Voting and attendance in Slovenian Parliament from 2004 to current term

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In Slovenia, we have a love/hate relationship with our politicians. We hate them, because at almost every single step they make, they let us know they are corrupt and they can easily get away with it. But in each new election new faces appear, promptly get elected and are hailed as saviors, who will finally clean the Augean stables of greed and corruption that has been accumulating for too long.

Most emotions are reserved for those in the front row, mainly government members. Members of parliament are somehow exempted, as they are not so widely known. Somehow, they are not monitored properly, at least in my book. There is a site that contains session records per member and per session, but it’s not widely known. It was an inspiration for this attempt to present members’ activity in an easily understandable and graphic way for current term and a few terms in the past.

See the interactive version:        Slo                             Eng

Interest groups

The main idea was to group the parliamentary members by similarity of their voting record. Most parliamentary members are bound by strict voting discipline, imposed by the parties they belong to. This way the parties can guarantee that some or another act will pass and become a law. But is this really so? I tried to use a simple machine learning technique to answer that question. First I collected all the voting results from parliamentary term and sorted them in chronological order, then applied the technique (k-means clustering, for technologically minded). Number of groups was set to ten, but I could increase it to see smaller groups – maybe fractions inside parties, or cross-party interest groups.

Below you can see an example of two groups from recent term.

Here is the first:

And here another:group1

It’s apparent that groups do not contain representatives from one party only, and the visual representation imparts a feel for the differences in voting. As I mentioned above, I arbitrarily constructed ten groups, but a serious researcher would play and tinker with the number, as every clustering technique is an exploratory process and must be iterated upon for best results. It’s interesting that the results also show other parliamentary tactics. This one below could be interpreted as obstruction, or simply passivity or indifference. So what is it? To ask this question is to answer it, I guess.

To put it in context, this is a group of left-wing opposition representatives during a period when they were in heavy minority.

Indifference or obstruction?
Indifference or obstruction?

In contrast, this is the right-wing voting machine that prevailed:

A disciplined voting machine
A disciplined voting machine

The contrast between these two groups is so dramatic that it would be funny, if these were funny affairs.  While the opposition was idling away, the majority voted into existence law after law that, together, still influence the lives of the Slovenian citizenry. In interactive version (English) you can explore what the votes were about by simply moving the mouse over horizontal stripes.

See the interactive version:        Slo                             Eng

Attendance record

Session attendance is another telling indicator of particular representative’s zeal in upholding democracy and fulfilling the interests of his constituency. It’s already apparent from  charts above, but I still constructed a separate graphics for that. It’s sorted by presence and more easily readable.

It has to be noted that some representatives were excused from voting sessions for various periods of time. Among them are those who became ministers and those who replaced them in the parliamentary seat, not being there before.

Here’s an example from the recent term. At the bottom, you can see two blocks with alternating presence. That’s because there were two governments. When the first one fell, the ministers returned to their seats; those who originally replaced them, returned to the party’s roster; new ministers were sworn in and abandoned their seats; and new replacements came from opposite camp.


See the interactive version:        Slo                             Eng

 Yes-men and rebels

Another interesting statistics is: representatives with most votes for yea or nay. I don’t really know how to interpret this, but I did it nevertheless. One could say that in terms with only one governments, members of ruling majority with most yea votes are those who unquestioningly toe the party line.  Conversely, those with most nay votes are most fervent members of the opposition. In terms with two governments, this is a little less clear-cut: one would have to separate the timelines and run the statistics on subperiods for each government. I didn’t do this, but a serious researcher would. I made this report to let them know that they are being monitored, but it’s a task of an investigative journalist to delve into the data and interpret it in a meaningful way. I don’t have time for this, and I don’t really know the particulars of daily politics here enough to be able to do that.

But I’m offering the database to anyone who would like to do that. Send me a mail for details, I’ll gladly oblige.

