck to those who wish to regulate this space! As my colleague Tim McNamara has noted “Crisis mapping is not simply a technological shift, it is also a process of rapid decentralisation of power. With extremely low barriers to entry, many new entrants are appearing in the fields of emergency and disaster response. They are ignoring the traditional hierarchies, because the new entrants perceive that there is something that they can do which benefits others.”

Phi Beta Iota:  The reluctance of honest senior individuals in the US Government to actually learn is quite troubling.  When combined with the abject corruption of many other senior individuals, there is no question but that the greatest threat to the United States of America is internal — what Lawrence Lessig calls “systemic corruption” and Robert Steele calls “lack of integrity.

 

Twitter, Crises and Early Detection: Why “Small Data” Still Matters

My colleagues John Brownstein and Rumi Chunara at Harvard University's HealthMap project are continuing to break new ground in the field of Digital Disease Detection. Using data obtained from tweets and online news, the team was able to identify a cholera outbreak in Haiti weeks before health officials acknowledged the problem publicly. Meanwhile, my colleagues from UN Global Pulse partnered with Crimson Hexagon to forecast food prices in Indonesia by carrying out sentiment analysis of tweets.

I had actually written this blog post on Crimson Hexagon four years ago to explore how the platform could be used for early warning purposes, so I'm thrilled to see this potential realized.

There is a lot that intrigues me about the work that HealthMap and Global Pulse are doing. But one point that really struck me vis-a-vis the former is just how little data was necessary to identify the outbreak. To be sure, not many Haitians are on Twitter and my impression is that most humanitarians have not really taken to Twitter either (I'm not sure about the Haitian Diaspora). This would suggest that accurate, early detection is possible even without Big Data; even with “Small Data” that is neither representative or indeed verified. (Interestingly, Rumi notes that the Haiti dataset is actually larger than datasets typically used for this kind of study).

In related news, a recent peer-reviewed study by the European Commission found that the spatial distribution of crowdsourced text messages (SMS) following the earthquake in Haiti were strongly correlated with building damage. Again, the dataset of text messages was relatively small. And again, this data was neither collected using random sampling (i.e., it was crowdsourced) nor was it verified for accuracy. Yet the analysis of this small dataset still yielded some particularly interesting findings that have important implications for rapid damage detection in post-emergency contexts.

While I'm no expert in econometrics, what these studies suggests to me is that detecting change-over–time is ultimately more critical than having a large-N dataset, let alone one that is obtained via random sampling or even vetted for quality control purposes. That doesn't mean that the latter factors are not important, it simply means that the outcome of the analysis is relatively less sensitive to these specific variables. Changes in the baseline volume/location of tweets on a given topic appears to be strongly correlated with offline dynamics.

What are the implications for crowdsourced crisis maps and disaster response? Could similar statistical analyses be carried out on Crowdmap data, for example? How small can a dataset be and still yield actionable findings like those mentioned in this blog post?

Vivek Ranadive may be a very clever man using the top 200 rich to finance a system that could rapidly be “flipped” to empower the bottom up.  Or he may be a very evil person who is planning to empower the top 200 in the same way that he empowered Goldman Sachs. If the top 200 are selecting those to be in the loop, this will accelerate their destruction of the Earth.  If instead this can be used to subtly educate the top 200 on “true costs” and show them how to achieve profit while doing good, it could be revolutionary.  If it can be rapidly scaled to be bottom-up in nature, it could be revolutionary.  Only time will tell.

The Private Social Network For The Extremely Rich and Powerful

An Esquire piece on $4 billion software company Tibco discusses its latest, perhaps most significant project:

TopCom is a private communications platform for the 200 most powerful people in the world. It is being officially launched in late January at the annual meeting of the World Economic Forum in Davos, Switzerland. It is basically a customized, ridiculously secure version of tibbr, a platform developed as a kind of combination Facebook, Twitter, e-mail, texting, and Skype. It is a private social network, essentially — in this case, for world leaders.

Because the World Economic Forum has a hierarchy, so does TopCom: The top two hundred WEF members — basically, the people who run the world — can speak to one another on a given subject, and then they can choose to loop in members from lower tiers (experts, academics, etc.) as needed, widening the pool of knowledge on whatever problem is on the table.

Esquire Article

See Also:

Virgin Truth One Page Final 1.1

World_Brain_Institute_1.6 pdf

Hacking Intelligence to Hack the Earth