Wednesday, 22 July 2015

Research at a click away: The inter-relational ethics of a connective methodology

Josh Jarrett is a PhD researcher as the Digital Cultures Research Centre, UWE Bristol. Josh’s research utilises an online ethnographic methodology to look the role of online play in the co-creative practices online games. Josh tweets at @Joshua_Jarrett and blogs about his research at, where this blog was originally posted.

 On Wednesday 20th May I had the pleasure to give a short overview of my research, its online methodology and some of the ethical grey areas at a University of the West of England event called ‘Ethics, Digital Data and Research using Social Media’. In this post I want to recall some of the points that were touched upon during the day, give an insight into my own online research and delve into exactly what some of the ethical grey areas are for online researchers.

MOBAs? A brief introduction to playful co-creativity

 Before I talk about specifics I should say a little more about myself and my research. I am a PhD researcher in my third year of research into the themes of online play and collaboration with the Digital Cultures Research Centre at the University of the West of England. More specifically, my research is interested in the ways online players creatively play in games and the way these acts of playful creativity carry ramifications far beyond the immediate actions of the player. For example if a player innovates a new play style in a game that had previously not been tried and this new style proves to be especially effective, how does this act of creativity carry further consequences to other stakeholders of the play space? Although it may seem like nothing particularly new for online games or acts of play more widely it is within these dynamics that one of the most played online games genres, the Multiplayer Online Battle Arena (MOBA), is underpinned. Known for games such as League of Legends or Dota 2, the MOBA genre has come to define the games and digital landscape in significant ways through popularising live streaming on sites such as, giving rise to a thriving worldwide electronic-sports industry and introducing new models of fair ‘free to play’ allowing vast numbers of players to play. All of these trends have meant the MOBA space is one laden in different stakeholders, from its millions of daily players, its worldwide network of professional players who make a living off playing and its developers who ultimately seek to monetise all of this activity. My research looks at the role of online play in co-creating this genre and more critically, how the differing power dynamics between stakeholders affects their respective values in the play space. For this post I want to leave all of that to the side however and talk a bit about how I go about ethnographically grasping this vast and dynamic online culture.

A connective Reddit methodology  

 My methodology consists of three distinct strands of data collection. These are auto-ethnographic in-game experiences, (professional) player interviews and open Reddit discussions. All of these research methods take place online and what is interesting is how they all share a connected relationship. Although my main source of data collection is open discussions on the news and social networking site Reddit (which is the focus of this post), it is vital to consider the methodology as a whole.

 The auto-ethnographic element to this research is similar to many studies of games, an essential element that informs nearly all of the work. As a research method auto-ethnography has been employed in numerous online games (Boellstorff et al, 2012) and it informs the questions posed in wider dialogues with players in Reddit spaces. However the auto-ethnography also serves as a window into more than just knowledge of the game and its culture but it also serves as a mode of authenticity when posing questions to players.

 A recurring theme throughout the day was how different online platforms share a connected relationship and can often bypass the privacy settings of one through the other, for example in case of Storify and Twitter that Kandy Woodfield touched upon. I find this example pertinent to the context of my own research as it taps into what Dijck (2013) has termed the ‘connective’ context of social media, which is to say the interrelated and ecological relationship different platforms share with each other online. Storify for example, is a site that heavily relies upon Twitter feeds to construct its content and in doing so the ethics of how to use Twitter must also consider Storify’s interrelation. In my own research this ‘connective’ context is one that is heavily woven into everything I do. By a large margin the open discussion format in Reddit is my main mode of data collection however it is largely enabled through my wider auto-ethnographic experience.

 When opening up a discussion with participants upon Reddit I am wholly transparent about my status as a researcher and always link my blog and state I am open to questions myself. In addition, I also introduce myself as a player through linking my in-game profile and stating an example of what I am talking about from my own experience. For players who are in a space to talk with other players and not expecting questions posed by an academic researcher this further introduction of myself as a fellow player is significant and opens up a much more casual, intuitive, and insightful response to my questions than it would if the questions were strictly formal. The status of the researcher as a player is especially important here as Reddit’s format can be extremely resistant to researchers due to the architecture of the platform.

 Reddit is similar to a forum in many ways and it works through the same persistent threads of conversation often heavy in memes and external links. The vital difference between Reddit and a forum is its system of up-voting and down-voting posts that leads to what many have dubbed a ‘Hive Mind’ whereby only certain types of posts are up-voted and therefore visible for the vast majority of users. In practice this means getting attention to a research question posed on Reddit can be difficult if, for example, people perceive your reasons for being in the space as not similar to theirs. In my experience of making threads in the /r/leagueoflegends sub-Reddit that has thousands of active users at any given time, attention to these dynamics has proven essential.  If a post does not get up-votes quickly it will sink and essentially vanish from users; so introductions are important! A connective sense of authenticity (I.E. auto-ethnographically playing the game as well as externally researching it) is one way of gaining traction here and it’s this kind of wider attention to online platforms that is an essential consideration throughout my online methodology.