Here are a few simple pie charts that illustrate what I just wrote:

Yes men and rebels
Yes men and rebels

See the interactive version:        Slo                             Eng

Unity index

While programming, it struck me that I could calculate a synthetic measure that would show the unity in the parliament. The reasoning goes: if the vote was unanimous, the parliament as a whole was united in cause at hand. But if half of representatives voted yea, and the other half nay, the parliament was divided. So I constructed a timeline of all voting sessions and colored every session according to this measure. Blue for unanimous vote, red for evenly split vote, and violet hues as nuances of disharmony.

Additionally, the bar heights indicate the presence ratio. Lower heights obviously mean lower presence.

In some terms, the presence falls toward the end, and the proportion of red bars increase. This means that the representatives lost heart and abandoned their posts, and those who stayed, quarreled bitterly.

Here are these graphics for various terms. They are stretched to same length. Perhaps a more correct, but less visually appealing approach would be not to stretch them, so the length of particular term would be apparent.

IV (2004 – 2008) – PM Janez Janša
V (2008 – 2011) – PM Borut Pahor – ended prematurely
VI (2011 – 2014) PM Janez Janša, PM Alenka Bratušek – ended prematurely
VII (present) – probable PM Miro Cerar

See the interactive version:        Slo                             Eng

Session timelines and voting networks

The drive behind this section was to find out whether the attendance is falling, as the session progresses into small hours. I found that not to be so, which is encouraging in a way. These charts at least show which sessions were bitterly contested, and which were almost unanimous. You can see examples of both behaviors in the graphic below.


Going one step further, I constructed a separate network for each session in a way that if a representative voted for a proposition, he or she is connected with it, otherwise no.

Networks are a little bit messy, and people tend to not understand them well. This network below shows three groups of representatives (you can zoom in and out in the interactive version). They are grouped close to the propositions they voted for. So this is another opportunity to find out the interest groups on the micro level, for each proposition. Some propositions don’t have a name, just a date. That’s not my fault, but the parliament’s, as they didn’t bother to publish it on the

See the interactive version:        Slo                             Eng

Seating order

Finally, here are some heatmaps for various variables, mapped on to seating orders. The first is partitioned according to representatives’ party. Sorry, no legend here. You can mouse over in the interactive version to show details.

The second is attendance heatmap. Green is full attendance, red is total absence, and there’s a linear color scale between them. This one provides at-a-glance overview of attendance of entire party blocks.

Next two are yea and nay heatmaps, so you can see which party blocks mostly voted yea, and which nay. They are normalized to their local maxima for visual appeal, but a more correct approach would be to not normalize them, so it would be apparent that a nay vote is much less frequent than a yea. Why, I have no Idea, but I imagine there must be a lot of technical votings, for example establishing presence and so on.


These seating orders are approximate, as I couldn’t get them for past terms from the parliament. They asserted that they didn’t have them, and claimed they don’t even have the current one, even if it’s published on their own website. There were more lies, but I won’t go into that here. They are, after all, in power, and I’m just a blogger.

Why they should engage in such behaviour is beyond me. Maybe they think that the information is theirs and should be kept from the public.

Again, if anyone needs the MongoDB database, drop me a note. My email address is on the About page.

See the interactive version:        Slo                             Eng

Discovering and visualizing songs with similar trends on the British Top 40 Charts from 1990 to 2014

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I often wondered what is an average lifetime of a pop song on the charts. If one follows music, it becomes intuitively apparent that there are in fact several types of hits. Some stay on the charts for many weeks, and others barely make it, then immediately slip out.

So I set about discovering groups of songs with similar trends, as they moved on weekly British Top 40 Chart from 1990 to 2014. A total of 1284 different songs appeared on the charts in that period. After a series of experiments, 100 groups were arbitrarily decided on. Position data for each song was collected across the weeks, then the songs were grouped using k-means clustering.

The result is part interactive, part static visualization, consisting of an exploratory chart and 100 small charts showing each separate group.

Check it out here! Or click the image below.Song trends over time in a typical group

Song trends over time in a typical group


To group the songs, the data was first scraped from, then arranged in format suitable for k-means clustering. The visualization was constructed with d3.

