Sheffield MA student Nadia Filippi reflects on her experience after 100 hours with the Linguistic DNA team at DHI | Sheffield:


As part of my MA studies in English Language and Linguistics, I had the opportunity to undertake a work placement of 100 hours at the University of Sheffield’s Digital Humanities Institute. The placement offered a good overview on the typical tasks and responsibilities of a researcher and was an excellent choice for me because I am interested in doing research and I am considering going onto PhD research.

When registering for the placement module, I only had basic knowledge of corpus linguistics. I was accustomed to qualitative research but wanted to discover quantitative methodologies and the possibilities that quantitative research can offer. Starting my placement, I was at a stage in my studies in which I was still looking for definite answers to all my questions about research. Moreover, I respected everything to do with numbers, but the idea of actually ‘doing statistics’ made me nervous. I consciously chose a placement to force myself out of my qualitative comfort zone.

My concerns resolved themselves during the placement. I had to familiarise myself with and use statistical software packages like SPSS and lost my initial fear. I began to understand how statistics could be used effectively to discuss questions and find information that qualitative research could not do in timely manner. For example, finding out which words frequently co-occur in a large dataset. Furthermore, I came to understand that doing research does not exclusively mean to narrowly focus on finding a clear answer to an initial research question. It is often more about refining the question, developing another one and accepting that there can be more than one right answer to it.

The power of the Digital Humanities Institute lies in quantitative analysis, engaging with statistical distribution, auditing datasets and computational methods. Yet, there is still qualitative work to do. For instance, I audited and reported on qualities of the YouTube dataset, wrote summaries of previous research and searched for suitable approaches or tools (e.g. a Part-Of-Speech tagger suited to social media data), by consulting published research from similar projects.

A YouTube Convert

It turned out that the placement as a whole, the experiences I made and the tasks I was given shaped my other studies. At the beginning of my placement, the Linguistic DNA team had just started providing support for the Militarization 2.0 project, in collaboration with the University of Leeds. I was immediately drawn-in by this study of YouTube gaming discussion and it ultimately gave me an idea for my MA dissertation.

I had the chance to look through some of the 6.7m YouTube comments gathered by Nick Robinson and his team at the University of Leeds, and think through how they might be analysed for concept modelling.

Screenshot showing comments on Battlefield 1 official trailer, via YouTube (15 May 2017). https://www.youtube.com/watch?v=c7nRTF2SowQ

In exploring the comments, I had to consider the characteristics of commenters’ language and reflect on the research questions. Gaming language, for example, is filled with specialist abbreviations such as “CoD:ww2”, which stands for the game Call of Duty: World at War 2. Information about nationalities (“the Germans”) and militarised language (“disabled”, “destroyed”) may also be key to answering questions about how users’ remarks connect with video content. Close reading of excerpts helps to inform how the Sheffield team respond to the main interests of the mother project Militarization 2.0: if and how social media is militarized and what effect that has on our society and the individual citizens.

By attending meetings, I gained insights into the process and decision-making in a big research project. This included, for example:

  • preparing big data (should we standardise the spelling of the comments or not?)
  • practical obstacles, such as YouTube’s technical limitations (which prevent us from retrieving all the answers to a specific comment)
  • deciding which variables to include (time, author, number of likes)
  • time and scope (how can the resources available be matched to the aims and desired outcomes of a project?)

Knowing the kinds of challenges that such a project can face was helpful in planning my dissertation, which I will be writing over the summer. Prompted by the DHI’s YouTube work, my research will discuss the kind of language generated by exposure to military video game trailers and investigate if there is a difference between the language produced online and offline. In undertaking this research, I will work with my own corpus of YouTube comments as well as with focus groups. The qualitative aspect of my dissertation will allow me to explicitly address and discuss the violence in these game trailers within my focus groups.

Overall, the work placement has been one of the most valuable and enjoyable modules of my MA. I developed many new skills, academically as well as personally. I am more confident about quantitative approaches and numbers, as well as the importance of humanities research as a whole.


Top image shows Sheffield MA student Nadia Filippi at the Linguistic DNA and Militarization 2.0 stand at the 2017 Festival of Arts & Humanities Showcase, Sheffield. The showcase was “a fantastic opportunity to open a dialogue about humanities research and its impact with the public”.

Quantity and quality: lessons from an MA work placement

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