04 March 2025 ~ 0 Comments

Finding a Connection between Kinship and Material Culture using Network Science

The past fascinates us. I often fantasize about how our ancestors lived: what did they do? How did they relate to one another? Were their social networks similar to ours? Archaeology is the way we try to answer these questions if we do not have written records. The main problem is that archaeology finds stuff — material culture. Social relationships can’t be dug up from the mud.

Together with Camilla Mazzucato and a team of archeologists and biologists, we decided to investigate whether we could actually infer the social relationships from the material culture we found. The result was the paper “‘A Network of Mutualities of Being’: Socio-material Archaeological Networks and Biological Ties at Çatalhöyük“, which appeared in January in the Journal of Archaeological Method and Theory.

The paper focuses on the site of Çatalhöyük which was inhabited in the Neolithic for several millennia. Çatalhöyük is the ideal place to draw connections between found material culture and interpersonal relationships because of some peculiar habits in the culture which inhabited the site. Upon the construction of a new building in the settlement, in fact, the habit was to bury the dead in the foundations (humans are weird).

Having lots of dead bodies is fantastic (all of a sudden I sound like Hannibal Lecter) because now in the buildings we can find material culture and human remains, both of which had a connection with the building itself. From the human remains we can infer kinship relationships between buildings, because people related to each other are buried in them.

This is where the team of biologists come in. They analyze the DNA of the human remains to establish which pairs of individuals have a first, second, or third degree relationship. Once we have both material culture and kinship relationships between buildings we can start asking ourselves whether the two correlate or not.

One way to do it is by using network variance. We can connect buildings — the nodes in the network — if they share significant amounts of material culture. Then, for a given kinship group — a set of people with at least second or third degree relationships –, we can create a numeric node attribute: how many individuals from that family were buried in each building.

The Material Culture Network of Çatalhöyük: nodes are buildings, connected if they share significant amounts of material culture. The node size tells you about the number of artifacts found in the building. The edge size is the number of common artifacts and the color is the significance level. The node color tells you how many individuals from a specific kinship group are buried in the building.

The network variance of this attribute tells us how dispersed the family is in the material culture landscape. By comparing this with a null family — a family that buries individuals at random — we can know whether the real family tended to be more concentrated in the material culture space than expected, a sign that material culture and kinship correlate. Which is what we observe!

The number of null models (y axis) with a given null family network variance value (x axis). The green band is the network variance value of the observed kinship group. Lower value = less variance = more concentrated.

There are a number of possible alternative explanations, of course. One we checked for is geotemporal proximity. Buildings nearby each other and built around the same time also share more material culture and related individuals. So we control for geotemporal proximity and we see that the effect is still significant. In other words: it is more likely for related individuals to be buried in buildings with similar material culture, regardless of when and where these buildings were built. We also check the robustness of our results by slightly changing the way we build our networks, and the way we analyze the DNA remains — the result is still there.

Our result suggests a nice consequence: it is justified to say that sites containing the same material culture hosted individuals that were related to each other. Found material culture can tell us something interesting and meaningful about the shape and dynamics of past social networks.

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05 March 2024 ~ 0 Comments

My Winter in Cultural Data Analytics

Cultural analytics means using data analysis techniques to understand culture — now or in the past. The aim is to include as many sources as possible: not just text, but also pictures, music, sculptures, performance arts, and everything that makes a culture. This winter I was fairly involved with the cultural analytics group CUDAN in Tallinn, and I wanted to share my experiences.

CUDAN organized the 2023 Cultural Data Analytics Conference, which took place in December 13th to 16th. The event was a fantastic showcase of the diversity and the thriving community that is doing work in the field. Differently than other posts I made about my conference experiences, you don’t have to take my word for its awesomeness, because all the talks were recorded and are available on YouTube. You can find them at the conference page I linked above.

My highlights of the conference were:

  • Alberto Acerbi & Joe Stubbersfield’s telephone game with an LLM. Humans have well-known biases when internalizing stories. In a telephone game, you ask humans to sum up stories, and they will preferably remember some things but not others — for instance, they’re more likely to remember parts of the story that conform to their gender biases. Does ChatGPT do the same? It turns out that it does! (Check out the paper)
  • Olena Mykhailenko’s report on evolving values and political orientations of rural Canadians. Besides being an awesome example of how qualitative analysis can and does fit in cultural analytics, it was also an occasion to be exposed to a worldview that is extremely distant from the one most of the people in the audience are used to. It was a universe-expanding experience at multiple levels!
  • Vejune Zemaityte et al.’s work on the Soviet newsreel production industry. I hardly need to add anything to that (how cool is it to work on Soviet newsreels? Maybe it’s my cinephile soul speaking), but the data itself is fascinating: extremely rich and spanning practically a century, with discernible eras and temporal patterns.
  • Mauro Martino’s AI art exhibit. Mauro is an old friend of mine, and he’s always doing super cool stuff. In this case, he created a movie with Stable Diffusion, recreating the feel of living in Milan without actually using any image from Milan. The movie is being shown in various airports around the world.
  • Chico Camargo & Isabel Sebire made a fantastic analysis of narrative tropes analyzing the network of concepts extracted from TV Tropes (warning: don’t click the link if you want to get anything done today).

But my absolute favorite can only be: Corinna Coupette et al.’s “All the world’s a (hyper)graph: A data drama”. The presentation is about a relational database on Shakespeare plays, connecting characters according to their co-appearances. The paper describing the database is… well. It is written in the form of a Shakespearean play, with the authors struggling with the reviewers. This is utterly brilliant, bravo! See it for yourself as I cannot make it justice here.

As for myself, I was presenting a work with Camilla Mazzucato on our network analysis of the Turkish Neolithic site of Çatalhöyük. We’re trying to figure out if the material culture we find in buildings — all the various jewels, tools, and other artifacts — tell us anything about the social and biological relationships between the people who lived in those buildings. We can do that because the people at Çatalhöyük used to bury their dead in the foundations of a new building (humans are weird). You can see the presentation here:

After the conference, I was kindly invited to hold a seminar at CUDAN. This was a much longer dive into the kind of things that interest me. Specifically, I focused on how to use my node attribute analysis techniques (node vector distances, Pearson correlations on networks, and more to come) to serve cultural data analytics. You can see the full two hour discussion here:

And that’s about it! Cultural analytics is fun and I look forward to be even more involved in it!

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