A program for analysis of how websites link to each other. The background of the software is a research collaboration between researchers from Karlstad and Bergen, to investigate how the scandinavian online news organizations link across geography, ownership, local/region/national scope, funding models, etc.

Set up the sqlite tables with (make sure the config.ini is ok and you google doc ready):

python setUpNewsNet.py

The collect data

python getNewsNet.py

And finally generate a graph file for analysis

python createGephiGraph.py filename.gexf

pip install

• requests
• tld
• beautifulsoup4
• pytrie
• networkx

Change the config file to work with your domain of study.

start mongodb with mongod

• må adde indexer for mongo db.newsnet db.external.createIndex( { "to_url": 1, "from_url": 1 }, {background: true} ) Btree::insert: key too large to index, failing newsnet.external.$to_url_1_from_url_1 1100 { : "http://www.facebook.com/sharer.php?p%5Bsummary%5D=Charlie%2520Yvander%2520och%2520Niklas%2520Eriksson%2520driver%2520sedan%2520ett%2520%25C3%25A5r%252...", : "http://www.dt.se/dalarna/alvdalen/synskadade-fick-kora-snoskoter" } db.external.ensureIndex( { "to_url": 1, "from_url": 1 }, {background: true} )

  also fails

  db.external.createIndex( { "from_url": "hashed" }, {background: true} ) virker! Men speeder et av to felter opp spørringen min? Kanskje ikke.. Kan ikke bruke hased index i compund index. Sier manualen..

Lage index for createGephiGraph: db.external.createIndex( { "from_base_url": "hashed" }, {background: true} )

We populate a database table with as list of urls with corresponding coordinates for this urls (a newsroom) location.

url is_composite_site lat lon Local_domain News_Outlet_Name Level http://birkenes-avisa.no/ no 58.334682 8.231821 Birkeland Birkenes-Avisa Local Aust-Agder http://bjuv.lokaltidningen.se/ 56.243771 12.862725 Lokaltidningen Söderåsen lokal/regional Ängelholm

1. We visit each of these urls. We collect internal links and keep track of these in a list. External links are stored to db.

2. The internal links we have found, and not yet visited, we visit and store external links to the database and keep track of new internal links in our list.

3. Repeat step 2 for as many "levels deep" we feel the need to map out. This task will grow to a really big task quite fast.

Note: To go 2 levels deep (after the initial list is generated from visiting the "clean" starting url) will get you far, going 4 leves deep makes this a very slow process if we examine a "old", big and complex site. Remember that most web content tend to continue to exist online, even if its not easy to find from the front page on the site. If you run into sites with slow response time (e.g. not cashed hard) this also can become a long wainting game..

Run the createGephiGraph.py file to genereate .gexf-files that can be opened and studied in Gephi [https://gephi.org/]. Remember to do this after you have collected data with getNewsNet.py

crashed on vk.se requests.exceptions.InvalidSchema: No connection adapters were found for '.http://www.vk.se/706572/kommunen-slutar-med-krankande-test' --> added exception for InvalidSchema With 4 levels deep, we stick on some sites (e.g. vg for almost interneties).

My v2 (2 levels deep) also died when my internet connection broke during the storm.

Når vi skraper sammensatte, som http://www.dt.se/ludvika ser det ut som vi ikke finner stort. Dette kan være fordi dt.se allerede er scrapet, men kan det også hende at interne/scraptede/etc settes feil på sammensatte url-er når objektet instansieres?

restart på p4.no (status i top indikerer at den har sovna, ting stod stille. dette var 2levels dyp, tror jeg)

restart (den grådigre 4 leves neep), ganse nært siste site, tror jeg.

• sn.dk har et meget pussig oppsett. sn.dk funker mens http://sn.dk/Naestved/ funker ikke.
• http://sn.dk/naestved/ funker. mens det finnes mange lenker av typen http://sn.dk/Naestved/Dagli-Brugsen-faar-en-ekstra-chance/artikel/556209 Pussig!

Vurderer å skippe sn.dk... Eller skulle jeg tvinge alle entiteter ned i lower-case?