And here are some of the small multiples.

Some of the 100 different groups. Click image for more.
Some of the 100 different groups. Click image for more.

Ours and theirs – an endless duel between computer generated Slovenian news commenters

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I’ve been following comments under Slovenian web news items for quite some time. The commenters there are well known for their animosity towards anyone who disagrees with their political worldview. Reading the comment section usually means immersing yourself in verbal filth, depravity and all imaginable kinds of hate speech.

Some time ago I wrote software for scraping comments off these websites and have been since then storing them in a database. They are useful for a number of things. For example,  I’ve had some success with stylometry (identifying commenters by their writing style, even when they post under different name), but this is a matter of another post. I also helped SAZU compiling a list of new slang words for the new Dictionary of Slovene Language.

So here’s a lighter project for people who don’t read these comments, neither they want to. If you want to see it all, just click the image below and behold the auto-generated stream for a minute or two.

Note that these are not real comments. The text is generated from two Markov chains, which have been initialized with texts of left-wing and right-wing commenters.  The comments used are approximately a year old, lest someone accuses me of participating in election campaign of some kind. The web page simply generates a few sentences from one, then from the other, and so it continues ad nauseam infinitum.

I think it’s a fitting commentary of Slovenian mentality. Slovenian-speaking visitors will notice that, even if the texts are probabilistically computer-generated, there’s still ample hurling of insults based on the outcome of the last World War. There’s quite a lot of that.

Also, even though both sides pack serious vitriol, the right wingers use more classic hate speech, and they write comparatively worse.

See for yourself!

Naši in vaši
Naši in vaši

Technically, it was a breeze to make. First I pulled entire corpora of selected commenters from the database in text form, then I used RiTA, a generative text tool, for initializing the two models and generating sentences. The code is very short, most of it has to do with displaying and scrolling.

But initializing models from loaded text:


And then generating sentences with just:

var sentences = model.generateSentences(nr);

Such is the beauty of RiTA.

My experience with publishing data visualizations on the web

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After a year of publishing data visualizations and learning many things in the process, I think I can share a thing or two about best publishing practices that I’m so far aware of.

Here’s the Google Analytics Audience Overview for in little more than a year of operation. Big spikes all come from social media. The biggest spike happened when some bigger players linked to one of my posts.  Lulls in activity are because of my holidays.

There are two more charts from my site further down. One shows number of visits from social media, the other top referring sites.

Google Analytics - Audience Overview for
Google Analytics – Audience Overview for

In other words, you made a data visualization, and now you’d like to share it with the right people so they can appreciate the findings or the execution.  Where to publish it?

Thinking about it, there are several groups one should target. Some effort is required to distribute your work appropriately.

Your target groups

Data visualization enthusiasts 

People who enjoy a well executed visualization, and may care about the actual content just in terms of whether it’s well and fairly presented or not. Be aware that data visualization is a geekish thing.

There are several such communities:


Dataisbeautiful on Reddit – a subreddit dedicated to sharing data visualizations. Infographics are not accepted there. You may want to publish the link at a time that maximizes visibility for North American visitors. Subscription is required before posting.
A word of caution – you shouldn’t use Reddit exclusively for self-promotion. Posting just links to your work will be considered as spamming sooner or later, especially if you cross-post same link to other subreddits, so tread lightly, and do engage in discussion whenever possible. The FAQ says that it’s barely acceptable to post one link to your site per five links to others. Conspicious violation of these rules will get you banned, or worse yet, shadowbanned, which means you won’t even know your posts don’t appear on Reddit. – a very nice website allowing users to submit various kinds of visualizations. They also run competitions from time to time with awards up to $5,000. If your visualization gets featured, that can substantially add to your traffic, as they also have an active Twitter account and regularly publish what they think is best. If you make it to that list, your reach may expand significantly.
They also partner with academic organizations and big companies, so this is definitely the site to publish on. – another website like, but they also have a business. They will help you execute a project for a fee, but their galleries are well visited. After submitting a visualization, an editor reviews it and either approves it or not. I’ve had several rejected, mainly because they were wrongly categorized, or were in a gray zone. For example, I submitted a  blog post with an interactive embedded map, but it was rejected. Then I made a separate page with the map, resubmitted it and got approved. Sometimes, they will mark a well-executed visualization as a Staff Pick, which can lead to more attention from the users.
Another thing with this site is that they may have really high-profile visitors, as in journalists who write for mainstream media. In fact, one of my most visited posts – My heart rate during latest episode of  Game of Thrones – got spotted there by a Popular Science journalist, who published it in an article, which attracted more journalists from other media, and they published separate articles on their own, all linking to the original.
Another well-received post was a map of building ages in my city (a Staff Pick) , which attracted attention of Wired’s science editor, who was putting together a Wired Map blog post about such maps. After a few mails back and forth, they featured my map on Wired site (see Media page for a list of all such articles).



