Tag Archives: terrorism

Op-interest: On Opinions + OpSec

On of my Webster Words-of-the-Day this week was “opine,” the act of having and stating an opinion. It’s something that I do often on my blog, but am encouraged to stem for a more objective perspective when it comes to professional data vis stuffs and news publications. Journalism solicits an ideally balanced representation of information, but as with any domain touched by human fallibility, it’s vulnerable to bias.



The Swedish have an interesting word for one-sided opinions: Åsiktstaliban, defining a group of people who tolerate only one opinion and can be colloquially synonomized with and global violence; and so this blog post is going to address opinion diversity and operational security, two poles of a global approach to citizen journalism and political activism. It seems an appropriate post in the days following September 11th and the tragic anniversary of the Westgate mall attacks, and definitely something that has peaked my [op]interest with as yet feeble articulation these past few months.

Like many developer-journos, I’ve been following the more tragic and graphic media reports out of Iraq, Gaza, and Syria lately. Jonathon, one of our developers at Ushahidi and Chris, his partner on the CrisisNET project, created a timeline of ISIS happenings a few weeks ago, followed by subsequent investigations of conflict in Iraq and Gaza, and this had me reading more about security and media verification for journalists in the Middle East, and otherwise hostile-to-media and humanity areas.

I touched on these topics briefly during my panel at HOPE-X with Harlo Holmes and Barton Gellman (livestream here), and again during our workshop on opsec last week and the Buenos Aires Hacks/Hackers Conference.


But independent of my own stuff, there’s a recent trend in crowdsourced citizen journalism that I want to encourage and support professionally and just personally. Part of supporting that initiative is providing open source tools to enable citizen reporters (like those in Ushahidi’s Toolbox), but part of it is also just sharing information openly about authoritative sources.


This is a good place to promote Bellingcat, and other work aimed at armoring activists, newsies, and the general public with information. While it probably won’t keep extremists from more barbarous and cowardly expressions of violence, being informed is non-trivial in the fight against global rights violention. A lack of information historically and consistently is the root of epic geopolitical blunders, tragic massacres, ignorance and ignoring of massive human rights transgressions, globally. To that end, and in a modest objection to the wave Åsiktstaliban media, I’ve assembled a small collection of links and sources to keep apprised of what is happening in places that are remote from my current locale. I’d love to solicit others so I’ve made a form at the bottom of this blog for collecting relevant media sources and tracking the safety of embedded journalists in the Middle East.

  • The New York Times had pretty decent coverage, McClatchy’s wires on the Middle East and the Guardian’s Liveblog have been pretty consistently informative
  • On twitter, I follow Blogs of War (@blogsofwar), and some specific journalists embedded in regions of interest (@BklynMiddleton, @IvanCNN, @Matthew__Barber, @Mudar_Zahran, @jrug, @abumuqawama, @joshuafoust,@combatjourno,@SajadJiyad,@RaquelEvita,@DrZuhdiJasser,@majidrafizadeh,@Reem_Abdellatif,@WalidShoebat)
  • I’ve started reading local bloggers and certainly Bellingcat
  • Vox had a pretty o.k. abbreviated breakdown of the current affairs vis-à-vis ISIS, HuffPo has a decent world roundup as well

But despite the intense media house coverage, I find myself often returning to individual blogs and the work of lone journalists; I think this trend is significant and I’m sure shared by many given the popular response to citizen journo-projects like Bellingcat. I find most embedded journalistss and local citizens to be the most informative for thorough and unapologetically blunt coverage.




As a personal/pseudo-professional aside, we’ve (@Ushahidi) also been working on an implementation of some data visualizations for election monitoring in Yemen, and this had me researching more of the political climate there (so samples below).


preview of Ushahidi V3 Viz

I’ve been wanting to build a visualization of global disappeared populations, of which there are many, in almost every country. Those that we hear about more often harken back to Colombia and Argentina circa the 1970s persistently through today, or more recently the 600+ Nigerian girls kidnapped by Boko Haram, the Yazidi women kidnapped by IS affiliates, or the Zone 9 Ethiopian journalists still detained in East Africa. When a country succumbs to brutal regime rule, it’s often the journalists, the vocal activists, and the outspoken citizenry spreading independent opinion and information about injustice that become the targets of violence and effacement tactics. Information becomes a target, and those who process and disseminate it are vulnerable to attack.