• Subdomener fra nwt.se Fungerer slik som at de oppfattes som eksterne domener. Hentes ikke med som innen vårt "scope of study" når vi lager grafer. I forhold til nwt.se/amal (eller liknende) sånes jeg dette er ok.
• domener med subdomener ([a-z].lokalavisen.dk/ or [a-z].lokaltidningen.se/) stripper jeg vekk trailing slash .rstrip("/") som en sær-behandling da disse har en tendesn til å lenke hverandre uten trailing slahs (i den svenske familien i alle fall). Dette har ulempen at andre domener med www som dubdomener (http://www.barometern.se/oskarshamn/) også mister trailing slahs. Bare et problem hvis i tilfeller ala http://www.barometern.se/oskarshamn-i-brann der entiteten http://www.barometern.se/oskarshamn forsvinner i resten av lenken. Jeg anser det som ok.

bruker mosh for å ha mobilt shell.

screen - the basics screen ctrl + a :sessionname geonews # eller screen -S geonews -L # (-L for log) ctrl + a d (setter session i detached mode) screen -ls (lister ut dtached screens) screen -r (kobler på)

Kjør: $ screen -S geonews -L (nyhetskategorier) est040@nelson:/GeoNewsNet$ deactivate est040@nelson:/GeoNewsNet$ source geonews/bin/activate

(geonews) est040@nelson:/GeoNewsNet$ python3 setUpNewsNet.py (geonews) est040@nelson:/GeoNewsNet$ python3 getNewsNet.py

ctrl + a d (detach fra screen)

vent "noen dager"

connect med screen -r geonews

når du er deconnected can du sjekke "status" med: tail screenlog.0

screen -r geonews 14/3. db'n har ikke addda noe nytt siden 12/3. Jeg stopper og starter på nytt.. Hadde kommet til dagen.no

ctrl + a d (for å koble ut igjen.)

Angrer som en hund på at jeg gikk for sqlite. Tror det er indexene mine som gjør at dette går så seint. Burde gått for mysql eller mongodb i stede.. .. og det burde vært paralellisert...

The code does what it is intended to now, but it is slow. I think I might have put the indices on the wrong fields...

Wrote gephi file: halfbaked.gexf Done in 20262.76573419571

sqlite> SELECT * FROM external_links WHERE from_base_url = "http://sn.dk/naestved";
Run Time: real 80.402 user 3.325506 sys 15.995853

sqlite> .indices external_links
external_from_index
external_to_index

lager index på from_base_url sqlite> CREATE INDEX external_from_url_base_index ON external_links (from_base_url); Run Time: real 69.543 user 12.016445 sys 13.564150

sqlite> SELECT * FROM external_links WHERE from_base_url = "http://sn.dk/naestved";
Run Time: real 0.000 user 0.000093 sys 0.000044

Wrote gephi file: halfbaked2.gexf Done in 155.7858419418335

sqlite> SELECT COUNT(link) as n, * FROM internal_links WHERE link_base_domain = "http://hoganas.lokaltidningen.se" GROUP BY link;

##with n internal lookup Wrote gephi file: halfbaked2.gexf Done in 880.4132630825043

sverigesradio is not a composite site like the others. It has sections form various districts (sverigesradio.se/place) but internally it works by linkin these as integers, http://sverigesradio.se/sida/artikel.aspx?programid=94&artikel=6422029 where 94 is Gotland, etc. I will not consider sverigesradio as a composite site.

• switched to lxml as parser
• added country for all sites.
• SVTs urls are wrong. they say nyheter/regional/{place} but should say nyheter/lokal/{place}
• now total of sites is 673 (sverigesradio is reduced to one actor)
• droped newsNetActors collecttion, and reloaded from google docs

Norway - http://www.medietilsynet.no/mediebildet/eierskap/

And also: RQ: does ownership matter for linking? Do newspapers within the same corporation link more to each other than to others? RQ: what type of content is linked the most? (e.g. sports/poltiics etc - if it is possible to content analyse the urls down the line…) RQ: what characterises the smaller papers that don’t link out at all? (there seems to be quite a few of these) e.g. where are they located? Coast? North? Independent ownership? etc RQ: What is the average geographical distance (in km) between major/minor linking nodes? (if this is possible to find out) RQ: what characterises the ‘islands’ of networks (e.g. nrk has its own internet)

Is a good way to go about this just to find the answers to these questions first? And then group the RQs under the abstracts and write out the analysis?