Connective ethics

 Similar to the Twitter / Storify example touched upon above, a space such as Reddit must be considered in relation to other platforms when considering the ethical implications of the research. If for example, you state not to use the name of participants as a measure of protection against their identity (even avoiding use of their pseudonyms) but you quote their responses from an open discussion, it is very easy for a search engine such as Google to identify the quote and take you to the page where the discussion happened. Throughout the ethics in social media research day similar examples of these interrelations between platforms undermining ethical standards were touched upon and just as the relationship between Reddit and Google is here problematic, so too are numerous other examples. Potential solutions to this particular ethical concern included closing threads after conversations end and paraphrasing quotes (as is often done when working with children) from participants to avoid search engine detection, however there is no clear or effective answer here.

As with many other researchers working with online participants, I do not have an answer to unravelling a universal code of ethical standards here. The interrelations between online platforms are constantly changing as they respond to a variety of socio-technical developments and as a researcher, the only thing I can say with any certainty is that attention to these dynamics is essential. A particularly connective methodology such as my own that I have touched upon here points towards the opportunities inherent in online research as much as it sketches out potential ethical grey areas. There might not be any clear or definitive answer when approaching these grey areas but nonetheless they need to be sketched out and carefully considered. Research at a click away is exciting, insightful and potentially rich as much as it can be problematic.


Boellstorff, T. Nardi, B. Pearce, C. and Taylor, T, L. (2012) Ethnography and Virtual Worlds: A Handbook of Method, Oxford: Princeton University Press.
Dijck, V, J. (2013) Culture of Connectivity: A Critical History of Social Media, New York: Oxford University Press.

Monday, 6 July 2015

Why is there so much research on Twitter? And what does this mean for our methods?

Wasim Ahmed is a PhD candidate at the University of Sheffield and the #NSMNSS Twitter Manager. This blog post was orignally posted on his research blog. You can find him on Twitter at @was3210
I was asked on Twitter by a fellow PhD student what tools and methods there were of capturing and analysing data from Facebook, and although I was able to find a few, there were far more Twitter data capture tools. I also noticed that there are very few tools that can be used to obtain data from other social media platforms such as, Pinterest, Goolge+, Tumblr, Instagram, Flickr, Vine, and Amazon among others. This led me to wonder whether it was tool availability, or some other reason for why there is more research on Twitter, compared to other social media platforms.
I then asked the following question on Twitter:
Why is there so much research on Twitter? Is it because it’s difficult to get data from other platforms? Or is Twitter a special platform?
I received a range of responses:
  1. Twitter is a popular platform in terms of the media attention it receives and therefore it attracts more research due to this cultural status
  2. Twitter makes it easier to find and follow conversations which consequently makes it easier to research
  3. Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data
  4. Twitter data is easy to retrieve as major incidents, news stories and events on Twitter are normally centered around a hashtag
  5. The Twitter API is more open and accessible compared to other social media platforms, which makes Twitter more favorable to developers creating tools to access data. This consequently increases the availability of tools to researchers.
It is probable that a combination of response 1 to 6 have led to more research on Twitter. However, this raises another distinct but closely related question: when research is focused so heavily on Twitter, what (if any) are the implications of this on our methods?
The methods that are currently used in analysing Twitter data i.e., sentiment analysis, time series analysis (examining peaks in tweets), network analysis etc., can these be applied to other platforms or are different tools, methods and techniques required?
I have used the following four methods in analysing Twitter data for the purposes of my PhD, below I consider whether these would work for other platforms:
  1. Sentiment analysis works well with Twitter data, as tweets are consistent in length (i.e., <= 140) would sentiment analysis work well with, for example Facebook data where posts may be longer?
  2. Time series analysis is normally used when examining tweets overtime to see when a peak of tweets may occur, would examining time stamps in Facebook posts, or Instagram posts, for example, produce the same results? Or is this only a viable method because of the real-time nature of Twitter data?
  3. Network analysis is used to visualize the connections between people and to better understand the structure of the conversation. Would this work as well on other platforms whereby users may not be connected to each other i.e., public Facebook pages, or images from Instagram?
  4. Machine learning methods may work well with Twitter data due to the length of tweets (i.e., <= 140) but would these work for longer posts and posts (i.e., Instagram) where images may be present?
It may well be that at least some of these methods can be applied to other platforms, however they may not be the best methods, and may require the formulation of new methods, techniques, and tools. On the tool front, I would like to see more software for those in the social sciences to obtain data for a range of platforms and including a range of data i.e., web links, images, and video. At the Masters and PhD level there should be more emphasis on training for social science students in effectively using existing software that can be used to capture data analyse data from social media platforms.