#nsmnss: the story of a network

Updates: Dec 2013: tweetchat on defining #some: Storify | Huma Bird analysis…August 2013: see paper (26 pages, PDF) on developing the network; the section on the community of practice looks particularly interesting

On 23 April the NSMNSS network held a digital debate, the last I think of a series of events before funding runs out in May. I’ve written four posts about #nsmnss, and following the blog and Twitter stream has played a key role in my learning about research methods in relation to social media over the last year – thanks to the team!

The ‘one year on’ presentation gives some insights into the success of the network and its activities. In terms of statistics, there are now 451 fully signed up members (35% non-UK) with 77 in the Methodspace group, and @nsmnss has 1000+ followers (with 900+ tweets).

I particularly liked the way the network played around with the full spectrum of f2f and virtual events (two conferences, four knowledge exchange seminars with around 25 participants each, three online seminars, seven Twitter chats), for example holding tweetchats prior to f2f events. Plus the videos shown at the digital debate were from the previous week’s conference. This hybrid/flipped events model could work well in other fora.

It is hoped to sustain the network after funding runs out – this presumably has the biggest impact on f2f events, but in the era of social media it should be feasible to carry on some activities. A poll is calling for volunteers to get involved in projects, take responsibility for organising Twitter chats, develop resources or deliver training. A test of the strength of the network!

A range of platforms was used – perhaps too many (home page vs blog vs Methodspace anyone?). One way of streamlining activities would be to slim these down and perhaps change the ratio of curation to content – another task which could be done by v0lunteers, assuming the Twitter account is to carry on.

Finally, the dog food question: is any social network analysis or other research planned as part of the network evaluation?

Qualitative methods for social media research

Webinar, 5 February: I tried to watch the webinar twice, but no luck- I’ve used Elluminate several times before, so maybe it’s a Java issue – I’ve had no end of problems since I upgraded last week. There’s a discussion page on Methodspace, but you can call me qualitatively out for now!

Event, 28 January: suspect there was no wifi at the venue, as no tweets were made during the session. A report is now available, dated 13 Feb.

Following the #nsmnss chat and event on quantitative methods came a chat on qualitative methods on 20 November, with an event scheduled for 28 January – see Deep data: digging into social media and the Tunisian Revolution case study for an intro to the issues.

Below are my notes paraphrased from the chat.

What are the biggest methodological challenges when using qualitative methods in social media research?

  • issues relating to public and private space; ethics;  both for observation and use of posts
  • ethics panels need to be educated about the real risks – difficult to just apply F2F ethics to online; there are unique and untested ethical considerations in socmed research
  • generalising with small (but rich) sample data; the volume of data
  • following people across multiple platforms; does this create potential ethical issues? depends on consent
  • what research questions are best served by social media; theoretical frameworks
  • negotiating your online identity as a researcher; how you become seen as a professional in online spaces
  • social media lets us study circulation, but ideas circulate in other places, too; ie life online is part of life offline – > no easy distinction between on and off line research? can we limit just to online? (depends on the research qu)
  • the digital divide – need to acknowledge when sampling OR can get to hard to reach groups, different access to populations
  • qualitative research lends itself very well to socmed research

Are social media affecting the way we do qual research, and how?

  • can study globally with diverse participants and little money!
  • allows geotagging of data
  • unprecedented size and depth of datasets updated in real time
  • re-interpretation of qual methodologies and methods – chance to be a pioneer!
  • options for using visual, verbal, textual data to create rich stories
  • research tasks are more fragmented, requiring more organisation and integration skills
  •  type of data influences design + analysis; advantage: rich + interesting, disadvantage: more steps to analysis

Does qualitative research using online social platforms change the relationship we have as researchers with participants? How?

  • depends on design –
  • analysing content rather than observing real time interaction has fewer proximity risks
  • if interviewing online lack of social cues is key (but with video etc can grasp many cues and non-verbals)
  • blurs boundarie s- to be recognised in a community u need to be part of it
  • similarities to participant observation issues in offline research
  • difficult to research a community you are part of, but presence not so obvious
  • ethics – how we manage dynamics, harm, disclosure etc when not in the same physical space
  • the more you open up online the greater the return – really interesting connections are made that wouldn’t happen with more distance
  • the ‘participant observer’ – like anthropologists, risk of going native?
  • can online create new cues? ways of typing, acronyms, etc?  new/replacement cues needed, but not the same amount of info and there can be cultural issues; need common meanings
  • for people with disabilities, sometimes online means MORE cues and better communication
  • email/text are great for individuals who are non-verbal or need more time to formulate responses

What tools do you use for analysis online qual data? Do existing tools work or is there a need for new tools?