Screen Shot 2014-09-18 at 2.35.38 PM preview of Ushahidi V3 Viz – gender counts

Perhaps some of their anecdotes and needs are things we might accommodate in the newer version of Ushahidi, or in CrisisNET, our pretty rad aggregator of social and streaming data on the global crisis situation, unified in a single API. And while there are many visualizations and representations of the statistics around targeted terrorist groups, a direct comparison between the composition of the victim population and the terrorist perpetrators is something perhaps worth investigating. A recent open analysis on government documents about outstanding terrorist threats and the TIDE “watchlist” (see also TIME, The Atlantic) reveals some interesting statistics about the paucity of females associated with violence as terrorists, but the general density of females associated with violence as victims.

TIDE by the Numbers

watchlist-by-gender2 of the 9 detained Ethiopian journalists were women, 600+ of the Nigerian girls where; a substantial portion of the limited documentation on Syrian disappeared citizens catalog female adults and children, and coupled with the female rights violations in Yemen, the disappeared counts are also substantial; the same goes for Turkey and countless other nations who’ve only begun to catalog disappearances publically. The Support Yemen Project has done some work to publicize the circumstances surrounding human rights and free expression throughout Yemen as has the AHA Foundation, in addition to logging the impact of terrorism and counter-terrorism, and yet much of my more informed perspective on women’s issues and violence in Yemen stems from the posts of a Yemenese female blogger. Again, my focus returns to local journalists independent of media affiliation. And while females are not the sole-authors covering female rights, the dangers faced by female journalists in terror zones corroborated with some recent reports from the NYTimes and the Huffington Post, as well as some more general blogposts on women’s rights violations authored by the aformentioned lone-journos I follow on Twitter. The circumstances demand a more responsible way to monitor and vet on-the-ground activity and reports, and increasingly social media monitoring and crowdsourcing applications are providing these windows to supplement the occasional blog post and media supported piece.

Screen Shot 2014-08-11 at 11.58.22 AM

This made me consider the unknown, and absence of information as an important root to some of the more brutal disappearances, and particularly lead me to consider the position of citizen-journalists who seek to amplify information about a space and are subsequently pushed by kidnapping, eradication, or imprisonment and public execution. Information is sometimes the most dangerous currency to smuggle from a vacuum, it can mobilize nations to send aid or commence peace talks, it can prompt the vicious reactions of groups who would execute victims to deter action, it can push citizens to technological circumvention tools in an effort to counter the habitual throttling of their internet access. It’s one of the more noble vocational pursuits to propagate honesty in a sea of redirection and rumor, and it’s something that can be enabled and aided by technology. Given my recent research and just general current events, I’m incredibly humbled that I get the opportunity to work on technology for crowdsourcing and spreading information, and so I wanted to address how we’re tackling the vulnerability of information providers with our tech at Ushahidi and our trainings at Internews.


In terms of self and source protection, Harlo and I have compiled some applications that can help with operational security for journalists, and this applies to citizen journos as well.



In terms of source verification, CrisisNET has prepped a roadmap series of features to integrate the likes of TinEye, and twitter verification via TweetCred. To that end, the devs at CN wrote about this application of authenticity readings to the CN service vis-à-vis  the IDF/Gaza conflict  recently. We’re working to build more security into Ushahidi’s platform as well, and otherwise increase the availability of our technology through much needed translation efforts. Like most of the platforms we provide, we rely often on crowdsourcing and community participation to complete the arc of their utility, and we’re hoping our community will help make our products better.

Outside our own repertoire, there’s a beta product called Scraawl that also aims to provide streaming data about large scale graph and social media collections. There are further, plenty of ways to contribute to crowdsourced journalism projects: join Open Reporter, a platform for free and open news, or Open Street Map, a crowdsourced program for mapping the globe, or Project Fission, an open source project to manage reporters’ notes and stats. Opine and add-to where possible, open information and citizen journalism still source some of the most up-to-date coverage of crisis worldwide.


Meantime, I’ll close with a more positive piece, reblogged to oblivion, on Yemen, a link to some github to watch as we move more data viz into Ushahidi’s core, and request eagerly any blogs/sources to watch below:

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Fortune Telling with Data: Modeling Threat with Feeble Predictors

Anna Pavlova - foxy chiromancerWhile in college, and unbeknownst to most people, I dabbled in some performance artistry and predictive analysis. Part of the performative nature of college is developing the capacity to claim competence in topics and credentials one has yet to earn, to educate formally while informally fumbling through social mechanics for which no adequate prerequisite is ever published, and finally to make elaborate promises to potential employers you can’t yet keep. Corroborating this farce with some documentation is usually expected, a cover letter here, a résumé there.