One more thing: I have recruited Aske Kammer to the project. He’s the only Dane (according to himself, and I believe him) that knows anything about newspaper structures and media regulation in Denmark. I’ve sent him our abstracts and shown him the programme. I’ll link him to our docs so he can check our data. He’s aware he’ll be fourth author on this, but happy to join our project. Hope that’s good with you two.

a) How interlinked is the Scandinavian news ecology overall? (we can say something about this, but have nothing to compare to) Avg degree, some measure of density, b) How interlinked are the countries overall? (this we can answer because we can compare them) avg degree pr country. distance across theach sub-network. how close to "complete" the networks are. c) Systematic interlinking? What is more significant (might have to do a statistical analysis of this?) x) ownership, y) geographic location, or z) size (local/regional/national)? (this we can answer, if we rank all the nodes according to centrality or connectedness, then run against ownership and size (circulation), and size - or according to distance from the capital, in km normalised/average, might be tricky for Denmark) d) Super-hubs and superhighways: for each country and what are their similarities and differences (this we can answer). Are there Scandinavian superhubs and superhighways? (can we answer) (importance of national/linguistic borders) Does Scandinavia link beyond itself? (might be able to answer based on URL rankings) hubs (from HITS), autority, pagerank + betweeness centrality + degree. e) Intermedia agenda setting: characteristics of the Scandinavian hyperlink ecology: to what extent are agendas local, national and nordic? To what extent are they international? (might be able to do this based on URL rankings) (tighter links the lower/more local you get or tighter links the more nation you get? The network is tighter the higher you get in the ecology)

2. Network brokers: Network Centrals: who constitute the ‘hubs’ in the network(s)? (who feeds whom?) (are regional papers brokers between local and national agendas?) (we can answer this) (bilateral relations, must identify regional brokers then, 5-6 players in Norway) Is centrality/broker position more related to x) ownership, y) geographic location, or z) size (local/regional/national)? (statistical analysis) Network direction: Which way does information flow? Upward (from local to regional, from regional to national), downward (from national to regional, from regional to local) or sideways (within corporations and local to local, regional to regional or national to national within regions)? (this we might be able to answer) Media type (the role of broadcasters) - are PSBs more important than newspapers in brokering the information flow? (characteristics of Scandinavian media system)(PSBs have their ‘own’ internet based on CMS-scaling/architecture, biased towards own ecology; systematic linking - we can create threshold to determine that these are ‘automated’) Intermedia agenda setting: the importance of brokers in linking national online news agendas + the importance of corporate ownership for ‘connectivity’ + the importance of place (geographical centrality) for intermedia agenda setting ‘power’? (this we can answer)

3. Network shadows (excluded/disconnected): Network exclusion: what portion of the network is excluded from the agenda setting ‘flow’? (threshold must be determined, percentage or random number or ‘scaled’ number) What characterises exclusion: ownership, geographic location or size? (per country) (decide on cut-off point for inclusion/exclusion) Centre/periphery dimensions: The importance of North/south dimensions + proximity to the capital for network centrality in the three countries. (statistical analysis) Intermedia agenda setting: Implications of participation in hyperlinked ecology for ability to perform intermedia agenda setting (slightly tautologic sentence, but should be entry point to discuss who are excluded - most likely these will be local newspapers) So what?

• who are the hubs in Scandinavia? Def: Hubs are nodes with high degree and betweeness centrality. Degree = inn links + out links. For all countries: (avg degree 19.9) top10: dagbladet.no 307 vg.no 288 tv2.no 263 aftenposten.no 254 aftonbladet.se 250 http://www.expressen.se 225 politiken.dk 217 ekstrabladet.dk 212 adressa.no 208 svd.se 186

  All countries are represented in the top10 list. I read this like the countries are relatively comparable and that the data collection method has not favoured any of the countries.

