Last month, I published an analysis of candidates’ public Twitter followers immediately before the General Election. As I showed, Conservative candidates tended to have more public followers than Labour candidates — but once we control for whether candidates had or had not previously been Members of Parliament, we find that Labour candidates tended to have more public followers than Conservative candidates. SNP candidates tended to have more public followers than Conservative or Labour candidates, although that finding was not statistically significant after controls because of the low numbers involved.
This does not, however, address the question of which party’s candidates had more public followers in total — nor the much more subtle question of how many of each party’s candidates’ public followers exclusively followed candidates of that party, and how many also followed candidates of other parties. To answer that question, we need to know who the candidates’ followers were. Fortunately, I do:
Just over half a year ago, the Telegraph carried out an analysis appearing to show that ‘the Labour leader’s shadow cabinet d[id]n’t have as wide a reach as their opposite numbers on Twitter’. This conclusion was arrived at by comparing ministers and shadow ministers whose roles were directly parallel: ‘[Jeremy] Corbyn has more followers than Theresa May, while Diane Abbott saw off Amber Rudd, John McDonnell beat Philip Hammond and Keir Starmer edged out David Davis’, but with regard to the others, ‘the Government enjoyed a clean sweep of the board’ (ibid.).
This is interesting, but I don’t find it satisfactory. The Conservative Party’s best known and most popular politicians were mostly in the cabinet. But while Corbyn himself remains the Labour Party’s biggest social media star, its second- and fourth-most popular MPs on Twitter were and are excluded from the shadow cabinet by virtue of not being Corbyn loyalists, while the third-most popular has technically remained a shadow cabinet member but was excluded from the Telegraph’s analysis by virtue of having no Tory opposite number.
So what happens if we look at the public followers of all prospective parliamentary candidates? This happens. (Figures collected in the week before the General Election for a different purpose and re-used here. Small parties excluded. If you want code, here’s my notebook. Hat tip to Democracy Club for its crowdsourced list of politicians’ social media accounts.)
The findings of wave 13 of the British Election Study are now out. Wave 13 was conducted just after the June 2017 General Election, and analysts all over the country have been crunching the numbers. This is my contribution, and looks at answers to the question, ‘As far as you’re concerned, what is the SINGLE MOST important issue facing the country at the present time?’ This was a free text question, so respondents were able to provide whatever answers they wished, without restriction. What I wanted to find out was whether people of different NRS social grades would express different concerns in their answers to this question. We already know that Labour gained vote share from the Conservatives in more middle class areas and lost it to them in working class areas. Might analysis of those ‘most important issues’ give a hint as to the different priorities of people of different social classes?
I’ll get some analysis of the numbers up before long, but — for now — here’s the chart:
So I am preparing to teach quantitative analysis of social media data using R, the open source language for statistical programming. I usually do anything code-related in Emacs, because I already know how to use Emacs and you can do everything code-related in Emacs and I don’t want to install and learn the quirks of loads of different IDEs. But that argument won’t make sense from the point of view of my students, firstly because they won’t need to do everything code-related, they’ll just need to create R notebooks, and secondly because they don’t already know how to use Emacs, and learning how to use Emacs is hard because Emacs is weird.
The first peer-reviewed journal article arising from the Valuing Electronic Music project has now been published in Cultural Trends as part of a special issue on empirical research into cultural value guest-edited by Dave O’Brien. It focuses on a key finding of the project: even though musicians can now distribute their music for free via the internet, their real-world location remains hugely important. Through qualitative research, we found that electronic musicians in London (a) considered themselves to benefit from being based in that city, and (b) considered a particular part of that city (the highly gentrified, ‘hipsterish’ district of Shoreditch and its immediate surroundings) to be particularly advantageous for less commercial kinds of music. Through quantitative research, we found SoundCloud users based in London to occupy a position at the centre of a network of ‘following’ relationships in which the next best locations appeared to be New York and Los Angeles. Our findings are consistent with the view that the 21st century ‘new media’ produce similar exclusions to the ‘big media’ of the 20th century and do not create anything resembling a level playing field between signed and unsigned artists, provincial and metropolitan scenes, or the developed and the developing world.
