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Welcome to my talk. Thanks for your nice introduction and the nice welcoming from you guys!
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You see the talk has the allusive name "Surveillance and language"
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which obviously alludes to Foucault with "Surveillance and punish" (Discipline and Punish in English)
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However, long before Foucault presented the genesis of the disciplinary society,
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you find a lovely moral tale in a children's book,
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which is named "The Kid in the glass house" by Heinrich Oswalt, written in 1877 and very foreshadowing
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In Frankfurt lives a glazier master,[br]Mr. Lebrecht Sheibenmann his name;
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He had a little daughter,[br]Who never wanted to be washed.
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And Gretchen came with sponge and soap,[br]So the bad girl ran away;
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It even flipped the washing table -[br]The water flooded the house.
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So Mr. Lebrecht Scheibenmann began[br]to build a strange house,
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A house made only of glass, that, alas![br]Was transparent throughout.
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And in this glass house[br]the bad daughter was then seated.
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So that, in order to see,[br]People stopped on the street.
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So the kid was ashamed and ran around[br]In the entire house and screamed:
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"Where can I hide?[br]You can see me from everywhere!
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The roof, the cellar, every room[br]Is made of glass! you can always see me!"
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The mother said: "My dear child![br]There is a quick fix to that:
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If people see you decent[br]They will pass by;
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[...][br]The daughter remembered that;[br]And tried to be seemly.
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And because it no longer screamed while washing,[br]Other people never laughed;
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Since everyone who peeked into the house,[br]Sees a kid that's very seemly.
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And if you have your own child, you people,[br]That always screams while washing,
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Just tell it Mr. Lebrecht Scheibenmann,[br]He will deliver you a glass house immediately.
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Yes, there... tentative approaches to applausing laughs[br]Applause
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Yes, interesting story, that is certainly fitting for our times
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as Lebrecht Scheibenmann is named Keith Alexander and works for the NSA
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The NSA has made glass houses out of all our homes
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we can all be seen in these glass houses
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and you don't know, or at least I'm quite sure that one pursues educational purposes
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that certain actions are no longer acceptable
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and that we internalize this observation
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Regarding this observation, language obviously plays a very important role
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Many of our statements take place in the medium of language
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This has also given hackers the idea to trick the NSA with a site like "Hello NSA"
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A website which assembles suspicious words into messages like a "bullshitter"
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and these are then tweeted, mailed or chatted upon
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to achieve something like the "operation Troll the NSA:
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that you can jam the NSA scanners, so that you can execute a DDOS attack
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simply by sending too much content, which is basically suspicious on the basis of keywords
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The point of my presentation is showing that the image of the NSA is wrong.
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We cannot assume that at the NSA people really print something
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as soon as a keyword is displayed and laughter start to analyse everything
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and look at it closer and do a qualitative evaluation
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and this certainly is a very intensive task
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and therefore a keyword spam DDoS would certainly be ineffective
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You all have probably read the thanksgiving talkingpoints of the NSA.
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I don't know if you stumbled across it, that under the 4th point there is something utterly important
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"NSA brings together the best linguists, analysts, mathematicians, engineers and computer scientists
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in the United States."[br]and the linguists are named first.
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slight laughter
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So you can see that the NSA is definitely aware of language as an important medium
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and which is also very important to them. In that it surely makes sense to deal with that
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It happens that the secretary of the Interior has leaked the most recent analysing software, the "Advanced Security Toolkit"
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Developed by the Von-Leitner-Institute for distributed realtime java. laughter
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First, we'll look at today's mission.
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Today's task is to check out the German blogosphere
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that seems to be radicalizing since the government's take-over by the grand coalition
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it's important to check if actions are in preparation to identify radical subjects if necessary,
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which are especially striking. As a start, we choose our targets, of course some are suggested to us
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Unfortunately I can only present a small selection of possible targets. I would have loved to take more
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There are a few socio-critical blogs and news sites
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like blog.fefe.de, Indymedia, Mädchenmannschaft, Netzpolitik.org, rebellmarkt.blogger.de
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And religiously motivated websites like kreuz.net islambruderschaft.com blog and discussion board salafistic
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and of course we confirm the selection. This is a very sensitive selection
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The following analyses are possible. Naturally, I can only show a selection of possible analytic tools today
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I wish I could show lots more, but there won't be enough time.
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First we'll look at what authors write about possible sensitive targets
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Meaning we'll make a target analysis.
