More on AI Literature Reviews in the Social Sciences.

Recently I needed to throw together an overview of the literature for a specific topic. The application was, surprisingly, non-scientific. People who work in the actual world of politics asked me what the literature says about a certain topic. This gave me another opportunity to test AI tools and their capabilities, especially since the topic was not something I was neck-deep in. Simply speaking, I would have had to do a literature discovery from scratch using something like my classic literature review technique if it wasn’t for AI.

If you haven’t read my old piece on AI literature reviews, it mostly still stands. Start there. Not here.

To make a long story short, after trying many things that worked very poorly, (lots of inaccuracies and hallucinations), I finally cracked it and found a fairly good way of doing this. The downside is that it is incredibly work-intensive. Maybe not as work-intensive as doing the classic literature review from scratch, but it really pushed the boundaries of what is possible with AI today and what is not. All in all, I had a lot of fun doing it once I got passed the excruciatingly long and painful preparatory phase.

I tried all the tools in my old piece and then some to try to automate this literature review process. Unfortunately, even scite_ is becoming less and less capable of social science research as they put up more and more guardrails. Its writing is shittier and shittier. Article discovery is, unfortunately, quite poor as (at least without tweaking) it does not find the most relevant literature. It’s still the best thing for discovery but what it writes up is inadequate. (It is good enough for the natural sciences, but not for social science.) I also tried more creative approaches but all of my attempts lead to the AI model just making shit up.

But I recently heard on a podcast, one of the three early April 2024 episodes of The Ezra Klein Show that current models can keep around a book’s worth of information “in mind” while chatting. And since I now have a Claude AI subscription I figured I should put this to the test. I grabbed a whole year of a relevant topical journal. I figured I should try to chat with the model about the articles published in the journal that year. (Yes. Download one at a time. Merge the PDFs and upload.) But I was told it was too much. So I did this for a half year. It worked OK, but it was very slow. Claude 3 told me I could only ask a few questions before it cut me off. Clearly, I hit the limits well before it was useful. And what I really wanted to do is to chat about 6-10 years of issues.

So I tried ripping the PDFs into plain text but I ended up hitting similar limits very quickly. And it didn’t seem to work too well. Looking at what came out of the pdf-to-text auto-generation, I am not shocked the AI didn’t know what to do with that.

Here’s what worked. I just decided to make article discovery mostly “manually.” Fortunately, here the task was either to find everything in the top journals about topic X or glance through everything in the X topical journals that could be used for purpose A. So this was done manually with searches within the journal’s webpages augmented with some specialized Google Scholar work. For the topical journals, I read the titles of entire issues. I also grabbed a few things that the journals recommended along the way in other journals they publish. (In fact, this was done already for months.) To add, I used scite_, I used Preplexity.

After all this, if an article looked like it could be relevant, I took its citation, abstract, and discussion/conclusion and manually pasted it into a text file. I separated articles by #####. This took forever. (In 2024, why did I have to manually copy and paste 60 article abstracts, I have no idea. I guess I do have an idea. Moving on…) But once I had a document with around 60 articles (around 82k words), I was able to upload this to Claude AI and have a good discussion about what is in there. (Pro tip. Never leave a DOI out of a citation if you ever do this. Ask me how I know.)

Yes, every prompt was super slow and I was hitting limits in about 6-8 prompts until it cut me off for a few hours. And this was with the paid Claude AI subscription. So I had to be thoughtful about what I asked. I rolled up my sleeve and used all my prompt engineering (and even more prompt engineering) skills not to waste a single prompt. (If the latter link was helpful to you, sign up for their newsletter. I am not sure I was supposed to give this to you without you signing up. There are many more cool pages where this came from. And if you are at all AI curious, the newsletter is a great quick read every day. ) And sometimes I had to wait until the next prompt… for hours. But it worked. To be sure, first I asked for a bibliography. (That was correct.) And I even got correct quotes when I asked it to go step by step and first identify the main points it will make, then find good quotes in the uploaded txt file to support those claims, and then write up the lit review. Prompts for a full lit review with a few subheadings to guide yielded something much shorter than I wanted (despite size specifications). Going section by section did the trick.