Visualoop – I’m not sure how to reliably submit your work to this site, but they do have a contact form at the bottom. They included me twice or three times in their weekly reviews. They probably spotted what I published on other sites. I recommend following their monthly dataviz calendar of events, there are a lot of conferences and hackatons there.


Various blogs and Twitter accounts

Some specialize in infographics, not making a distinction between that and a data visualization, others post just map-related stuff, and some are just run by geeks who enjoy something novel or cool. Search for them on Google.
As for Twitter accounts, search for tags: #dataviz, #ddj, #data-journalism and such, then follow frequent posters and institutions. Also try to find accounts of data journalists and professionals and technologies you used in your work, for example Sigma.js. Follow, retweet, etc … if even one of them retweets your link, you can see a hundred times more exposure as usual. Also, try to patiently build and cultivate your online community. This is an area in which I lack, as I’m more work oriented. Read articles such as these.

Kantar Dataisbeautiful awards
Kantar Dataisbeautiful awards

Data visualization competitions

It’s good to apply for as many of these as you can, even if you have to pay a symbolic fee. If nothing else, you may get longlisted, and your link will be displayed on a prestigious page, thus exposing your work to more interested people.

Try these:
Knight – Mozilla Fellowships – this is not strictly an award, but they offer fellowships at prestigious news organizations around the world. They only select up to six people a year for a ten-month term, but I think that’s a great opportunity.
Information is Beautiful Awards – There’s a low fee to enter. Yearly.
Global Editors Network Data Journalism Awards – link is for 2014, admission is free. Open Challenges – interesting challenges with handsome awards.
Urban Data Challenge – they supply urban data, your job is to visualize it. Yearly.

More events at Visualoop/Events.
Be on lookout for various visualization hackathons.

I also recommend subscribing to DashingD3 newsletter, go to, there’s a sign up an the bottom. They also have another D3 newsletter for D3 freelance opportunities.

Journalists interested in data visualization

Data-driven journalism is an emerging trend. Most big publishing houses create prestigious visualizations that garner a lot of online interest. Guardian, Bloomberg, Washington Post and New York Times come to mind first, but there are many more, so there are naturally many journalists who are looking for a scoop in this area. You can find these people on Twitter, but there’s also a newsletter which some of them read. Subscribe to, and send your posts to

Business-oriented people

Join the LinkedIn’s group Data visualization. Actually, I got the idea for this post from a past discussion there. It has a ton of mostly business oriented posts and resources. You can also post your creations there. There’s also a lot of people there who might need a service you provide. More on this in the section about monetizing your work below.

Academia and  government

In my experience, this is an organic thing. If your visualizations have an educational value and you regularly post them, academics will notice and contact you. I was once contacted by a Canada’s health department’s official who used my findings from this post in his presentation at an international conference.

People interested in content and findings of your data visualization

This is the trickiest part, and possibly the most rewarding. There’s a lot of trial and error and improvisation involved here, but try posting on online forums and other communities, mailing to editors at news organizations, Facebook groups and such. I once posted a link to  My heart rate during latest episode of  Game of Thrones to a Game of Thrones forum and got overwhelming response.
Be careful though, as there’s a thin line that separates rightful enthusiasm from obnoxious spamming. In an ideal world, you would be an active member of these communities before you posted your link there.