  Norway: (avg 20.1)

  filter on equal -> country -> "norway"

  rerun statistics -> average degree

  dagbladet.no 227 vg.no 217 tv2.no 210 aftenposten.no 190 adressa.no 162 nettavisen.no 157 dn.no 142 vl.no 137 ba.no 133 nordlys.no 130

  Sweden: (avg 14.7) <- considerably lower that no & dk. Sweden is less densely connected than no & dk.

  aftonbladet.se 171 http://www.expressen.se 150 svd.se 130 dn.se 122 sverigesradio.se 111 sydsvenskan.se 88 gp.se 86 http://www.dt.se 82 vk.se 79 metro.se 78

  Denmark (avg 19.0)

  politiken.dk 136 ekstrabladet.dk 130 bt.dk 119 ing.dk 119 jyllands-posten.dk 118 http://lokalavisen.dk 106 information.dk 97 http://hilleroed.lokalavisen.dk 96 http://odense.lokalavisen.dk 92 http://sn.dk 91

  betweeness centrality

  high betweeness centrality indicate brokers. not neccesarily those with the most links, buth thos who bridge many other nodes and connect sub-networks.

  " A node with high betweenness has great influence over what flows -- and does not -- in the network. "

  All counties:

  Diameter: 8 Radius: 0 Average Path length: 2.9867914111522045 Number of shortest paths: 336902

  ekstrabladet.dk 41263.93636843192 dagbladet.no 29611.224392797114 aftonbladet.se 27663.82829942289 http://www.expressen.se 26885.243524572707 bt.dk 25917.69692662152 politiken.dk 25621.63665811628 http://sn.dk 24143.091811246548 vg.no 23409.96047976205 tv2.no 20882.578110514445 aftenposten.no 17786.310645656176

  Norway

  Diameter: 5 Radius: 0 Average Path length: 2.209987950880611 Number of shortest paths: 39007

  dagbladet.no 4454.433816613989 tv2.no 3527.9099772020304 vg.no 3486.637313937973 adressa.no 2861.665249068022 aftenposten.no 2510.561352960613 aftenbladet.no 1639.330697848793 vl.no 1231.4625231848288 nettavisen.no 1183.553803202622 bt.no 1175.575012093173 http://www.nrk.no/sognogfjordane 1175.2475101928578

  Sweden

  Diameter: 6 Radius: 0 Average Path length: 2.5861001782890973 Number of shortest paths: 29727

  aftonbladet.se 5803.547137243973 http://www.expressen.se 5609.396352495715 dn.se 2472.5492983872805 http://nwt.se 2105.714641924687 http://landskrona.lokaltidningen.se 2040.7825600906715 http://www.stockholmdirekt.se 2011.9543219923016 sydsvenskan.se 1982.2296656207 svd.se 1953.916633234799 http://lund.lokaltidningen.se 1452.8707196354098 http://www.expressen.se/kvallsposten 1234.5744801655057

  Denmark

  Diameter: 7 Radius: 0 Average Path length: 2.7496524533375877 Number of shortest paths: 43879

  ekstrabladet.dk 9172.043195072005 http://sn.dk 7476.216659506314 bt.dk 6184.624521304597 ing.dk 4924.781942814383 politiken.dk 4553.08232835623 tv2.dk 4502.381811943395 http://sn.dk/vordingborg 3968.782662034418 http://www.dr.dk/nyheder 3253.3922526212164 jyllands-posten.dk 2646.7783662689953 http://www.fyens.dk 2068.7279521779947

  HUBS are also a measure from the HITS algorithm, identifying ca 5 big hubs in each country. The authority measure from the same method identifies more or less the same nodes. PageRank is a bit stricter, giving us 3-4 big nodes in each countries: no -> vg, dagbladet, tv2, aftenposten se -> expressen, dn, aftonbladet dk -> ekstrabladet, politiken, bt

• An oposite: who are not central, not brokers. who are "off the race"?

Sites order by dregree asc, with max(order) 2. Notice the nr of internal scraped to get these, this is not because of lack of data, this is just the fact that these site rarely link other newssites in our sample. It can be skewed because of pay walls, but that does not change the fact that these are isolated in regard to a casual reader (without a subscription).