After going through and checking everything (themes the articles were cited for, quotes, cites, bibliography), I can honestly say this worked very well, especially for the narrow purpose. The only issue I had was made-up DOI links when the document uploaded left the DOI link out. The literature discovery was practically manual labor, and the prep work for the uploaded book-length document was mind-numbing, but the results are very good, and overall, it was a fifth of the time to do it this way even including double-triple checking everything.

If it wasn’t so excruciatingly slow, and if I wasn’t running up against limits, it would be amazing to just have a conversation about the pieces with the AI. Have a discussion with the papers or paper authors. In fact, I am now convinced journals should train their models (GPTs?) simply with their own text and allow subscribers to have conversations about the content.


As I signed my name to the third piece of paper recently (or was it the fourth), I am wondering, where is that person who swore they would never write a book. Now, with this signature, I have three book contracts, and even an edited volume forthcoming soon.

So lets talk about these a bit. It is good to take stock of what am I doing.

Hawkins K, Carlin R, Littvay L, Rovira Kaltwasser C (eds.) (forthcoming) The Ideational Approach to Populism: Concept, Theory, and Analysis Extremism and Democracy series at Routledge

I am very much looking forward to this one. A little over a year ago I wrote an opinion piece in Nature where I argued for the importance of systematic comparative analysis. This book is our (Team Populism‘s) first attempt at this and, while not perfect, I am pretty happy with the outcome.

Then there is the almost finished book with my former students, Bruno Castanho Silva and Constantin Manuel Bosancianu on Multilevel Structural Equation Modeling. There is no book dedicated to the subject, to date. There are a few good book chapters, but we wanted to do something more accessible to a less technical crowd (and since Bruno asked me if we could just write this equation in landscape, I am not sure we succeeded). We pitched the idea to SAGE’s Little Green Book series a while back and they liked it. We managed to workshop earlier draft chapters at the 2016 and 2017 ECPR Summer Schools in Methods and Techniques and in a few weeks we will do it again. Hopefully it will be a complete draft by then. This solely depends on my writing and editing superpowers. (Yeah, I should be writing that and not blog posts.)

(On a side note, how inept I am at up to date quantitative methods technology will become clear at the bottom of this post, but I am proud to see that my former students’ webpages are on github – a platform I very much need to learn how to use. (And they aren’t the only ones to have their pages on github.) Not having the time to learn everything I want to learn, I hope, is compensated somewhat by pushing my students down rabbit holes they need to tumble-down to get (well) ahead of where I am at. #FeelingOldUnder40. I know, I know, I am embarrassing…)

Riding on the success of this idea and after years of discussions on what we should collaborate on, Jochen Mayerl and I sat down after the 2016 ECPR Summer School and spent two days discussing (and pounding out) what a short introductory Structural Equation Modeling book would look like if we wrote one. Fortunately SAGE’s same series, that was visibly missing a modern SEM book from the 170+ book repertoire, sounded convinced. We spent some time a year later working out some of the details, but the work starts early next year and hopefully we will have a draft to pilot at the 2018 ECPR Summer School.

But even before that, Cambridge University Press approached Kirk Hawkins to write a short book for their new Elements series on how the 2016 US election (read: Donald Trump, but we think also Bernie Sanders) fits into the comparative world of populism. Americanists in the US can be quite detached from the world of comparative politics and it is clear that current events caught them (as much as everyone else) by surprise. Americanists want to look into Trump and populism, so instead of reinventing the wheel (or rather, further confusing an already massively muddled concept – there is a lot of that going on nowadays), maybe placing the US in a comparative context where populism has been studied for decades is not a bad idea. And the Elements series looks like a great medium to do this. This also gives some opportunities to show off systematic comparative research (which is still the exception in populism studies).

