Category Archives: Developing

The Connected Cars Ecology

The Connected Car Ecology

We are beginning to understand the connected car ecology as a precursor to autonomous automobility. Interestingly, and form discussions with industry experts of late, autonomous automobility isn’t the issue here – driverless cars are here in various incarnations. What many specialists are saying is difficult to imagine at this stage is how cars will communicate with other cars.

A great example is an oil spill on the road. ‘In the future’ cars will identify the oil spill, proceed with caution, alert other cars to avoid the spill or proceed with caution, and alert authorities to come and fix it.

Another interesting area I have been thinking about lately was established with my recent conversation with the Zoox crew. In talking about the transition of the horse and cart technology to the combustible engine technology, low value driving was  re-introduced as a task for the driver . The driver would have taken care of the low level driving, potholes, tree branches, gradients, while the driver would have taken care of the high level driving – location A to location B. Driverless cars once again take the low level thinking out of mobility.

These are some of the threads we are developing in the latest Moving Media discussions. In the meantime, enjoy our latest visualisation of the Connected Car ecology that highlights the organisational, political and commercial actors identified in the discussion to date. More to come…

The Connected Cars Ecology

The Connected Cars Ecology

Original image by Dean Terry

Improving the Social Network Analysis Methodology

We have been collecting data for just shy of a year now and have been developing the Twitter social network analysis methodology for a little longer than that. As you might recall, we have been following the Mobile Health conversation via the #mHealth conversation and have finalised the collection of those data. The processing is almost finished and we can now progress to the next stage of ethnography to further understand what we have collected.

We have been improving the methodology as we go and the last time we received some assistance was to write some code for the Gephi program. Recently, we have been talking with colleagues from the University of Wollongong’s SMART Infrastructure faculty, who have been developing the collection process of Twitter data. Their project is related to flooding information in Indonesia (CogniCity), however Tom Holderness has been kind enough to share his work on GitHub.

When we install this JavaScript, which uses NodeJS, we will have an automated version of the manual process we have been struggling with for the past year. Further, the code is customisable so the researcher can  query the Twitter Stream API for the specific data they require. You can read more about the CogniCity NodeJS application on GitHub.

If we can improve the processing speed further, we will have a research prototype that can be shared with other researchers who are interested in Twitter social network analysis – hopefully a post soonish will reveal this!

Ingrid Mason and Luc Small delivering the closing address of THATcamp Sydney 2013

#THATcamp Sydney, October 2013 Review

We recently presented some of our social network analysis research of the informal policy actors within the mobile health regulatory space at the THATcamp Sydney unconference. Unfortunately I missed the first day and Fiona could only attend the first session of that day, but we managed to see a full (half) day on the second.

Amongst other great projects I observed, one that certainly stood out for me was the Australian’s Women’s Register. In conjunction with the University of Melbourne, they are doing some outstanding work on collecting and analysing data about Australian women – highly recommend checking this out.

Fiona and I then conducted our session which we took as an opportunity to talk about our work so far and use expertise in the room to interrogate and develop our methodology. It was an amazing experience and you can hear the audio from our talks here.

Some of the points that emerged from the discussion include:

  • Who are the official organisations that are interacting in these conversations?
  • There is existing research to suggest that participation is driving policy
  • Debra Lupton at the University of Sydney to explore data and politics
  • Nick  Thurburger University of Melbourne, experience and developing methodology in converting Excel sheets to data clean http://languages-linguistics.unimelb.edu.au/thieberger/
  • Thresholds of data – what to use, who are they, where are they, rural voices etc – we need to think about our own thresholds which shape the data analysis
  • Demographic profiling in the process – this is critical to understand the ‘why’ of the interactions between the actors
  • Content coding around the data collection, who do we want to hear from?
  • Jake Wallace Charles Stuart university, experience in political party process
  • Policy analysis portal – I am thinking we need to develop a tool similar to this to embed in our site – that is users can bring their data to it and run their own analysis
  • Positive and negative sentiment in tweets – UWS are working in this space
  • Government institutions are legally required to collect social media conversations – interesting!
  • Internal organisational cultural behaviours influencing what is said  and what is not said via freedom of information act
  • If so, could we access Yammer data?
  • Digital engagement as opposed to social media
  • Atlas of living Australia dashboard.ala.org.au - great site to play around with in representing data
  • Internet archive to send or harvest bit.ly links – credibility of user data in government policy
  • Suirveillance in terms of peaks of use, who is using soc med and when
  • Gnip.co twitter api – this is the ‘fat tube’ of Twitter data and Fiona is working on a collaborative approach with UTS
  • Steve Cassidy at Uni of Macquarie – workflow people http://web.science.mq.edu.au/~cassidy/

