Brown Bag Seminar: Frictional memory – the afterlives of lynching in the digital public spheres
In this talk, we present a hybrid methodological approach for studying large-scale digital discourse by combining computational analysis with close and interpretive reading. We use our recent study of the digital afterlives of lynching on Twitter as a case study to demonstrate how computational methods can help researchers identify patterns across large collections of social media data while retaining the contextual and interpretive depth of qualitative analysis.
We introduce the idea of scalable reading, which combines the breadth of computational or distant reading with the depth of close reading. Our dataset consists of 133,370 English-language tweets referring to lynching in the United States between 2007 and 2022. We demonstrate how different computational techniques can be used at different stages of the research process, including natural language processing, transformer-based named entity recognition, entity verification using Wikidata and Wikipedia, hashtag co-occurrence analysis, and BERT-based topic modeling.
Rather than treating computational results as the final interpretation, we use them to identify patterns, connections, and points of tension that can then be examined through close reading. For example, hashtag co-occurrence analysis allows us to trace how references to historical figures and events become connected with contemporary events and movements, while topic modeling reveals different and sometimes competing meanings associated with the term “lynching.”
We will introduce three methodological concepts that emerged from this combination of computational and interpretive analysis: temporal traction, referring to how digital references connect past and present; semantic abrasion, referring to the collision of competing meanings and interpretations; and visibility routing, referring to the uneven ways in which platform infrastructures shape what becomes visible, by whom, and where.
The talk will also discuss the methodological challenges of working with large-scale social media data. Computational approaches can reveal patterns that would be difficult to identify through close reading alone, but scaling up does not eliminate the need for contextual knowledge, interpretation, and ethical reflection. In particular, we discuss the risks of decontextualization and the ethical challenges involved in computationally studying histories of racialized violence.
Finally, we reflect on what this approach can offer researchers working with other forms of large-scale textual or digital data. The central methodological question is not whether computational or qualitative methods are preferable, but how the two can be combined so that computational patterns become starting points for interpretation rather than substitutes for it. This approach offers a way of working across disciplinary and methodological boundaries and may be useful for researchers in the humanities and social sciences who want to incorporate computational methods into the study of digital texts, discourse, memory, and cultural phenomena.
Feeza Vasudeva is an Academy Researcher at the University of Helsinki (Research Council of Finland, 2025–2029), leading the DIVINE project (Digital Interfaces and Virtual Innovations in New Expressions of Faith). Her research investigates how digital technologies reshape political belief, religious publics, and democratic life. Her work spans multiple interconnected areas: as part of HSSH’s ‘Datafication of Society’ initiative, she examined how collective violence, specifically lynching in India and America, circulates, persists, and fragments across digital platforms; simultaneously, she investigated political deification and religious populism in global contexts. Within DIVINE, this now extends to AI-generated religious content and aesthetics, including her work on Godbots, a study of how conversational AI reconfigures who or what can speak for the sacred.
Narges Azizifard is a PhD in Computer Science and a researcher with experience in machine learning, natural language processing, and digital humanities. She worked as a Postdoctoral Researcher at the Department of Digital Humanities, University of Helsinki (2022–2025), where she used NLP and machine learning methods to analyze large-scale social media data, including Reddit and Twitter. Her research interests include computational social science, spatial analysis, digital humanities, NLP, and data-driven discourse analysis.
Eetu Mäkelä is a professor of Digital Humanities (Human Sciences–Computing Interaction) at the University of Helsinki. At the Helsinki Centre for Digital Humanities, he leads a research group that seeks the technological, processual and theoretical underpinnings of successful computational research in the humanities and social sciences. Additionally, he serves as a technological director at the DARIAH-FI infrastructure for computational humanities and is one of three research programme directors in the datafication research initiative of the Helsinki Institute for Social Sciences and Humanities. He also leads the Helsinki Liberal Arts and Sciences Bachelor’s Programme at the University of Helsinki.
The Methodological unit at the Helsinki Institute for Humanities and Social Sciences (Uni Helsinki) organizes a recurrent Brown Bag Seminar to highlight novel methodological approaches in the humanities and social sciences. The idea of the meetings is to introduce methodological innovations and cutting-edge research in various disciplines in an easily accessible manner and have an interdisciplinary discussion in an easy-going atmosphere over lunch.
There will be a 20-minute introduction to the methodological theme, followed by an open discussion of 40 minutes. The seminars are open to everybody and welcome a multidisciplinary and methodologically curious audience. The language of the meetings can be Finnish or English.
This is a hybrid event taking place at the University of Helsinki Main Building, Fabianinkatu 33, 3rd floor, room F3010. For remote participation, use this link.
More information is available on the event site.