Friday, 2 December 2016

On Social Media Analytics

Phillip Brooker is a research associate at the University of Bath (UK) working in social media analytics, with a particular interest in the exploration of research methodologies to support the emerging field. Phillip is a member of the Chorus team; a Twitter data collection and visualisation suite ( He currently works on CuRAtOR (Challenging online feaR And OtheRing), an interdisciplinary project focusing on how “cultures of fear” are propagated through online “othering”. @Chorus_Team

NSMNSS events have always been good value for me. I haven't quite been a part of the network since it kicked off, but I certainly have tried to be an 'active member' for the years that I have been involved with it. So when Curtis Jessop emailed me to ask if I'd give a talk on the practicalities of using Chorus to do social media analytics research, I jumped on it. Moreso than telling people about our software and what we've used it to do, these events are always the perfect chance to hear about innovative current research in the field. I won't go through my talk in too much detail here since I generally try not to be too reductive about how Chorus might be used in social research. Best to download it, watch the tutorial video, read the manual and then play about with it yourself (all of which you can do at :::PLUG ALERT!::: Suffice to say that my talk aimed to run through the basic features and functions of Chorus as a free tool for collecting and (visually) analysing Twitter data. This included a demonstration of the two different data collection modes – the more familiar query keyword search which you can use to look for hashtags and so on, as well as our native user following data collection function which lets you capture sets of user’s Twitter timelines. And from there, I ran through the different ways of visualising data within Chorus – in brief, the timeline explorer which provides a variety of metrics (e.g. tweet volume, percentage of tweets with URLs, positive and negative sentiment, novelty and homogeneity of topic) as they change across time, and the cluster explorer which produces a topical map of the entire dataset based on the frequency with which co-occur with one another. The aim here was to show how Chorus might be used by researchers to answer lots of different types of research question, both as a full all-in-one package, but also in a more exploratory way if users want to quickly dig into some data for a pilot study or similar – readers especially interested in what Chorus might offer might find one of our recent methods papers useful (available at:

However, what I want to comment more pointedly on in this blog is the NSMNSS event itself, because to me it marks something of a turning point in social media analytics, where it's finally becoming very clear just how distinctive we've made (and are continuing to make) the field. There seems to have always been this worry that working with digital data runs the risk of turning the social sciences into unthinking automata for blindly spotting patterns – the supposed ‘coming crisis of empirical sociology’ referred to by Savage and Burrows in 2007. And that characterisation has not really disappeared, despite social media analysts natural objections to it as a way of representing our work. Thus far, social media analytics has (arguably) necessarily had to progress in a way that directly references those concerns – researchers have made it their explicit business to show, through both conceptual and empirical studies, that there is more to social media data than correlations. However, at this most recent NSMNSS event I got the sense, very subtly, that something different was happening. As a community, we seem to be moving past that initial (and I reiterate, very necessary!) reaction into a second phase where we’re beginning to be more comfortable in our own skin. We’re now no longer encumbered by the idea of social media analytics as “not data science”, and we’re seeing it recognised more widely as a thing in and of itself. As I say, it might seem a subtle distinction, but to me it suggests that finally we’re finding our feet!

Of course, this doesn’t mean we have neatly concluded any of the long-standing or current arguments about the fundamental precepts of the field – my background in ethnomethodology and ordinary language philosophy gives me a lot to say about the recent incorporation of ideas from Science and Technology Studies into social media analytics, for instance. But nonetheless, for me, this event has demonstrated the positive and progressive moves the field seems to be making as a whole. We already knew it of course, but it’s clearer than ever that there are very interesting times ahead for social media analytics!

Wednesday, 30 November 2016

Democratising Access to Social Media Data – the Collaborative Online Social Media ObServatory (COSMOS)

Luke Sloan is a Senior Lecturer in Quantitative Methods and Deputy Director of the Social Data Science Lab at the School of Social Sciences, Cardiff University, UK. Luke has worked on a range of projects investigating the use of Twitter data for understanding social phenomena covering topics such as election prediction, tracking (mis)information propagation during food scares and ‘crime-sensing’. His research focuses on the development of demographic proxies for Twitter data to further understand who uses the platform and increase the utility of such data for the social sciences. He sits as an expert member on the Social Media Analytics Review and Information Group (SMARIG) which brings together academics and government agencies. @DrLukeSloan

The vast amount of data generated on social media platforms such as Twitter provide a rich seam of information for social scientist on opinions, attitudes, reactions, interactions, networks and behaviour that was hitherto unreachable through traditional methods of data collection. The naturally-occurring user-generated nature of the data offers different insights to the social world than that collected explicitly for the purposes of research, thus social media data augments our existing methodological toolkit and allows us to tackle new and exciting research problems.

However, to make the most of a new opportunity we need to learn how the tool works. What does Twitter data look like? How is it generated? How do we access it? How can it be visualised? The bottom line is that, because social media data is so different to anything we have encountered before, it’s hard to understand how it can be collated and used.

That’s where COSMOS comes in. The Collaborative Social Media ObServatory (COSMOS) is a free piece of software that has been designed and built by an interdisciplinary team of social and computer scientists. It provides a simple and visual interface through which users can set up their own Twitter data collections based on random samples or key words and plot this data in maps, as networks or through other visual representations such as word clouds and frequency graphs. COSMOS allows you to play with the data, selecting subsets (such as male and female users) and seeing how they differ in their use of language, sentiment or network interactions. It directly interrogates the ONSAPI and draws in key areas statistics from the 2011 Census, allowing you to investigate the relationship between, for example, population characteristics (Census) and anti-immigrant sentiment by locale (Twitter). Any social media data collected through COSMOS can then be exported in a variety of formats for further analysis in other packages such as SPSS, STATA, R and Gephi.

COSMOS is free to anybody working in academia, government or the third sector – simply go to and click on the ‘Software’ tab on the top menu bar to request access and view our tutorial videos.

Give it a go and see what you can discover!