The irony of Kirk (the American) asking me (the Hungarian) to write a book with him on American politics (not his field, much more mine) should not escape anyone. Between the two of us, I think we have sufficient chops in systematic comparative research on the level of elites, masses, focus on Latin America, Europe and, of course, the US to do this right. This is going to be fun. I am very much looking forward to it. And it is going to be tough as deadlines are tight. They want the book by the end of January. (February might actually be doable. At least these things are short, which, in the case of the SEM book may be more a challenge than an asset, but…)

There is another reason I am looking forward to this. Recently, a medical researcher colleague asked me to help with some stats. I figured I will finally do this the right way. I have been teaching R and good coding practices for years to my students but I never internalized them myself. I figured I will do this right, for once. (I won’t open SPSS, etc.) It was so much fun. For most of my political science work I rarely get to open a stats package. I rarely open
R and not realize it is way outdated and I should upgrade. When I have to run something, it is usually in Mplus and I can usually get a collaborator to give me clean, Mplus ready data (or I am in a rush and do it in SPSS, thought it has been a while). Now I have a project where I can and will do everything myself and do it right. Looking forward to learning more about visualization in R. I did a workshop with Martin Molder but still never had to open ggplot2.

So I have three books to write before the start of my sabbatical (assuming the request goes through to leave next fall for a year). What will I do on my sabbatical? I guess I have ideas. CEU’s Comparative Populism Project will start to produce data. There are multiple other grant proposals, grant calls in the pipeline with the potential to keep me busy doing populism research (or, at least, scientific busy work with hopes of generating something useful – like data – for research). Maybe another book? Definitely articles. I do have a plan at the back of my head to write one more stats book, this one on experimental design for political and social scientists. We will see if that happens. I need to do a lot more research on what is out there before I commit to writing even a proposal for this.

Our article on Populism and Belief in Conspiracy Theories is up on pre-release

Our article with Bruno Castanho Silva and Fede Vegetti in the Swiss Political Science Review special issue on populism is titled: The Elite Is Up to Something: Exploring the Relation Between Populism and Belief in Conspiracy Theories.

Abstract: We explore the relationship between populist attitudes and conspiratorial beliefs on the individual level with two studies using American samples. First, we test whether and what kinds of conspiratorial beliefs predict populist attitudes. Our results show that belief in conspiracies with greedy, but not necessarily purely evil, elites are associated with populism. Second, we test whether having a conspiratorial mentality is associated with all separate sub-dimensions of populist attitudes – people-centrism, anti-elitism, and a good-versus-evil view of politics. Results show a relation only with the first two, confirming the common tendency of both discourses to see the masses as victims on elites’ hands. These findings contribute to research on the correlates of populism at the individual level, which is essential to understanding why this phenomenon is so strong in contemporary democracies.

CEU’s Intellectual Themes Initiative Funds Our Comparative Populism Project

We are grateful for CEU’s Intellectual Themes Initiative for funding our project. Now we have a lot of work to do over the next two years. Announcement penned by Erin Jenne:

We are excited to announce our two-year interdisciplinary CEU grant project, Comparative Populism, which will launch September 2017 and is conducted by Levente Littvay, Bruno Castanho E Silva, Rosario Aguilar Pariente, Constantin Iordachi, Nick Sitter, Zsolt Enyedi, Elissa Helms, Balazs Vedres, Judit Sandor, Matt Singer, Norbert Sabic, Federico Vegetti, several CEU students (hopefully) and with external support from multiple scholars including Team Populism.

This project brings together CEU and international scholars working on topics related to populism across different disciplinary traditions. The aim is to build up a comparative database on countries across Europe on the varieties of populist politics and policies across the region from the end of the Cold War to present and to explore the connections between populism on the one hand and gender, law, foreign policy, and party politics on the other. By joining the different methodological skills and perspectives across the different academic units, the project team can arrive at a multi-faceted understanding of why populism manifests more strongly in some countries than others in the same region, why it takes on social conservative dimension in some places and more nationalist/nativist dimension in others, and how all of this connects to gender, the law, foreign policy, public administration and party systems.

Find out more HERE!

Joining the Presidium of the Hungarian Political Science Association

I am honored to be elected by the membership of the Hungarian Political Science Association and especially thankful to Zsolt Enyedi for the nomination. I have not been a contributing part of Hungarian political science so I greatly appreciate the trust of my Hungarian colleagues especially in my absence. I will work hard not to let them down. I also would like to congratulate president Kriszta Arato for her election. Excellent choice. Looking forward to working together.