Site Degree Betweeness central n_internal_scraped level bha.dk 0 0.0 3090 local oehf.dk 0 0.0 1729 local oyblikk.no 0 0.0 142 local folkebladet.dk 0 0.0 1217 local graastenavis.dk 0 0.0 92 local alingsaskuriren.se 0 0.0 42 local http://www.vamdrup.dk/news 0 0.0 481 local uge-avisen.net 0 0.0 774 local lyngposten.dk 0 0.0 280 local sydvesten.no 0 0.0 249 local vestnesavisa.no 0 0.0 60 local galtenfolkeblad.dk 0 0.0 2017 local midtvendsysselavis.dk 0 0.0 44 local blekingeposten.se 0 0.0 24 local gudenaadalens-avis.dk 0 0.0 383 local klt.nu 0 0.0 57 regional vestavind-sveio.no 0 0.0 49 local vaksdalposten.no 0 0.0 163 local meloyavisa.no 0 0.0 230 local sallingavis.dk 0 0.0 117 local hf.no 0 0.0 477 local ugeavisenkarup.dk 0 0.0 1241 local folkebladet.info 0 0.0 2244 local farsoavis.dk 0 0.0 2326 local http://minby.dk/vanloese-bladet/ 0 0.0 431 local gladsaxebladet.dk 0 0.0 1848 local dragoer-nyt.dk 0 0.0 764 local vesthimmerlandsavis.dk 0 0.0 1033 local kungalvsposten.se 0 0.0 361 local fjendsfolkeblad.dk 0 0.0 1598 local sydtid.dk 0 0.0 8421 local rnn.dk 1 0.0 2083 local lanstidningen.se 1 0.0 629 regional varmlandsbygden.se 1 0.0 470 regional smbt.se 1 0.0 416 regional skaraborgsbygden.se 1 0.0 648 regional hoglandsnytt.se 1 0.0 1564 regional tranas-posten.se 1 0.0 2310 local skargarden.se 1 0.0 1364 local firdatidend.no 1 0.0 181 local molndalsposten.se 1 0.0 349 local uppsalatidningen.se 1 0.0 300 local http://minby.dk/oesterbro-avis/ 1 0.0 438 local bsnett.no 1 0.0 531 local http://nyhende.komheim.no 1 0.0 333 local aarsavis.dk 1 0.0 1915 local kanukoka.gl 1 0.0 5886 regional amliavisa.no 1 0.0 69 local nsnet.dk 1 0.0 1 local bmz.se 1 0.0 1 regional velkomne.no 1 0.0 425 local samso.dk 1 0.0 678 local markbladet.se 1 0.0 1496 local http://dinby.dk/frederiksvaerk-ugeblad 1 0.0 13 local nyhetsbynleksand.se 1 0.0 439 local kidsnews.dk 1 0.0 389 national vaxjobladet.se 1 0.0 237 local http://minby.dk/city-avisen/ 2 0.0 476 local http://minby.dk/valby-bladet/ 2 0.0 511 local http://www.stockholmdirekt.se/tabynyheter 2 0.0 12 local http://www.direktpress.se/goteborg 2 0.0 754 local http://www.stockholmdirekt.se/sodrasidan 2 0.0 19 local http://www.stockholmdirekt.se/malarotidning 2 0.0 16 local nordrenett.no 2 0.0 595 local aasanetidende.no 2 0.0 1251 local varingen.no 2 0.0 241 local http://www.stockholmdirekt.se/enahabo 2 0.0 16 local frostingen.no 2 0.0 344 local statstidende.dk 2 0.0 8641 boblad.no 2 0.0 324 local http://www.stockholmdirekt.se/kista-rinkeby-tensta 2 0.0 10 local 8sidor.se 2 0.045454545454545456 2063 national selbyggen.no 2 0.0 515 local vtb.no 2 0.0 155 local naestved-bladet.dk 2 0.0 402 local skive-her.dk 2 0.0 10281 brandebladet.dk 2 0.0 5730 local hasserisavis.dk 2 0.0 392 local orusttjorntidningen.se 2 0.0 169 local tysnesbladet.no 2 0.0 531 local drangedalsposten.no 2 0.0 733 local sovesten.no 2 0.0 716 local http://minby.dk/vesterbro-bladet/ 2 0.0 426 local http://www.stockholmdirekt.se/viisolna 2 0.0 12 local austevollforlag.no 2 0.0 2885 local ballerupbladet.dk 2 0.0 1732 local nordschleswiger.dk 2 0.0 2534 local synste.no 2 14.271759618404435 56 local http://minby.dk/amagerbladet/ 2 0.0 273 local kjavis.dk 2 0.0 1935 local

This is local sites, (local 78, national2, regional 8,blank 2), more in denmark than the other (denmark 40, sweden 26, norway 24).

