The Hidden Conference Cost of doing Interdisciplinary Work

Hello blog!

Long time no chat. May was entirely lost in the black hole that is the end of the semester and the start of “academic conferencing.” In the past month, I attended the International Communication Association’s conference (ICA 2019; what I would consider the “main” conference of my primary field, Mass Communication) and a workshop at the the North American Chapter of the Association for Computational Linguistics conference (NAACL NLP+CSS 2019). I have a nice break through the remainder of June and July, and then in August I have one more conference (Association in Education for Journalism and Mass Communication, AEJMC 2019).

Which brings me to my topic of the day: the cost of attending conferences to stay up to date on interdisciplinary scholarship.

Realistically, I work in three intersecting fields (four, if you include my computational stuff separately): Mass Communication, Political Science, and Linguistics. Removing a component of the trifecta is not possible; it would mean fundamentally misunderstanding my research agenda.

There are a lot of benefits and problems to doing interdisciplinary research, which many other scholars have spoken on. I love interdisciplinary work, personally, because that’s where all the enjoyable little questions are. And, as valuable as specialization can be, most research questions can be studied in many ways, depending on the department/discipline you end up in. A question about political language may produce different results if studied in Sociology, Psychology, and Political Science. So, to me, the rigorous thing would be to do interdisciplinary research—to be specific in your question, broad in where you look for theory, and concrete in your study’s operationalization and methodology.

But there are substantial professional costs to doing interdisciplinary work. A Google Scholar search of “interdisciplinary research difficulties” will yield more than enough articles to give you a sense of how much the academy has struggled to deal with interdisciplinary scholars (I choose the word “deal” carefully… rarely do I feel as if the academy “supports” interdisciplinary work).

One of those weirdly silent struggles is the cost of attending oh-so-many conferences. In an ideal world, I’d like to submit to conferences for all the fields I participate in (ICA/AEJMC for Mass Comm, LSA for Linguistics, APSA/MPSA for Political Science, NAACL/CoLing for Computational Linguistics). There conferences are important for many reasons. They help you connect with others to find jobs (a super important thing for any graduate student), they expose you to the latest studies and results in the field, and they help you connect with other people who are doing similar work to you.

But each conference can cost a substantial amount of money to attend. Below are the registration cost of the seven conferences I noted above, and a few others:

Conference 2019 Location Regular Reg Student Reg
AEJMC Toronto $ 215 $ 125
APSA Washington D.C. $ 160 $ 125
CoLing Santa Fe $ 715 $ 500
ICA Washington D.C. $ 300* $ 165
IC2S2 Amsterdam 345 € 195 €
ICCSS Amsterdam 450 € 350 €
LSA NYC $ 86 $ 90
NAACL Minneapolis $ 595 $ 295

(* ICA has tiered prices depending on where your institution is located. These are U.S. prices, Tier A.)

For each conference, you also need to account for hotel and airfare, at minimum. The best conferences are the ones that are proximity close (the location of NAACL, in Minneapolis, was a huge reason why I submitted a paper to begin with), but you are typically looking at between 300 and 500 dollars for a round-trip flight to somewhere-in-the-U.S. (aka: Chicago or DC). Conference hotels usually charge between 175 and 250 per night (graduate students bring down the cost substantially by staying with other graduate students). If you are a lucky young scholar like I am, you will have tt professors who will assist with food and drink for a good portion of the trip, but this is obviously not always the case.

All in all, you can be spending somewhere between 500 and 1000 dollars for each conference you attend. This cost increases considerably for non-(U.S. and European) scholars, who have to not only fly in from another country ($$$ international flights anyone?!) but also apply for visas, an increasingly daunting task (most of my conferences are in the U.S., which makes me double-privileged as a scholar in the States).

If you’re a scholar working in two disciplines, that’s twice the conferences you may need to pay for. Or, you’ll have to sacrifice attending certain conferences in one year to attend another. For a young scholar, particularly one doing interdisciplinary research, not attending a conference means missed opportunities to meet people, connect about research, and find future avenues of collaboration.

Given this, we need to start thinking about the conference model, and how that limits young scholars who cannot normally afford to attend so many conferences. Alternative ways to participate, cheaper locations (and cheaper hotels), and having more included in a registration can go a long way.

Attending the R Forwards Women's Package Workshop (Hosted by R-Ladies Chicago)

This weekend, I had the pleasure of attending an R Forwards Women's Package Workshop. It was hosted and run by members of R-Ladies Chicago: Angela Li and Stephanie Kirmer.

Though I have attended and run many one or two hour workshops, this was my first long-day, single-topic workshop (9:30 to 4:00 pm) and I thoroughly enjoyed the experience! It’s definitely a format that would be useful to teach more complex topics, like software development and package building. There was a lot of info-in-brain-cramming, but I also felt like I learned a ton in a very short time span.