In a powerful essay cheekily posted on the website of what may be the UK’s most obsessively corporate university, Suman Gupta bluntly asserts that ‘[t]here is no place for leaders in academia.’ (2015, parag. 1) As he observes, once academics-turned-administrators begin ‘imposing some Great Order… by managing and strategising and propaganda, seeking compliance and exercising opaque executive prerogatives, they start killing off academic work’ (2015, parag. 2). With its recent series of questionable management initiatives, from concentration of resources on bureaucratically-selected ‘strategic research areas’ to development of a (second) free MOOC platform on its paying students’ tab, Gupta’s employer must certainly have provided him with ample opportunity to judge the truth of this proposition. But the relevance of his critique is much wider than a single institution, as we see from the tragic case of Stefan Grimm: a highly successful medical researcher who committed suicide whilst being threatened over his failure to meet arbitrary funding targets (see Parr 2014). While the killing off of scholarly work does not invariably mean the killing off of scholarly workers, it is clear that, across the UK, the term ‘academic leadership’ is ‘now unequivocally taken [to mean] “management of academic workers and institutions from above”’, and those that practise it have come to be ‘regarded as being worth more than academics of any sort.’ (Gupta 2015, parag. 5) In his last words to his colleagues, the late Prof. Grimm put it more forcefully, describing his employing institution in terms that at least some readers of this article may find resonant: as he saw it, it had become ‘a business with very few up in the hierarchy… profiteering and the rest of us… milked for money’, wherein the ‘formidable leaders’ that do the milking ‘treat us like shit.’ (Grimm 2014, parags. 12, 10, 16, reproduced in Parr 2014) It hardly needs pointing out that there has never been an attempt to demonstrate that academic work benefits from ‘leadership’ in the sense described by Gupta and Grimm: top-down control by target-setting, HR-sanctioned procedural bullying, and ‘strategic vision’. The drive for ‘leadership’ is, rather, part of an ideologically motivated investment in management at the expense of labour, clearly seen in the ballooning of executive salaries, both inside and outside educational institutions, during an age of so-called ‘austerity’.
This project began with Pierre Bourdieu’s argument that cultural value is a form of belief. Drawing on Marcel Mauss’s work in the anthropology of religions, Bourdieu (1993 ) argued that a painting or a poem is a sort of fetish: that is, a ‘magical’ artefact whose special status derives from the fact that believers hold it to be magical. So, for Bourdieu, cultural production involves not only the production of artefacts, but also the production of belief in the value of those artefacts. It’s easy to see how this would apply to what Bourdieu called the ‘field of large scale production’, i.e. the commercial culture industries: big businesses such as major record labels and Hollywood film studios invest both in the production of what is now called ‘content’ and in advertising and other forms of publicity through which to generate demand for that content. But what most interested Bourdieu was what he called the ‘field of restricted production’ or the ‘field of art and literature’, which puts little emphasis on the audience, is embarrassed by excessive commercial success, and appears to operate on the principle of ‘art for art’s sake’.
Wanting to find out what was typical SoundCloud behaviour – as opposed to what our case study users were doing – we took a random sample of 150000 SoundCloud accounts earlier this year and downloaded their profile data, plus the profile data of everyone they were following (plus some other stuff, but that’s for another time). One of the things we did with this data was to construct a social network graph showing ‘follow’ relationships at city level: every time our computer program found that a sampled user self-identified with city A followed a user self-identified with city B, it created an ‘arc’ (represented with an arrow) from city A to city B. We then combined all the arcs so that instead of, say, 2000 arcs from city A to city B, there would now be a single arc with a ‘weight’ of 2000. We then imported this data into Gephi, sized the nodes representing cities to reflect the total weight of all the incoming arcs, positioned them with the Force Atlas algorithm, and used the Louvain community detection method to identify ‘clusters’, where a cluster is a group of nodes that are better connected to each other than they are to nodes from outside the group. And here’s the result, with five colours to represent the five clusters.
In my previous post on this topic, I introduced a problem – how to understand the work that explicit genre categorisations are made to do by people uploading tracks to the SoundCloud audio-sharing website – and a potential solution – identifying the three categories most frequently used by each individual in a sample and studying regularities in the ways in which pairs of categories tend to pop up within the same group of three. I also presented some partial and preliminary findings in the form of a matrix comparing co-occurrences of the five genre categories most frequently used by people within an initial sample. And I either glossed over or left unmentioned a slew of problems, some of which we’ve been more successful in addressing than others at present (because these are only blog posts, and we haven’t finished the research yet). The biggest problem is the sample itself: the analysis was done on the basis of a snowball sample, when a random sample would be more appropriate. Hence the provisionality of all this. The analysis will be redone soon on the basis of a sample that will enable us to make more robust claims, but in the meantime I wanted to share our thought processes and working methods with the world because – quite apart from anything else – I’m excited about the patterns that are emerging.