If we do the same with betweeness centrality, we get a list of 233 sites with 0. These are also mostly local (local184, regional26, national10, local/regional3, (blank)10) and slightly more danish. (denmark 121, norway 50, sweden 62)

Summary: It is quite normal for local news sites to be quite isolated. We might even say disconnected by choice, as it would be very easy to just add links. (Discussion, do these only cover stuff that is not covered in the bigger regional/national sites, and thus not linkable within the pre-defined scope of our study?)

• who are systematically linking each other (more than once or twice) How do we define systematically? Is it a number threshold (e.g. more than 10 times)? Is it relative to the actor doing the linking (percent of all links to other sites in our sample is higher than n pr cent)? Is it relative to the number of internal links we have scraped to get the out links (nr of links to a particular actor divided by all outbound links including stuff outside our sample (I have the data for that)), or some other idea of "systematic"?
  • Can I refrace this to: What links are high in weight? (no) We have weighted the egdes, by the number of times a link from one node to another is made. Perhaps such a simple solution can suffice? (no)
  • Better: divide the weight on the n_internal (as a number of size of the news site) and get a likelyhood for that link. Then get top 10% of this new list. (penalizing sporadic linking) This I have added as a new attribute to the edges: w_over_internal

Ok. Så the systematic linking is the head of the long tail distribution of the w_over_internal attribute. Top 10% os ok?

There are 13406 edges. The 1340th has w_over_internal 1.875 Adding filter range(w_over_internal) 1,875 - max Within the filter Degree ranger (more than 1, so we remove node without edges) NB: Nested filters are applied with the most inner filter first, then the outer last. Odd, but true: http://www.clementlevallois.net/gephi/tuto/en/gephi_advanced%20functions_en.pdf

That gives us the network of truly systematic linking. 246 nodes, 1339 edges. In Norway NRK stands out. Some local clusters ({steinkjæravisa, inderøyningen.no, t-a.no}, {rbnett.no, smp.no}, {østlendingen, ringsakerblad} {rogalangs-klyngen, 9stk aviser})

In Sweden similar patter emerges: the stuff that is split up (like NRK) "holds together": {expressen.se x3, http://www.helahalsingland.se x4, allehanda x2, barometern.se x2, unt.se x2, dt.se x5, ekurinen.se x2, stockholmdirekt.se x7} Some clusters includes other nodes than its own domain: {nwt.se x9 this is also linked to sla.se and mariestadtidning.se} {lindernytt.se, aftonbladet.se} {vaxsjobladet.se, olandsbladet.se} {partilletidning.se, alingsastidning.se, lerumstidning.se},

Denmark has 143 nodes satisfying these creteria. Much more than se and no. With more nodes comes more complexity. Some "normal" patterns emerge. Stuff that is split up clusters. {nordjyske.dk x12, dr.dk x4, sn.dk x35, etc. } And some "normal" smaller clusters, probably with the same owner: {tv2.dk, mediawatch.dk, energiwatch.dk, medwatch.dk, finanswatch.dk, fodevarewatch.dk} {aoh.dk, mja.dk, aoib.dk} {b.dk, aok.dk, business.dk} {fyens.dk x11, ugeavisensvendborg.dk, ugeavisen-odense.dk} Then the complicated part: {city}.lokalavise.dk makes a giant sub-network. It connetcts with jyllandsposten.dk and ekstrabladet.dk (but those two does not connect directly). This .localavisen.dk is probably this densely linked because of the CMS arcitecture, but in effect it connetcts Denamrk. dinby.dk/{stuff} is not dense enough to show up as one network here, but in smaller clusters that connects with other things, such as amtsavisen.dk, dagbladet-holdterbro-struer.dk, dagbladetstringtjern.dk etc. This too might look like ownership. Denmark should probably be studies closer because of its more chaotic stucture with dinby and lokalavisen.