On professional applications, it seemed impressive to have a range of acronym memberships to organizations with undefined but assumed-legitimate titles. Interviewers, however, seemed inattentive to merit or documentation fluff, so in small print among some legit scholarships and volunteer positions, I wedged in a nod to my extra-curricular involvement in the “ESP Volunteer Aid Org.”


I am not a performance artist or a seer and this was an experiment I dropped shortly after, but I think there’s some irony in that, while attempting to assemble my prospective professional qualifications, I spent some time considering a career in heightened sensory perception. Or rather, I tried to jest-test the merit system with an obviously bogus acronym.  Funny in retrospect, but previous experience in ESP isn’t far from the tacit prerequisite many would assign to data mungers these days, and particularly anyone who does even mild statistical analysis on crisis datasets. With all these data, surely someone would be able to claim clairvoyance, solve international crises with a affinity for computational analysis of historic precedent, surely the answers are there?


fortunetellerAs someone who works with data, and more importantly as someone currently living in the future (compared to those currently living in my home country…whoohoo Nairobi time), I thought it might be appropriate to exercise my clairvoyance and provide clarity on life in Nairobi, assessing some of the current intensities, and the probabilities they might aggravate. In any case, the explosion of state department travel warnings in my inbox this week has made my reticence on the subject a bit obnoxious so I’m going to diverge from my typical open source software soap-boxing to write a bit about the statistics of terrorism and the particulars of my current condition.

I write from the position of a math hobbyist,  and an amateur clairvoyant, and Electric Powerso I’ve peppered this post with some specious but thoughtful observations about the news I’ve been following, what I’m currently experiencing, and the links, images, and resources my limited bandwidth allows me to explore.

In brief, Nairobi has been intense of late. The state department issued 4 official warnings this week, encouraging US residents and visitors to avoid Eastleigh, travel to Mombasa, proximity to Burundi, and most recently, travel in Kenya, period. These precipitate from the bombing earlier this week in Nairobi (6 fatalities and 20+ injuries), the recent church attack in Mombasa (4 fatalities) and general insecurity about al-Shabaab and armed operatives threatening attacks in any country conducting peacekeeping and/or military efforts in Somalia. :(

For it be morrow.. Speculation about the probability of a “large scale attack to come” made me start thinking about the meaning of “large scale” and projected “imminence” when it comes to statistically predicting events of high variability. How large is a “large scale” event? Anything where multiple deaths result seems “large” to me, though my definition has adjusted to accommodate recent conditions. If authorities are projecting a large-scale event to come, what about the unsettling events of now? How soon is imminence, not to be too much of a Morrissey fan-girl, but how soon is now?

Which to choose?So with all of these questions and my own preoccupation with quantifying self, I thought it might be time to read up on a few predictive models of the likelihood that something might happen.

Periodically people people post comparatives online like “you’re __times likely to die of x than get involved in a terrorist attack.” These were net popularized post-9/11 it seems, though, the scale and impact of that attack in the domestic US was fairly singular and data collected prior to it would do little to predict its occurrence and re-occurrence without admission of several limiting factors and uncontrolled variables.  That said, in the Annals of Applied Science (vol. 7, no. 4 2013) last year, Clauset and Woodward wrote a paper called “Estimating the Historical and Future Probabilities of Large Terrorist Events” in which they hoped to define a generic statistical algorithm for estimating the likelihood of terror events in complex social systems. These kind of predictive stats depend on so many variables outside precedent empirical data but the authors present multiple tail models and disclaimers about the limitations of their predictions to control for this (Matlab code and sample data available here if you want to play).

Kreskin's ESP board game

Of particular interest is their summary forecast are estimates for 3 potential scenario probabilities based on possibly forecast from past data:

“Rather than make potentially overly specific predictions, we instead consider three rough scenarios (the future’s trajectory will presumably lay somewhere between): (i) an optimistic scenario, in which the average number of terrorist attacks worldwide per year returns to its 1998–2002 level, at about ⟨nyear⟩ = 400 annual events; (ii) a status quo scenario, where it remains at the 2007 level, at about 2000 annual events; and finally (iii) a pessimistic scenario, in which it increases to about 10,000 annual events.”