  • QSR’s NCapture, works with NVivo 10 to capture and analyse tweets, activity on Facebook pages and in LinkedIn groups
  • for the statistically inclined – TwitteR

Quantitative methods for social media research

Update: see coverage of the CCC Symposium in Copenhagen on 16 October for lots more on the perils of big data and quant methods.

The second round of #nsmnss activities took the form of a tweetchat (24 September) followed by a knowledge exchange event (26 September). Topics covered: data visualisation, populations and sampling, and big data. Notes below mainly paraphrased from Twitter. Here’s some videos too (posted 25 Jan 2013), and there’s a webinar on 8 April which I am sitting out.

Are we doing more than data mining when we analyse social media data? Which research questions is it best able to answer? What are the biggest methodological challenges when working with quantitative social media data?

  • Compared to surveys, social media data are conversations rather than responses to standardised questions.
  • Overall quality of data – social media is an expressive medium.

Social media data can often be analysed using visual methods. How can we visualise data collected by social media? How does visualisation relate to statistical analysis? What are the payoffs from using visualisations?

  • We can see patterns not visible in a table, for example can compare categories of data by visualising the size of each block of data- see Information is Beautiful.
  • Tools can explore data interactively – see Interactive VisualizationsGapminder and Hans Rosling’s TED Talk – and track how users explore data.
  • Visualization for storytelling and illustration, or visualization for prediction? Visualization to answer research questions.
  • “Often beautiful, sometimes helpful.”  Dataviz may be adding impact and getting statistics to a wider audience, but is it adding anything methodologically? Visualisations can be misleading – remember the real purpose of the data.

What is the ‘population’ on social media platforms? How do platforms differ in population characteristics? How can we select cases or samples on social media? Is it possible to get a statistically representative sample using from social media platforms? Does it matter?

  • Sampling goal: to make statements about a certain group of people or objects (webpages, tweets etc)
  • Sampling frame: list of every object in population. Internet: special issues, hard to find list of all blogs, pages etc, meaning that bias is common.
  • Huge problem with sampling – lack of demographic info on most people’s profiles makes this even harder.
  • Advantages of sampling with the Internet – cheap, fast turnaround, no interviewer effects.
  • Disadvantages – many have no or limited Internet access, so cannot generalise findings. Population characteristics unknown, meaning of behaviour may be unknown.
  • Social media users are only representative of social media users, not of any larger group – see Sampling and social media and Tortoise or the hare: social media sampling.

Social media research can involve very large datasets. What do we gain and lose with big data? How is big data changing the way we do research?

  • Are we analysing big data because it’s available rather than because it is suitable to answer our research questions? Bigger does not necessarily means more useful.
  • Availability of big data precedes availability of suitable quant methods for analysing complex data structures – we need to understand the structure before we can analyse it.
  • cases: Waller’s study of Australian Google users | text analysis using Google books, studies by Michel et al and Heuser and Le-Khac
  •  Twitter as social network or news/broadcast medium? Example: Kwak’s study of 1.47 billion social relations.
  • Is retweeting a social relationship? Claims based on big data network graphs, eg w influences x,y & z  because w retweets them. Does an RT mean you’ve influenced someone?

Ethics in social media research: #nsmnss tweetchat

Update, March 2014: more from NSMNSS on ethics: NatCen research and findings, Janet Salmon’s research, tweetchat held 11 March. See NSMSNSS’ thinking ethically questionnaire for a useful overview of the issues.

On 17 July NSMNSS held a tweetchat on ethics in social media research, prior to a f2f knowledge exchange event on 24 July (programme).

Two pre-chat blog posts announced the tweetchat and how to join. Nice idea, using virtual prior to a f2f event. I downloaded and scanned the +/- 186 tweets during the chat – some interesting stuff there, if disjointed. Transcript uploaded the next morning, with the advice to “scroll down to the bottom of this post and read back up to follow the debate”…hmm it’s not that hard to re-order tweets in a spreadsheet. And how about a light edit, with collated responses to the questions, social tweets removed and thrown into Storify?

It would be interesting to analyse a tweetchat – what are the ethics there then?

The debate can be taken forward on the NSMNSS Methodspace presence, maybe here.

Questions for discussion at the 24 July event:

  • Understanding our digital identities: What is the ‘case’ in social media research? Do our digital identities and behaviours vary to our offline identities? How does this affect the ethics of social media research?
  • Different platforms? Different ethics? What about the ethics of the platform providers? Do researchers have different ethical responsibilities on different platforms?
  • Public and private data – Are public profiles available for research? What ethical responsibilities do we have?
  • Drawing together key messages for ethical guidelines: Exploring existing frameworks, identifying gaps and additions.

For more see Farida Vis on ethics (video | slides) from the first #nsmnss event and a post on ethical issues in conducting research in online communities (published 22 July). Plus, from a non-academic perspective, Storyful on the ethics of livetweeting overheard conversations.