And if that’s not enough, the wonderful Yvonne Perkins kept a Google doc of the session which can be viewed online.

Great session and i think it is a fantastic primer for our paper which we will present at the aaDH (Australasian Association of the Digital Humanities) conference in 2014 – thanks for the input THATcamp!

Gephi-logo

Improved Gephi processing through Java RAM allocation – downloadable

Recently, our social network analysis methodology hit a snag as the computer I am using started to crash when attempting to process our larger data sets. The data sets are not extremely large at this stage (approx 8MB Excel sheets with about 80 000 lines of text), but nonetheless too big for my MacBook Pro to handle. Just to remind you, we are using Gephi as our analytics software (open source)

I started looking into virtual servers where Amazon EC2 Virtual Servers are the benchmark in this domain. They seem to be located in Northern America, i.e. San Francisco, and I have been advised the geographical location of Amazon is good when scraping data from technology companies like Twitter and Facebook, who also host their data in a similar geographical area. However, Amazon does appear to be a little too expensive for the research budget – although very tempting to wind some servers up to collect and process our data quickly.

The second option was to lean on the national super computer infrastructure for Australian researchers, NeCTAR. I established two medium virtual servers (2 vCPU, 8GB RAM, 60GB local VM disk), installed a Ubuntu operating system, but had difficulty in talking with the system (happy to take input from anyone here).

Then, we had a meeting with Information and Communication Technology (ICT) people at the University of Sydney who have been very helpful in their approach. We have been liaising with Justin Chang who provided us with an improved version of Gephi that essentially enables us to use more RAM on my local machine to process the data sets. Justin provided me with a disk image that I installed, tested and was able to get moving with the analysis again.

I asked if I could share the Gephi with our readers, to which he agreed – and provided a step by step on how he created an improved RAM allocated version of Gephi:

- Download the ‘Gephi’ .dmg frill from: https://gephi.org/users/download/

- Open the .dmg file

- Copy the Gephi.app file to a folder on your desktop

- Ctrl + Click the Gephi.app file and click Show Package Contents

- Navigate Contents  > Resources > Gephi > etc and open the gephi.conf file in a text editor

- Change the maximum Java RAM allocation:

FROM:

default_options=”–branding gephi -J-Xms64m -J-Xmx512m -J-Xverify:none -J-Dsun.java2d.noddraw=true -J-Dsun.awt.noerasebackground=true -J-Dnetbeans.indexing.noFileRefresh=true -J-Dplugin.manager.check.interval=EVERY_DAY”

TO

default_options=”–branding gephi -J-Xms1024m -J-Xmx2048m -J-Xverify:none -J-Dsun.java2d.noddraw=true -J-Dsun.awt.noerasebackground=true -J-Dnetbeans.indexing.noFileRefresh=true -J-Dplugin.manager.check.interval=EVERY_DAY”

This enables Gephi to utilise up to 2GB RAM when processing data, you can allocate any amount of RAM here (as long as it is less than your systems RAM resources)

- save the file

- run the application ‘Disc Utility’

- from within Disc Utility click file > new > Disk Image from Folder and select the folder that you created on the desktop and then click Image.

You can download the DMG with the two versions of Gephi (1GB and 2GB).