Social networking

Social media visits to virostatiq-com
Social media visits to virostatiq-com

I mentioned social networking above, but I feel this topic requires a separate treatment. Examine chart above to get an idea of relative importance of these media.

Make sure you add sharing links to all your visualizations to make it easy for visitors to share them. They probably won’t use the buttons, but some of them will be reminded of possibility to share, and will do it their own way.

  • Facebook – try to join various data visualization groups and post there. Here are some: Gephi, Urban Data Visualization, Infographics and Data Visualization. Be aware that some of these groups are private. Also, post on your wall (obviously) and walls of organizations or pages that publish content that relates to your work, but carefully.
    Another strategy is to ask friends, who might be opinion makers, to post links to your works. I have such a friend, and when he does it, it makes a huge difference. Like factor ten difference, and they will reach other opinion makers, who will repost.
  • Google+ – consider creating a page with your efforts, and link it with your blog or site as per instructions. That will bolster your search results on Google and give you another avenue for showcasing your work and promotion. For example, here’s my page.
  • Twitter – I mentioned Twitter strategy above, so again, read articles such as these.
  • LinkedIn – join groups and post your work. This is a good place to develop business leads. Complete your profile and publish link to it on your blog.
  • Pinterest – create account, pin static images of your work to a panel with appropriate name, for example “data visualizations”.
  • Tumblr – consider creating a separate blog there and repost everything.
  • StumbleUpon – submit every link you produce. I had moderate success with Interactive timeline of the PRISM scandal.
  • Digg – submit all your links. data visualizations frequently appear on Digg front page, although I didn’t make it yet, so I can’t give a firsthand account on what kind of traffic you can expect.
  • GitHub – if you’re an accomplished programmer, clean your code and commit it there. I’m not, so I don’t. But it surely helps, especially if you manage to put together a library that others will use.


This is an area in which I don’t excel, but it’s a game which can potentially make a huge difference. Be sure to optimize your code and insert with meta tags. If your work is ajaxed, read Google guidelines for indexing such sites.

For more information turn to bloggers who make money out of their sites, there are tons of super useful resources out there. I’m a one man band, so it’s hard for me to keep current on all this in addition to technology and content.

Book to read: The Art of SEO by Eric Enge.

Monetizing your work

If visualizing data is more of a hobby than your primary work area, this article about reinventing yourself might boost your courage. In any case, don’t expect an avalanche of business opportunities and money from your hobby. Some might materialize though. So far I had the pleasure to do three projects for a small fee, and there’s another one in the works. A relatively well-known social network from Seattle contacted me to make a map. They saw my gallery over at and proposed some business, and I accepted. Needless to say, anything made for real production must be super tight, so there was a learning opportunity.

Some friends suggested that I display ads on my site. I won’t – firstly because I don’t believe that data vis enthusiasts would click on any, and secondly because I don’t have enough traffic to warrant inclusion. It would just be silly. The most I did was to enroll in Amazon Associates program and placed some links in posts to see what kind of revenue we’d be talking about. It’s of no consequence, but I might continue to do that, if only for information value in the books advertised.

Half a year after starting this blog, I won an award on Memefest Friendly Competition about Food Democracy. I went to Australia on their budget. That’s pretty cool. Now I consider my blog and my hobby as a potential vehicle to enrich my life in such unexpected ways.

Book to read: How to Make Money with Your Blog: The Ultimate Reference Guide for Building, Optimizing, and Monetizing Your Blog.

Other considerations

  • Optimize for mobile! There are times when half of visitors on my site have mobile devices. So make your visualizations responsive, and be careful with user interface so that you catch touch events.
  • Do not cut corners. A week more programming can make a difference between a featured visualization or a mediocre one, that’s going to get buried under other submissions in a day.
  •  Content is king. Ever heard this phrase? I did, but I had trouble understanding it. It means that a mediocre, but tight work on a superhot topic can be a hundred times more interesting than a perfectly executed job on uninteresting data.

