Hadiths are a major source of available narratives on early middle eastern history. It is a huge body of narrations about the sayings of the Islamic prophet (and others) on different events and phenomena, that transmitters passed down from one generation to another (this claim is both refuted and supported by different evidences), and different generations recorded these in different books. Hadiths have far reaching implications to this day, as they often influence legal, cultural, political, and social decision making in the Muslim world, and they are also entangled with other cultures that are trying to understand Islam. The dynamics of these narratives reveal very important historical insights and provide a lens into the authenticity of the narratives and historical claims.
I was an undergraduate student at Bard College (NY) studying physics and computer science when I met Mairaj in 2011. He was teaching an advanced Islamic Legal Theory class that I took. Even though science and engineering students often complain about their humanities classes and I was no different initially, the liberal arts culture at Bard slowly changed my views. I was drawn towards the humanities and was increasingly taking advanced humanities classes to fulfill my requirements, instead of the usual introductory classes science students take to get by.
Mairaj was a young professor, fresh out of Princeton with a PhD in religious studies, and I would approach him after the classes to talk about the kind of research they do in general. Mairaj used to be a student of information management and knew some coding and databases, so unlike many other humanities professors he would enthusiastically discuss possibilities in digital humanities research. We explored a few things in 2011 and 2012 on Isnads and Matn (information on how transmitters passed the Hadith literature). I wrote some code to combine Matns, and discussed results with Mairaj, but that was pretty much it. The project seemed to die down.
After I left Bard to pursue a Master’s in Computing, I was increasingly drawn towards Network Science. In 2013, I reached back to Mairaj with the idea that we could interpret the Hadith literature as a social network of transmitters. Mairaj moved to UC Davis at the time. We collaborated again to explore some preliminary results in centrality metrics in the Hadith network. Mairaj was not an expert in any of web scraping, data mining, or network science, but over the next few years he taught himself a lot of the tricks by studying my and other people’s code.
When I started my PhD at MIT, we made an informal habit of visiting each other at MIT or UC Davis, and would have these few-days long hackathons where I would code non-stop and Mairaj would organize our data into better databases or historically interpret the computational results. We would discuss results and visualizations and talk about writing a book together. In fact, in 2015 I received a book offer from Princeton University Press, but we were not entirely sure if we could commit the time required for such a project so we passed the offer after drafting some parts of the book.
From 2016, we doubled down on our efforts and made consistent progress. New members would join our team regularly. Some left and some stayed. Mairaj took care of all the management, with occasional support from myself. We started writing and getting grants. We eventually had some excellent technical people in the team (Danny Halawi, Mohamed Alkaoud, and recently Shuaib Choudhry) so I did not have to do heavy lifting on coding the analytics infrastructure anymore. We also had other humanities researchers give us feedback and discuss our work over long sessions. At this point, I shifted my attention and time to some of the theoretical questions that I have been exploring since the beginning, such as, what does the overall structure and dynamics of the Hadith network look like? What does it say about the current notions of Hadith transmission and disputes on early-Islamic history? I attended history and religious studies workshops and conferences since 2014, presenting my research on these questions.
At the moment, our work's portfolio includes 1400 parsed books, with 50,000 authors and millions of links between them, and a collection of data analysis and visualization techniques to understand the narration dynamics in this huge network.
Note that this was not my PhD research. My main interests are Human-Computer Interaction (HCI), Data Science, Interactive Computing, Design, and Wearable Sensors. However, I have been very passionate about this project, and whenever I got a chance or needed a break from my PhD work I would work on this. Having read some other religious studies PhD works, I feel comfortable to say that the impact of our team’s Hadith analytics works could be equated to several PhDs. I would not dare call it a second PhD for myself because I know my readings are not at the level of an actual humanities PhD. However, having done a lot of scattered readings over the years, I do entertain the idea at times that I should take a religion PhD qualifying exam to figure out where I am really at in this regard.
With the background out of the way, let me summarize a few key differences between the sciences and humanities cultures. When speaking about science, I speak from experience in the CS, Physics, and Applied Math communities, but I have heard and read many stories from other science PhDs that I feel comfortable to make some general statements.
Publication expectations: Probably the first thing a scientist (especially a computer scientist) would find surprising in the humanities land is the pace of publications. While quantity of papers doesn't reflect quality, computer scientists are used to submitting substantial new works and ideas to conferences every year. The pace at first may seem slow in a digital humanities collaboration.