Finding Mobile Internet Policy Actors in Big Data

Adapted from the original image by Cambodia4kids.org, published under CC BY

Adapted from the original image by Cambodia4kids.org, published under CC BY

Some recent thinking by the Moving Media research team on the implications of big data research in relation to locating informal policy actors:

Scholarly interest in data privacy and the regulation of mobile Internet has intensified in recent years, particularly following Edward Snowden’s 2013 revelations about Prism, the US government’s secret communications surveillance and data mining project. Much analysis has focused on the politics and architectures of data privacy regulation and network access. However the surveillance moment also invites scrutiny of academic data gathering and mining online. In open governance movements such as Occupy there has already been considerable debate about the ethics of big data research, particularly where the aim is to track individuals’ online agency around political processes and policy activism. With that context in mind, this paper examines the methodological implications of conducting large-scale social network analysis using Twitter for mobile Internet policy research.

Mobile internet is emerging at the intersection of broadband internet, mobile telephony, digital television and new media locative and sensing technologies. The policy issues around the development of this complex ecology include debates about spectrum allocation and network development, content production and code generation, and the design and the operation of media and telecommunications technologies. However not all of these discussions occur in formal regulatory settings such as International Telecommunications Union or World information Summit meetings, and not all are between traditional policy actors. Increasingly social media platforms such as Twitter and Linked-in host new networks of expertise, informal multi-actor conversations about the future of mobile Internet that have the potential to influence formal policy processes, as occurred during the January 2012 SOPA/PIPA campaigns in the US.

As part of the three year Australian Research Council Discovery project Moving Media: Mobile internet and new policy modes, this research team is mapping and interpreting the interplay between these diverse policy actors in three areas of accelerating media development: digital news, mobile health and locative media. However research into informal policy networks and processes online presents interesting problems of scale, focus and interpretation, given the increased affordances for citizen participation within the international political arenas of social media.

To better understand who these online stakeholders might be in the mobile health field, and how they operate in relation to the normative policy and regulatory circuits, we have adopted a social network analysis methodology, in order to track Twitter-based social relationships and debates. Using a series of hashtags, including #mhealth, #mobilehealth and #healthapps to track ongoing policy-related exchanges, we have begun to identify who is influential in these spaces, what they are talking about and how their input to debate may impact on mobile internet regulation.

This paper will outline that SNA approach and highlight some of the procedural and ethical concerns surrounding big data collection and analysis, which are consistent across contemporary digital humanities research. These concerns include how we can use big data harvesting and analysis tools to align quantitative with qualitative methods, how we can justify our research claims via these tools and how we might better understand and implement these innovative research methods within the academy. In particular the paper will interrogate the methodological suggestion that qualitative methods lead quantitative research, considering instead whether a more rigorous approach is to invert the quantitative/qualitative relationship.

The combined conversation around healthapps, mhealth, apps and FDA

Mapping the mobile health policy actors: Who is talking to whom on Twitter, and to what effect?

This is a methodological post on some social network analysis work we are developing for Moving Media. The premise for the SNA research is reasonably simple:

Task: Perform social network analysis around the Twitter conversations about the FDA’s proposed health apps guidelines, posted July 19th 2011:

Public brief: http://www.fda.gov/forconsumers/consumerupdates/ucm263332.htm

Document: http://www.regulations.gov/#!documentDetail;D=FDA-2011-D-0530-0001

Comments and Submissions: http://www.regulations.gov/#!docketBrowser;rpp=25;po=0;dct=PS;D=FDA-2011-D-0530;refD=FDA-2011-D-0530-0001

Aim: To map the dispersed network of actors discussing the FDA policy consultation process in social media channels, visualising their relative influence and communicative relationships.

After some initial Twitter research, we found the #FDAApps hashtag to be the conversation we wanted to analyse. The only drawback is that this conversation seems to be unreachable – the Twitter API didn’t return anything although the conversation is there. Any suggestions on this would be appreciated. Following on from this I did a search across four conversations: #mhealth, #healthapps, #FDA, #apps. It is an experiment in both the methodology and the content.