 Top referring sites to

Here’s a last chart to sum up. It’s self-explanatory and gives a little more perspective to topic at hand.

Referrals to in one year
Referrals to in one year
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Conspiracy theories as network graphs: antigravity, Illuminati/NWO, JFK, 9/11, chemtrails visualized

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This is an attempt at visualizing different conspiracy theories. The visualization tries to show interconnectedness of actors, organizations and concepts in each one, so a network graph was chosen as a mode of presentation. The presented theories are: The Antigravity Drive, Chemtrails, The Cabal (American deep state from JFK assassination to 9/11), The Illuminati/New World Order, and the most recent, the Malaysian Airlines Flight MH370 disappearance. In a way, it’s a progression from the previous network visualization about the PRISM scandal, which was once also considered a conspiracy theory.

I chose this topic because those theories always attracted me as a means of alternative explanation of things that I couldn’t understand in official versions of events. That is not to say that I necessarily believe in any of them. For example, I’d be hard pressed to believe in the Moon Landing Hoax theory, which I first included here because of relative ease of gathering source material, but later discarded because of its relatively low value. The Flt 370 theory has extremely low credibility too, and I wonder what I’ll think when this post is a year old.

Some of the others, for example Nick Cook’s antigravity drive thesis, are extremely well researched, and many eminent scientists appear to believe at least part of it, if we are to believe his book The Hunt for Zero Point: Inside the Classified World of Antigravity Technology.

Launch the Conspiracy Theory Explorer! Use Chrome, if possible. FF and II are terribly slow.

Conspiracy Theory Explorer
Conspiracy Theory Explorer – launch viewer!


Conspiracies and conspiracy theories

A conspiracy, according to Wikipedia… may also refer to a group of people who make an agreement to form a partnership in which each member becomes the agent or partner of every other member and engage in planning or agreeing to commit some act.“. This is a pretty broad definition. It can apply to a government, a company, or every group of people who are trying to further an agenda, be it good or bad for their natural or social environment. But anything labelled as a conspiracy almost always has an evil association, for example “A civil conspiracy or collusion is an agreement between two or more parties to deprive a third party of legal rights or deceive a third party to obtain an illegal objective.” (Wikipedia – civil conspiracy), or “In criminal law, a conspiracy is an agreement between two or more persons to commit a crime at some time in the future.” (Wikipedia – criminal conspiracy).
A conspiracy theory is therefore an attempt at explaining a real or imagined conspiracy. In this sense, even official stories of various incidents are conspiracy theories, unless they are well founded in evidence and irrefutable facts. In a free society, a kind of market then forms of conspiracy theories, in which those with better means, but also more vested interests, compete for public’s attention  with other bodies of citizenry, whose interests and aims can differ significantly. For example, a government can execute a false flag attack, as the Nazis did in Poland at the beginning of WW2, and spin a theory that the other party did it, in order to go to war and grab land. The public may then be motivated to concoct a variety of counter theories with various motives – simply seeking the truth, overthrowing the government by exposing the lies it tells, furthering some commercial agenda, for example selling books, or purely personal paranoid agendas, which serve no one else than the authors and their need to sustain their delusions.
Let me briefly explain the theories I used in this visualization. First two are quite believable.