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On the basis of Name Entity Recognition it examines the collocation for possible terror targets
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We have to... what is this? ...let's have a look in the manual, what Named Entities are
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since it is our first day today
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First of all, Named Entities are expressions which distinguish one entity clearly from other entities with similar attributes
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Spontaneously one thinks of names, but it's not trivial to say what a name is
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Accordingly, Named Entity Recognition is the procedure with which one identifies such Named Entities
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There sure are different classes of Named Entities, e.g. people, organisations, places
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Sometimes it's not very clear what belongs to a certain Named Entity, e.g. "der Bundestag" (Lower House of German Parliament)
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this can be a geographical place as well as an organisation
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Now we still need to know what collocations are
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They are statistically overly random frequent word combinations
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so "we define a collocation as a combination of two words, that exhibit a tendency to occur near each other in natural language that is to cooccur”
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like "take a road", "go down a road"
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Those are typical connections between the words "road", "go down", or "take"
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and these connections form collocations if they are overly random
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as we could determine with statistical tests
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and we can observe them in natural language
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One example - you don't need to read that now - I wanted to show an example for the word "Spezialexperte"
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you can see the "keyword in context" here, being the requested key word
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and you can see the contexts of this word, so apparently they haven't found a "chosen special expert for internet issues"
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We won't have to make a quiz game of what blog it could come from
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What you do then, for a collocation analysis you examine contexts
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e.g. here five words on the left, five words on the right till the beginning or end of a sentence
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You just count the words that are in the blue area
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and you compare the relative frequency with the words which are on the left and right in the white area
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If a word appears significantly more frequent in the blue area, you can say it is a collocation of the word "Spezialexperte"
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What is striking here for example is "kriegen" or "Adobe-Spezialexperten" laughter
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You can visualize collocation as graphs laughter
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The knots denote lexemes (I'm not sure what's there to laugh about)
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(that's serious linguistics!) and the edges denote "is collocation of"
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So here you see "the best of the best, sir", Sarrazin and Mehdorn belong there.
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It proliferates a little more. "Adobe-Backup", "Backup-Spezialexperten“ … interesting
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Ok. Now we are in the area of the target analysis. Let's start the analysis.
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What is it we are doing there? What we're doing is recognizing all Named Entities in all Corpora
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We first calculate it with methods of mechanical learning.
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Meaning you examine certain contexts in which the Named Entities stand.
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We have a training corpus which already knows what Named Entities are
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e.g. that "Bundestag" is an organisation and the software learns from these contexts
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what typical contexts for such Named Entities are and tries to apply them to new Corpora
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What we're doing here: we identify in all corpora, in all blogs, that we examine, the Named Entities.
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we categorize these Named Entities after people, organisation, geographical locations and other
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and then we calculate the collocations to the relevant Named Entities.
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e.g. "Angela Merkel" could be interesting or something
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And then we also look in the collocations, if they contain any danger words
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Meaning words that indicate terror plans or others. Now we'll do that.
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The analysis seems to be finished and the result is, we have danger level 1 of 5, so it's not really tragic
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the software suggests a check of the danger level regarding Berlin
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being the location of donalphonso, the blogger of Rebellmarkt
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A potential target of Fefe is the SPD (Social Democratic Party) laughter and the Maedchenmannschaft one is "Kristina Schroeder" (Minister of Family Affairs)
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As an example, we now have gotten an order to see what bad things donalphonso writes about Berlin and if he is planning something
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Now we can display collocation graphs or geo-collocations
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This means that we have a map and at the places which donalphonso writes about there are the correspondent collocations
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In America he writes about Boyd and culture, lone perpetrators, confused and "hate mail" and stuff
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Germany, Middle Europe is in the focus of course. It goes down till Italy
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There you can also see what donalphonso writes about
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We're approaching Berlin. There are too many collocations to evaluate
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So we look at our collocation graph and look for references to terror that could take place
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I'll read out some: " „Berlin“, „Slum“, „Reichshauptslum“, „arm“, „Transferleistung“, „abscheulich“, „Berliner Hipster“ laughter
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While this may show quite a negative attitude towards the subject, it's not exactly suspicious of terror.
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The other potential target were the organisations "SPD" with Fefe
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We'll look at the collocation graph. Fefe and the SPD. laughterapplause
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hey „betrayer party“, „fall-over party“, let's turn back briefly
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In total, in the entire list we really found words such as:
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„hang“, „force“, „top candidate“, „betrayer party“, "fall-over party“, „pest“, „cholera“ laughterapplause
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If we look at the collocation graph, we can already see that those are accusations
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But Fefe is not planning to finish the top candidate off
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Let's continue with the ideology monitor. We'd want to take some measurements now...
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It has been proven that the NSA has filed many software patents for algorithms about Named Entity Recognition
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There has been quite some research going on some time ago
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But first you find out what interesting targets are and what is said about them
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You can certainly improve that by measuring ideologies.
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What we want to calculate now is the similarity of texts, from blogs to certain ideologies
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We have the possibility of measuring extreme leftist, rightist or islamistic attitudes
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We do this by calculating typical collocations... for a certain corpus
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From this corpus we learn. So that's our model of comparison.
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