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Disciplinary Babble: Uniting Language Across Disciplines

The S3MC group held our first workshop about social media methodology in February. We had a fantastic group of experts come from all over the country to join us in thinking about how to converge study design across social science and computer science, especially in light of new data sources like social media data.

We learned a lot, but one major takeaway was how hard it is to talk to one another.

Every discipline has its own language. We spend years – sometimes even decades – being instructed and socialized into a particular way of thinking about the world, with a set of vocabulary to match. This mild brain-washing, which to be fair has many benefits, turns out to be very difficult to counteract.

At our meeting, we created a board that was available throughout the two-day workshop, where people added words whose meaning they weren’t sure about.

The S3MC group held our first workshop about social media methodology in February. We had a fantastic group of experts come from all over the country to join us in thinking about how to converge study design across social science and computer science, especially in light of new data sources like social media data.

We called this our glossary, although that is probably a bit too generous, since many of the words have yet to be fully defined. Throughout this process, we identified two main issues that can lead to language confusion.

First, there are words, phrases, acronyms, and terms that some disciplines use and others do not. Some examples of this include algorithmic bias, and endogeneity. When someone uses such a term, people from other disciplines are confused because they don’t understand the term. In this case, however, it is relatively easy to solve the problem. The person who doesn’t understand can simply ask for clarification, or point out that they don’t know the term, and the original person using the term can offer a definition.

Second, and perhaps more complicated, are words and phrases that are used across disciplines but in different ways. Examples of this second type of confusion include active learning, reliability, model, sample, and certainty. In this case, confusion may persist longer because people may not even realize they are using terms in different ways. This can cause conflict when everyone thinks they are on the same page (that is, using the same terms), but are actually thinking about things very differently.

So how do we overcome these challenges and use a common language? To some extent, language confusion may be inherent in developing interdisciplinary or multidisciplinary collaborations. But we do think some approaches make it easier to navigate than others.

  • First, speakers (or writers, or presenters) can be explicit about what they mean. Rather than relying on disciplinary terms to do some of the explanation, speakers can take a little extra time to make sure a concept or description is clear.
  • Second, we think it’s really important to facilitate an environment where saying “I don’t know what that means” or “I’m not sure we’re talking about that idea in the same way” are acceptable statements.

Too often in academia, too much emphasis is placed on being right, and being confident in that right-ness. Fostering an environment where admitting confusion or ignorance is not only acceptable but encouraged as a form of intellectual curiosity, and responded to with respect, can help people to better communicate to one another and overcome these challenges. Interdisciplinary teams need to be open to learning new words and alternative definitions for disciplinary words in order to create a common language for a new team and new ideas.  Something as simple as a glossary board can be a great reminder that we don’t always speak the same language, and that is ok!