The Cabal: the story of American deep state and events from JFK assassination to 9/11 attacks

JFK Assassination in Dallas
JFK Assassination in Dallas

How the secret cabal of highly influential men formed behind US Government for the purpose of killing President Kennedy, and how it later evolved into a secret government that controls most of the aspects of American politics and life. In it: JFK and RFK assassinations, the presidential careers of the Bushes, Clinton, and Obama, the Oklahoma City bombing, the 9/11 plot, and the murder of countless witnesses, politicians, and journalists who sought to expose them, including Sen. Paul Wellstone and even Hunter S. Thompson. Everything, according to the authors, has been an inside job.
The research has been done by Mark Gorton, and material from visualization comes from his two essays (Fifty Years of Deep State and  The Political Dominance of The Cabal) available on the Internet, but also from these books he references: How the CIA Controlled The House Select Committee on Assassinations” Chapter 17 of “The Taking ofAmerica 1-2-3” by Richard Sprague, The Road to 9/11: Wealth, Empire, and the Future of America by Peter Dale Scott, Defrauding America: Encyclopedia of Secret Operations by the CIA, DEA, and Other Covert Agencies by Rodney Stitch, George Bush: The Unauthorized Biography by Webster Tarpley and Anton Chaitkin, Dark Alliance: The CIA, the Contras, and the Crack Cocaine Explosion by Gary Webb, and Compromised: Clinton, Bush and the CIA, by Terry Read.

The Antigravity Drive

The Henge - a test site for Die Glocke?
The Henge – a test site for Die Glocke?

A story of how the Nazi regime allegedly developed a form of anti gravity propulsion in total secrecy, made possible by a strictly compartmentalized environment, imposed on the German war production efforts by the SS. The technology was then seized by the US military and other allies after the war and developed further in utmost secrecy. The first such machines ever seen were so-called foo fighters. These balls of light, sighted and documented by various US Air Force pilots, flew in parallel with bombers and fighter planes, and frequently executed seemingly impossible air maneuvres. Also mentioned is a mythical machine The Glocke (The Bell), which ran on red mercury and was responsible for death of several scientists due to extreme radiation it produced, and the discoveries of Viktor Schauberger. His implosion engine, which drew heavily on vortex physics, was allegedly successful, and produces two flying prototypes. The US military immediately grabbed and classified much of this work, and it stays secret until now. It’s said to be employed in B-2 bomber and various flying craft sighted around Area 51 in Nevada. The story also goes to mention modern experiments in anti gravity physics, notably performed by Evgeniy Podkletnov, which allegedly succeeded in reducing gravity over a spinning superconducting electromagnet for two percent.

The material for this story comes entirely from Nick Cook’s book The Hunt for Zero Point: Inside the Classified World of Antigravity Technology, although there are many other books on this topic, for example Tom Agoston’s Blunder!: How the U.S. gave away Nazi supersecrets to Russia, Dr. Paul LaViolette’s Secrets of Antigravity Propulsion: Tesla, UFOs, and Classified Aerospace Technology, Joseph Farrell’s The SS Brotherhood of the Bell: The Nazis’ Incredible Secret Technology and Reich of the Black Sun: Nazi Secret Weapons & the Cold War Allied Legend, and some others. All of them are well-researched and worth reading.



A popular conspiracy theory about fat trails that civilian airliners leave in their wake. These chemical trails – as opposed to regular vapor contrails – are said to contain microbiological material and heavy metals, which seem to serve a variety of purposes. Among them: population reduction through novel diseases, such as Morgellons disease, which causes plastic fibers to grow through the skin, weather engineering for purpose of military dominance by the U.S., geoengineering to further reduce population, facilitation of communication with deeply submerged military submarines, and straight mind control in conjunction with HAARP.
Material came from assorted Internet sources, most notably, and the book Chemtrails Confirmed by William Thomas. There are other books, for example Chemtrails, HAARP, and the “Full Spectrum Dominance” of Planet Earth by Elana Freeland, and What In The World Are They Spraying? by G. Edward Griffin. Morgellons disease is expounded on in the book How to Get Your Life Back From Morgellons and Other Skin Parasites Limited Edit by Mr Richard L. Kuhns.

