Dear member of the HNR community,
We are pleased to announce another event in the HNR Lunch Lectures Series. These online lectures will shed a spotlight on recent research and ideas from the field of historical network analysis to promote discussion among the HNR community.
The next in our regular series of lunchtime lectures is on November 24, 2022 (12-1 p.m. CET). Our speaker will be Anastasia Glawion, postdoc at the LitLab of the Technical University of Darmstadt. You can find her abstract below or on the HNR website. If you would like to participate, please register via Eventbrite before November 22, 2022. You will receive a Zoom link by email prior to the lunch lecture.
Retrieving online memory practices: an network analysis pipeline
by Anastasia Glawion
In this talk, I present a pipeline of network analysis applications to the studies of user communities on online forums. In my dissertation, this pipeline was applied to the study of a historical online forum dedicated to the memory of World War II, resulting in the extraction of three large groups of memory practices – empirical, conversational and conservational practices. Each group of practices contained several more specific subgroups. For example, empirical practices were divided into those aimed at memory objects and those aimed at places of memory. Conversational practices were characterized by varying degrees of sentiment and ranged from neutral military-history discussions to heated debates on national memory. Finally, conservational practices were only possible because of the actions of brokers: users who had access to archives were sharing that access with the forum community.
The methodological pipeline consists of three steps. First, interactional data is extracted and a modularity clustering algorithm is applied, thus identifying dense subgroups within the network. In a second step, for exploration purposes, a textual corpus is created on the basis of the dense subgroups. A topic modeling algorithm is applied to the corpus, whereby the resulting topic model can be presented as a term-overlap network to demonstrate dense subgroups that also have strong thematical overlap. The third step contains the analysis of the discussion level while considering each discussion’s role in the dense subgroup extracted in the first step with the help of structural equivalence in networks.