Speaker: Phillip Stenmann Baun (1) and Kristoffer Nielbo (2, 3)
Affiliation: (1) Department of Global Studies, Aarhus University, 8000 Aarhus C, Denmark; (2) Center for Humanities Computing Aarhus, Aarhus University, 8000 Aarhus C, Denmark; (3) Interacting Minds Centre, Aarhus University, 8000 Aarhus C, Denmark
Title: Right-wing Mnemonics
Abstract: This paper presents a natural language processing technique for studying memory on the far-right political discussion forum /pol/ on 4chan.org. Memory and the use of history play a pivotal role on the far-right for temporally structuring beliefs about social life and order. However, due in part to methodological limitations, there is a lack of knowledge regarding the specific historical entities that make up the far-right memory culture and wider historiography. To better grasp the structure of far-right memory, this paper opts for a data-intensive methodology, using machine learning on a data set of approximately 66 million posts from /pol/ from 2020. 19,821 random posts were manually annotated, according to the presence of historical entities. After evaluating interrater reliability, data were used to train a naïve Bayes text classifier to learn the lexical features of so-called ``posts of memory’’ ( POMs ). After parameter tuning, the model extracted from the dataset a total of 1.083.471 POMs with a precision score of 98.43%. It is argued that this technique provides a novel way to automate the identification of historical entities within the far-right authored text, of benefit for the fields of memory studies and far-right studies, two fields that have traditionally relied on more qualitative close-reading approaches. By investigating the mnemonic features of the /pol/ posts during steps in the methodological pipeline, the paper contributes important insights into the challenges of identifying and classifying lexical features in hyper-vernacular digital spaces like 4chan, where communication is highly defined by intertextuality, semantic ambiguity, and cacography.