Attendees of the 2019 R Forward Package Workshop, taught by Angela and Stephanie of R Ladies Chicago!

Attendees of the 2019 R Forward Package Workshop, taught by Angela and Stephanie of R Ladies Chicago!

The session was broken down into a couple broad topics: package development, git+r, unit testing, documentation, and package sharing (e.g., licences, indicating dependencies, CRAN). This made the material useful for both specific-use packages (e.g., building a data wrangling package for my specific research group) and for more public-facing packages (e.g., a package that one would want to upload to CRAN).

Top 5 Things I Learned:

  1. The usethis package is so convenient and important for package development. For example, the function


    will create the skeleton of the package files for you, including folders for R-code, the “man” folder (“man” stands for manual), and description/namespace files. This makes package building so much earlier! You can learn more about it in Wickham’s R package book.

  2. Using the “::” operator allows you to see the exported variables or functions in a package namespace. But if you really want to see under the hood “:::” allows you to see everything (there’s some more about it on StackOverflow), including the functions that are not publicly exported.

  3. Semantic Versioning - How have I only learned about this now, despite attending several data carpentry workshops and classes?! I am such a stickler for version recording, even in my non-computational work (I have been subconsciously semantic versioning my human content analysis codebooks), and it’s so nice to finally have a specific phrase associated with this process.

  4. A quote from my favorite slide of the day: “If the first line of your #rstats script is


    I will come into your lab and SET YOUR COMPUTER ON FIRE 🔥.”

    Confession time: I do this a lot! 🙈 ::embarrassed:: In terms of workflow, I am generally quite sloppy about separating projects and keeping relative paths. I first read about projects in R for Data Science, but I never took the lesson to heart until this weekend. I know… it’s bad given how much I code. So I guess I’ll be “konmari-ing” my R code this semester(i.e., create an Rproject space for each of my projects)!

  5. A great tip from Stephanie’s top tweet (about Git): “when you screw up a git merge, you can use git reset --hard master@{"300 minutes ago"} with any time quantity you want in there to get back to where things were a period of time ago.“

Extra Bonus: Differencing vs. Logging Time Series Data

I happened to also sit next to economist Dweepobotee Brahma, which was a great coincidence since I’ve been binging time series models and papers for the past year. I happened to randomly gripe about how economic data is often processed (i.e., logged). She was kind enough to explain to me why economists did this, and why growth rates are so important to the research in economics and econometrics. Having been taught by a political scientist, whose questions are not as focused on exponential relationships, I didn’t know much about this alternative treatment/perspective on time series data, and it was really interesting!

I’m starting to wonder if this is especially important to modeling follower growth. Nearly all follower count time series I’ve analyzed have been fully integrated I(1), if not to I(2). Often I will first- or second- difference this as a way to make the series stationary. However, I’m realizing a logarithmic transformation is probably more appropriate for what we try to measure (implicitly, it’s a growth rate question).


Overall, I’m really, really glad that I was able to attend this workshop. It’s definitely up there on my “favorite R workshops ever” list. Organizationally, there were a lot of little things I wanted to take back to my workshop strategy (for example, this was the first workshop I attended where we used post it’s to indicate whether we needed help with specific tasks) and obviously it was great for advancing my R skills.

One of the most important “big picture” lessons I learned was that if I want to actually do software development in R (or any programming language), I have to be more organized about my code. I am organized when it comes to data management, but am definitely less-so with my scripts and functions. Workflow wise, I want to get on top of this by the end of the semester.

I’m also one step closer to completing a major R-new-year’s-resolution: Build an R package! I have a couple of functions that I rely on for data wrangling operationalized text data to time series data, so I’m eager to wrap them all up in a neat little package for future use.

And finally, attending the workshop was a great reminder about how amazing the R community is, both offline and online. That’s one of my favorite parts about being an R programmer—the community makes it easy to be excited about learning R.

I am so grateful to Angela and Stephanie for hosting this amazing workshop, and to R Forwards for sponsoring my attendance. If you are interested in checking out the materials from this weekend, they have made the workshop material available here.

August: The Month of Travel!

It's been quite some time since I've done a lot of travel, but my August will be all about trips, trips, trips!

In the beginning of the month, I will be attending sixth AEJMC in Washington D.C. (Aug 6-9)  to present my work on news coverage with tweets written by Russian IRA accounts. I'm looking forward to presenting my work, and taking some time to visit the city!

Then, it's off to NYC for Restaurant Week and to celebrate a friend's birthday!

Back to Madison briefly, to get ready for a family trip to Europe with John! Our Europe trip includes stops in Keflevik, Iceland; Paris, France; and Amsterdam, the Netherlands. After several years of travel-less-ness, my wanderlust is really itching.

Here's to a month of good adventures, new ideas, and an ever-expanding universe! - Jo