I used twitter4j and R to make a word map of Donald Trump’s tweets. I thought it would be interesting to see what his most used words are. The program downloads 3000 of his most resent tweets, unfortunately it cannot download all of the extended mode tweets. Only the first 140 characters. It wasn’t that interesting in the end.
Trump word cloud.
I have processed more of the Daniel Morgan data, and thus have an updated network of the data. Below is a visualisation of the data produced by extracting the network structure from Neo4J using R and iGraph, then saving the network as a gexf file and importing into Gephi. The network is more complete but also has edge labels.
Updated version of the Daniel Morgan data.
The released Panama data comes in the form of a Neo4J database, or the files that you can make one with, seems to me a little tricky to do much with. There is no detail beyond attributes of the different entities, so that limits us to looking at the relationships alone and it is hard to judge the significance of the relationships without the context… that said its a fun data set to play with.
I decided to draw out some graphs of how things are connected via other things. Below is one from Officers connected to other Officers via *something* else, generated via R using iGraph from the Neo4J data set. This produces a few clusters containing a relatively small number of nodes connected to others. The query that produces the graph is, “MATCH (n:Officers)-[:`officer of`]->(o)<-[:`officer of`]-(m:Officers) WHERE NOT id(n)=id(m) AND id(n)<id(m) RETURN n.name AS Officer1, m.name AS Officer2, count(o) AS Weight”
Officers connected to other Officers via anything else.
After listening to the Daniel Morgan podcast, Untold, I became really interested in the murder investigation. To help me follow it I started building a network of all the key people, organisations, and events in the case. The networks this produces can be seen here,and you can keep up-to-date with the progress on the network here.
There is an updated network image here.
The story is a compelling one, I suggest you either listen to the podcast or read the book. Very briefly it looks into the murder of Daniel Morgan, and the subsequent investigations into the murder and the police handling of the murder. The book builds a compelling story of decades of struggle by the Morgan family to get justice, and the difficultly they have had in discovering the truth.
The network is not complete, at the time of writing I have only put in the ‘easy’ bits. The network stores objects as the nodes, so people, companies, organisations. The lines, or edges, store the relationship between the objects, e.g. Alistair Morgan is ‘brother_of’ Daniel Morgan. The visualisation is produced using Alchemy, and the data is stored in Neo4J. I intend to continue to develop the network further, and the visualisation which needs things like edge labels. Once the network is more complete it would be interesting to see if there is any useful analysis that can be done on the network. It would also be interesting to expand the data to include other related and interesting cases. Such as the Stephen Lawrence murder, and the Leveson Inquiry will likely form a part of Algorithmic Indexing in the future.
Here is a picture of the network in Neo4J:
Part of the network of actors in the Neo4J database.
I was invited to give a talk at Oxford University on Crypto Wars 2.0 for the Cyber Security DTC that is jointly ran by Oxford and Royal Holloway. I have given a talk on the Crypto Wars at Durham in the past but this talk was a combination of a revisiting the the Crypto Wars today, but also a look to the future. I have produced a podcast of the talk and the slides are available here.
We have a new piece published in Business History, Complexity in History: modelling the organisational demography of the British banking sector. Continuing our work modelling the British banking sector we have responded to the very interesting comment by J Bissell, “The decline in the British bank population since 1810 obeys a law of negative compound interest“, on our original paper. This gave us the opportunity further discuss the role of modelling in understanding historical processes and present some more recent insights into the development of the banking sector. We where also able to revisit the Tipping Point in the sector and question whether it is indeed a tipping point at all.
Garnett, Philip, Simon Mollan, and R. Alexander Bentley. n.d. “Banks, Births, and Tipping Points in the Historical Demography of British Banking: A Response to J.J. Bissell.” Business History 0 (0): 1–7.
J. J. Bissell, “The decline in the British bank population since 1810 obeys a law of negative compound interest”. Business History Vol. 59 , Iss. 5,2017