That then, looks something like this:

Crystalball looking cuteThe Clauset and Woodard analysis further predicts a range forecast of 19-46% chance that at least one catastrophic global event will take place in the next decade. But to localize this a bit, and for the sake of argument, let’s take their “status quo” (a modest median) probability calculation trained from the RAND-MIPT Terrorism Knowledge Base, and set up the conditional probability that there will be a terrorist happening of catastrophic proportion (p=0.461) while I am in Nairobi (approx. 30/(365*10yr) possible days; p=0.008) . The condition is fairly unlikely, but unfortunately increases when you factor in covariates like my general foreignness (> victim likelihood…bummer, p=0.475), and the  logic that the violence occurring with agglutinative regularity will likely foster additional conflict and escalated tension:
“For instance, international terrorist events, in which the attacker and target are from different countries, comprise 12% of the RAND-MIPT database and exhibit a much heavier-tailed distribution, with αˆ = 1.93 ± 0.04 and xˆmin = 1.”
EAC MapTrying to control for multiple variables is complicated, so even in a problem which can be structured as conditional (probability of x given n state) struggles in this scenario. Does the probability of one state affect the other and yet still require factoring in both? And if so, perhaps it’s a joint probability issue between independent events. When the prediction derives from historical information, perhaps a Bayesian use of prior probabilities could be trained for future forecasting but even then…complicated. And regardless, perhaps the historical data is limiting in applicability due to scope; my definition of “catastrophic” scales down to the mere injury of a family member/friend, decidedly distant from the catastrophic proportion of 9/11 or any event with Chiromancerupwards of 1000 fatalities used to make these kind of probabilistic predictions.
Most of the math here is beyond my own research level but one factor that strikes me as strange, given my work with crises in the context of maps, is the absence of particularly specific geo-data analysis. The East African Community (EAC) hasn’t been spared much violence in the past few years, and sadly, in the last few months in Kenya. so I’m interested in reading about statistical modeling done on conflict probabilities with geo-specificity. Maybe this a usecase for the Wolfram Language when I’m brought out of my beta-in-waiting status; something to counter the pop-y around the world travel time estimates and polar auto-opposite calculations that have been so fun but maybe not particularly applicable to my current situation. What might be applicable is a computational knowledge engine that would assess my IP address, map it to a lat/long and then calculate how far I should move in the city to avoid conflict on a daily basis (*winks* at Wolfram friends).
To be fair, the Clauset and Woodward research  honestly nods to the variables not-completely considered in their analysis:
“Technology, population, culture and geopolitics are believed to exhibit nonstationary dynamics and these likely play some role in event severities…our approach is nonspatial and says little about where the event might occurrefinements will likely require strong assumptions about many context-specific factors (Clauset + Woodward, 15).”
Evangeline Adams (American Astrologer) explores a mapBut, I’m still wondering about alternatives to these estimations, what is the best research body to design these kinds of models and who has the best open test data on the topic? I’m generally skeptical of predictions based on historic data without geo-reference these days, since so much of what happens depends on a cultural/historical/social context that is impossible to divorce from a particular place;  the general forecast of 19-46% chance of something happening in the next decade at a global scale is hard to conceptualize when you consider the umpteen geopolitical factors that might cluster likelihood around certain high-tension locales (Clauset + Woodward 14). Perhaps there will one day be a service to prioritize these factors and co-variates based on personalized social and surveilled data as Seth’s Worry App concept suggests:
“Worry is the very first technological solution that maximizes the benefit of mankind’s oldest task: anxiety.
Using this flow of data, the Worry app computes the things you ought to be worried about. For example, instead of needlessly wasting time worrying about a random event like being bitten by a brown recluse spider, the Worry GPS system can point out that based on where you are, you’d be better off worrying about a different, unpreventable event like being killed by a fire hydrant flying through the air or perhaps by an angry rooster wielding a knife. The Worry app will alert you to that, which dramatically increases the effectiveness of your worrying.”
Destiny awaitsI’m into anxiety optimization and maximized thought efficiencies, perhaps a maturation of my adolescent ESP :). 
In all seriousness, there is probably little statistical value in projecting these possibilities where I am currently. Fortune telling with so many variables can be complex, though the projections remain pretty unsettling.
But apart from all the speculative quant, there are some simple qualitative observations that I can make:
  • things are heating up every day here
  • any situation where “safety in numbers” is a paradox because avoiding congregation points (malls, churches, etcet) has become a way of avoiding conflictESPad is probs bad
  • at the end of the day, statistical randomness is a really unfortunate jerk who even despite your best precautions can allow for some pretty horrific happenings (a carjacking happened in my inner circle this week, for example)
  • there’s something broken about the fact that the entire reported anti-terror budget of Nairobi is less than my current apartment’s rent outside the city (both cases supposedly sustaining a month’s worth of expenses). If we drill down on that statement semantically, and not quite statistically, we can conclude that the collective safety of a city in a time of “imminent” crisis is roughly worth a one bedroom apartment.

Mathematical calculated? Feebly. Statistically significant? Probably. Totally unfortunate? Predictably.

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