Illuminati / New World Order

Illuminati / NWO
Illuminati / NWO

How a handful of secret societies dominate the world. The plot allegedly has its roots in The Bavarian Illuminati society, started in the eighteen century by Adam Weisshaupt. They were eradicated, but some claim they survived in a covert form, forging an alliance with international bankers. Most big world events since then were planned in advance, among them both the advent of Communism, Nazism and Zionism, World Wars, and the third too. Says Pike: “The Third World War must be fomented by taking advantage of the differences caused by the “agentur” of the “Illuminati” between the political Zionists and the leaders of Islamic World. The war must be conducted in such a way that Islam (the Moslem Arabic World) and political Zionism (the State of Israel) mutually destroy each other. Meanwhile the other nations, once more divided on this issue will be constrained to fight to the point of complete physical, moral, spiritual and economical exhaustion…We shall unleash the Nihilists and the atheists, and we shall provoke a formidable social cataclysm which in all its horror will show clearly to the nations the effect of absolute atheism, origin of savagery and of the most bloody turmoil.”

In recent times, the organizations that further Illuminati goals are Council for Foreign Relations, Trilateral Commission and the Bilderbergers. Here are some books: The Illuminati: Facts & Fiction by Mark Dice, and The Illuminati original by Adam Weisshaupt.

Malaysian Airlines Flight MH370 disappearance

Boeing 777 (symbolic picture)
Boeing 777 (symbolic picture)

A recent theory about the whereabouts of the missing plane. On it, there seemed to be an awful lot of technical personnel, involved in developing military hardware. They supposedly worked for a company named Freescale Semiconductors, which was in a patent wrestle with the Rothschild family. Acording to the story, Israeli agents and elements of US military hijacked the plane and secretly flew it to Diego Garcia military base in the Indian Ocean to debrief the experts and possibly use the plane in another 9/11-style attack in the future.



Construction and visualization of visualization networks

A few words for technologically minded.  The networks were constructed by text-mining the source material, isolating known entities in sentences by means of massive dictionaries, connecting them in subnetworks (each sentence – one subnetwork), and finally adding them in the master network for that topic. Only sentence-length subnetworks were constructed, although it would be probably more fruitful to connect entities in paragraphs too. That would yield a too convoluted master network, so I stayed with sentences for clarity.

The dictionaries were automatically generated from source texts, then edited, Many synonyms had to be added, since my dictionary generating technique relies more on brute force than on semantic aspects of text. Again, the connections are not semantic, which means that if there was a sentence “The Illuminati are NOT connected with the CFR”, Illuminati and CFR would still be connected. Here I’m relying on the power of statistics: in majority of sentences there mostly appear connected entities. For the minority in which they are not, the bonds between them are too weak to influence the big picture.

I did try to process volumes of texts with a natural language processing framework, namely Apache OpenNLP, but got frustrated with the amount of work that would be needed for this little hobby project. I’d need to train the classifiers to extract named entities, which is no small feat, and I’d probably not use them again. To gain some insight in types of connections between these entities, I tried parsing the sentences into parse trees, then extract relationships, but parsing tech is not very accurate. It would probably do, again relying on power of statistics, but the sheer amount of relationship types would add little to visual value of the graphs, so I decided that I’d do this with a simpler project first. The logic I wrote is still in project source code, so if anyone is interested, mail me (About page) and I’ll send it your way. Same goes for the graph files and the categorized dictionaries.

Finally, the topic networks were exported as subgraphs, so that every node in the network is represented by a subgraph. These subgraphs are added into – or removed from – the master graph by the client. The networks in Browser are managed by sigma.js. Preliminary analysis was done in Gephi, I recommend Network Graph Analysis and Visualization with Gephi by Ken Cherven.

Additionally, geographic entities were extracted for each node. These are represented on a small map in the bottom of the screen. Map is managed by d3.js.

Interacting with visualization

There are two modes – reading the story or exploring on your own. Switch between them by clicking a button on top right of the graph. While read the story, the graph will change in real time as you scroll the text down. If you choose to explore, you can click on terms, and their subgraphs will be interactively added to the master graph.

Clicking on a graph node will expand it (load its associated nodes and display them, if previously not loaded), or delete it, if it was already loaded, at the same time showing the text from which its existence was text-mined.

There’s no way for the user to control the map. It’s there for informative and decorative purposes.

There’s more help in the main visualization, check it out!