One reason maybe is just the nature of research. Often times writing 500 lines of code to deal with a dataset is much easier than reading through big volumes of text and understanding the nuances presented by their authors. Even though coding clever algorithms is no small task, the humanities works often are the long and tedious ones. The other reasons for such prolific activity in the sciences have good and bad causal forces. The bad force is how money and grants are set up in the current system. The outcomes of grants are usually publications, and there is more money allocated for science/engineering. Therefore, many “garbage papers” are written in abundance in a “prolific” field like computer science to keep the money flowing. Even good scientists often feel pressured to churn out useless papers, and ignoring that mindset is a challenge when you are aiming for a job market.
Having said that, I think there is room for improvement in the humanities too, where the sciences are more or less doing it right. It boils down to team-based work.
Team culture: The research works in humanities are often done by lone rangers, instead of large international collaborations and teams that are frequently seen in the sciences. For no-BS prolific scientists, frequent publications is the way to disseminate knowledge asap and open it up for critique, and science appears to move faster because of that. The challenge for a reader is to then filter through the incremental works described in #1 and find these gems. Once you build a reputation as a no-BS scientist/team, it’s easier to just find and follow your work.
This kind of international teamwork mindset could be adapted in the humanities too, with better means of knowledge distribution.
Problem selection: When dealing with theory, I personally favor research in the Pasteur’s Quadrant, fundamental research that has applied use cases. Search for a never-ending series of abstractions — ignoring the human meaning making process — sometimes seems redundant to me. It is a view that I share with Sabine Hossenfelder, and this style of research seems to be prevalent among many theorists.
Interestingly, I think I have seen this trend in both the humanities and sciences. Without pointing out specific works, I would just say that the premature search for can-explain-it-all theories is quite abundant in both fields, whereas we should first look for provable hypotheses that may or may not generalize all too well. That is part of doing science.
Superiority/Inferiority complex around technology: A recipe for disaster is to build an interdisciplinary team whose members don't know (or trust) each other’s strengths, or don't have the capability to understand the available strengths properly. Some scientists might think they are the genius here and doing others a favor. At the same time, humanities scholars may get excited at the prospect of applying technical methods without understanding the methods and their implications completely. It is inevitable for a new interdisciplinary team, and I was fortunate that in our team none of these happened, except occasional tensions at times.
I have observed such trends in other collaborations though. I have often seen HCI collaborations where a more technical person (say, a machine learning theorist) would be the person holding back the less technical but more humanities/social science oriented researchers, who may be waiting for the technical person to apply some cool techniques on their hard-earned, curated datasets. However, applying a cool method may not answer the question at hand, but both parties don’t seem to care as this might get them a new paper. To do good research, we need to understand each other’s works better. A corollary to this is that good team spirit in this realm means maintaining a delicate balance of trust and doubt, which brings me to my next point.
Teacher and Colleague: In a good digital humanities collaboration, each person is both. As a teacher, you have to sometimes direct with certainty, but as a colleague you have to listen and trust. In our work, Mairaj’s subject matter expertise and mentorship guided my investigations. However, my emphasis on certain techniques paid off, and Mairaj had to trust me on my mathematical instincts on his subject matter.
Presentation: This is a very minor but funny one. Let’s just say I was not impressed by slides with poor visual choices, such as cyan/purple backgrounds, full of yellow and red colored text, with no pictures, that some North American humanities professors were just reading out loud in some conference sessions. While I did see very good quality presentations in the same conferences, I didn’t realize slides that can instantly make you fall asleep could be part of the same sessions. You say I shouldn’t judge from a few conferences? I agree, but it was unexpected nonetheless. I am used to seeing demo videos with published papers, and well crafted slides for presentations in HCI. So naturally I would expect that professors who teach in US universities do not just read their slides that are full of text. Dissemination of knowledge in widely understandable and enjoyable formats should be prioritized by any researcher.
Philosophical Differences and Similarities
I am not entirely qualified to comment on such a huge topic, but I can talk about this philosophy from the perspective of cognitive science and embodied mathematics, a field that formed the backbone of my PhD dissertation. To begin to understand the philosophical differences between sciences and humanities, and to act on reducing our existing biases and assumptions, we perhaps need to understand the nature of mathematical abstractions first.
A. The nature of abstractions: Mathematics is feared by many, and a lot of works in the digital humanities require some level of mathematical maturity (coding is a form of mathematics too). The reason most people often feel lost in this land is because the gradual layers of abstractions and metaphors are often too many to form a coherent mental model. Let me explain with a diagram from my PhD dissertation.