Answer: By looking at the 10% strongest ties as a representation for systematic linking, we find newsrooms within the same brands and what looks like the same owers (not tested).

This question can be explored more bye looking at 20-10% range, to see what the second tire looks like, that's propably where we find the "newsworthiness"-linking (where links are made because of some newsroom setting the agenda, and others linking to them to connect to the current agenda)

• Does location in a mayor city help network centrality?

• Does local news sites connect their readers to the larger national disqorses?

• does the big hubs link out to the local Filter on betweenes centality, then inter egdes on level. BC does not seem to show up in any filters, so I use degree. That should give aprox the same central nodes. Degree range(100-307) +

• can I find the "umbrella-structre" from Helles theories?

• to what extent are the countries connected? Filters: range(w_over_internal) + inter Edeges (countries) + degree (1 or more) Gives all nodes that still has edges that cross a border, where a certain w_over_range threshold is meat. With no threshold 37,8% of the nodes has a link across country borders, 9,32% of all edges. That is more than expected. But: this is just edges, we have to include weight in order for this to sober up. The once of these edges that occur more than 10 times makes 209, and of these the top 46 are from {place}.lokaltidningen.se to ekstrabladet.dk or politiken.dk (same owner?). The degree of the network goes down to 2,27. We are much less densely connected. A typical site that does link across countries, only reaches 2.27 sites across a border. That is about a a 10th as connected as it is within its own country. But: still more than I had expected. Of these 209 we check on In-Degree and Out-degree. This tells us who links more out (active across borders, international mindset) and who are linked to (relevance across borders). The out-degree (more different links across borders) top list is: ing.dk, dagbladet.no, informasjonen.dk, modkraft.dk, aftenposten.no, vg.no, dn.no, dagen.no, adressa.no, bohuslaningen.se, bt.dk and aftonbladet.se (all have 5 or more in out-degree in this subset.) The biggest in-degree (are seen as relevant across borders) politiken.dk, akstrabladet.dk, expressen.se, aftonbladet.se, dn.se, vg.no svd.se, bt.dk, aftenposten.no and dagbladet.no (with 6 or more links across countries) At range(w_over_internal) 2: we only have politiken.dk and ekstrabladet.dk from denmark linked with sweden though {place}.lokaltidkningen.se 0,01 gives 39 nodes (5.8% of the network) and 56 egdes (0.42% of edges)

  Threshold 0,01 (links that happens 1 time pr 100 documents/articles) gives 39 nodes (5.8% of the network) and 56 egdes (0.42% of edges) that link across country one time pr 100 produced articles.

  39 sites does does meet this requirement, that is more than what I

• Does the regional paper connect the local and national?

• RQ: How/to what extent are the local, regional & national sites connected?

  • Network density. How far are the subnetworks (no/se/dk) across?
  • Do national connect more with national, regional & local?
  • Do we find isolated "islands" of sites?

• RQ: Who are the hubs in the Scandinavian news ecology? What do they connect to? What barriers do the cross?

  • Who in total, and who pr country take the role as central players?
  • Do these link more to international/national/regional/local?
  • Are they similar across countries?

• RQ: What is typical about the local news sites in the network?

  • inn links vs out links
  • number of national/regional to local links

• RQ: What describes the hubs in the network? Are the big city nationals only? Do the connect mostly with other nationals and regionals, or do the connect "all the way down"?

  • Are the hub only in big cities/capitals?
  • Do the big players link to local, or more to the regional?
  • Are any hubs regional/local, or only national?

• RQ: If we filter the graph pr country, do we find the same national/regional/local patterns en all countries? Do the hubs stay the same?

• RQ: Is the major linking highways between nr1 and nr2 city in all countries?

• RQ: Are the major linking highways between the major news outlets?

• RQ: To what extent do we find linking across national borders? If any, what describes this king of linking?

• RQ: In the capitals, do we find more links out than inn, in total?

• Do local newspaper get linked more than they link out themselves?