Here’s the breakdown on the process (and it gets a bit nerdy from here):

1. I tracked four Twitter conversations (#mhealth, #healthapps, #apps & #FDA) and processed the data through the Twitter API, Open Refine and then into Gephi. I imported the .csv file into Open Refine to extract the @replies and the #hashtag conversations – a process of deleting much of the data and producing a .csv file Gephi likes. I then imported the data into Gephi, ran a Force Atlas and Frutcherman Reingold layout and ranked the labels by degree. I then played with the statistics slightly by running a Network Diameter across the network (Average Path length: 1.0508474576271187, Number of shortest paths: 236), which enabled my to colour the labels via their betweenness centrality on a scale of 0 – 6, Eccentricity 0-2 and closeness centrality 0 – 1.5. I then ran a modularity stat across it (Modularity: 0.790, Modularity with resolution: 0.790, Number of Communities: 18). 18 communities!

 

2. I did this for each set of data, that is #mhealth, #mobilehealth, #healthapps, #apps and #FDA. Each process provided a visualisation that demonstrates the key conversation hashtags and the most significant people in those conversations. Here’s the preliminary analysis:

#healthapps conversation

#healthapps conversation

FDA20130528

#FDA conversation

#apps conversation

#apps conversation

#mhealth conversation

#mhealth conversation

3. I then combined the cleaned data of the four conversations together to create a ‘super set’ to understand the broader ecology of the policy discussion around mhealth and health apps.

The combined conversation around healthapps, mhealth, apps and FDA

The combined conversation around healthapps, mhealth, apps and FDA

Preliminary analysis: What we know (and this is my first critical analysis of this process – it could change as I become more aware of what is going on here):

  • The conversation between the FDA and healthapps is stronger than the other two topics due to its location in the network
  • @Vanessa_Cacere is the most prominent twitter user in #apps (she often retweets our tweets too!)
  • @referralIMD is prominent in #mhealth
  • @MaverickNY is prominent in #healthapps
  • The bluer the colour of the actor, the closer they are to the topic – ‘closeness centrality’
  • @Paul_Sonnier [https://twitter.com/Paul_Sonnier] is extremely significant in the overall conversation – ‘betweenness centrality’
  • There are some other probably other significant terms here like #digitalhealth, #breakout, #telehealth, #telemedicine
  • It sucks some CPU processing power
  • The healthapps viz did not work so well, and I’m not sure why.

The limitations as of now:

  • This isn’t the #FDAApps conversation from July 2011 on, this is the mhealth conversation of the 28 May 2013
  • I’m not entirely sure it’s possible to construct an archive from events past – I need to look into this further
  • I think I can code a program that pings the Twitter API automatically every 20 seconds and then automatically adds it to the dataset. If I can build this, we can start tracking data from now on issues/conversations we think are important. I am manually doing this now, but it is really laborious.
  • There is conversations around #apps in general here too. A proper analysis will likely need to clean the raw data further to eliminate any inaccuracies of the representation

Any input on this process would be greatly appreciated and if you have any insights on the findings, please comment below.

Early days with Global Media Policy

The kind folk at Global Media Policy (GMP) have been busy helping us develop our section on their site and we are happy to announce we have our very own space, the Mobile Internet Policy section.

As the research develops in the Moving Media project, we will contribute ‘profiles’ to the GMP site, including People, Organisations and Actors, Policy Documents and Resources to align with the taxonomy of the existing site. The objective of contributing our research findings to the GMP site is the additional profiles will highlight how the governance of mobile media and communication intersects at a global level. For example, after entering three Organisational Actors into the Mobile Internet Policy section, Apple Inc., Nokia Corporation and the Australian Communication and Media Authority (ACMA), we can visualise how these three stakeholders interact with each other AND how they relate to other media and communication governance sections. Currently we have five visualisations available to us: List, Sunburst, Dendrogram, Network and Arc.

There is a great deal of work to develop and a significant amount of data entry required, but we have the early developing stages of a rigorous research collaboration. To start things off, here is the first visualisation we have extracted form the GMP site:

Beginning of the research

Beginning of the research