Of course, as linguists, we have our ideas about the value of Hansard. We regularly use academic research methods to study big textual datasets like Hansard. Simply expressed, we use software to look at complex patterning in the text, and obtain quantitative data about those patterns. Researchers then use relevant contextual information to interpret the findings and draw conclusions about the textual choices in the dataset. Underlying Hansard at Huddersfield’s goals is a conviction that such research methods could also benefit non-linguists. We have our own ideas about the benefits, but is this what actual non-linguist users might be looking for? That is why engagement with the potential end-users of our Hansard at Huddersfield interface is of utmost importance to us. We hope this blog will give you an insight in how we are trying to do that.
Who do we engage with? By publicising our project on social media, and by emailing organisations we think might benefit from using Hansard, we aim to converse with two kinds of end-users: those who already use Hansard, and those who are yet to discover its usefulness. For those already familiar with Hansard, we intend to increase the benefit of Hansard for their research. At the same time, we would love to present a tool that attracts those unaware about Hansard to make use of it. To accomplish either goal, we need to know what people would like to find out Hansard.
Social media, face-to-face meetings, questionnaires, Skype and telephone conversations have so far been means of interaction with end-users. We engage with some of them collectively during our end-user meetings, and others we ask individually. The most productive means by which we have gathered suggestions so far was our round of end-user meetings in July 2018, attended by 20 individuals, where we presented some ideas about how an adapted version of our linguistic methods might be relevant to their aims. Our end-users discussed how they would like to use the Hansard dataset and how they felt our Hansard interface might be able to help them.
It emerged from those discussions that contextual information for the dataset would be of importance for a maximally interpretable dataset. That information could then for example be used for comparing contributions by two particular MPs, or perhaps two parties.
As we anticipated, the raw quantitative data produced by linguistic software proved inaccessible to non-expert users, demanding too much knowledge of linguistics and statistics for easy interpretation. Solving that problem by presenting suitable, interactive visualisations of the data was welcomed. Because the visualisations change according to changes in the variables studied, they provide a wealth of information in an accessible way. Interactive visualisations promise to be less overwhelming, better interpretable, and of course more appealing.
While we are still looking for suggestions like the ones above, our next round of meetings and individual interactions with end-users will explore how well our end-users think our interface responds to what they want to do with Hansard, and whether they need help with understanding and interpreting the visualisations. We are happy for you to get in touch to help us out (hansard@hud.ac.uk)!