Echo Chambers without Conversation? Enriching Research on Polarization and Fragmentation on Twitter with the Analysis of Reciprocal Communication

Echo chambers on social media are described as homophilic clusters that are characterized by a repeated confirmation of users’ political opinions and a lack of confrontation with other opinions, a process that leads to the solidification and radicalization of beliefs. The empirical research literature on the existence and effects of echo chambers yields quite mixed results, with some studies finding evidence for the existence of echo chambers and others not. In this article, we argue that network analytic research about echo chambers on Twitter would benefit from an investigation of reciprocal communication. Current research finds evidence for echo chambers for political topics in retweet networks. However, such approaches may not adequately reflect the degree of fragmentation on Twitter because a retweet is a form of information diffusion that does not support the reciprocity necessary for political discussions. To capture reciprocal communication, we instead suggest to focus on replies. We then show that typical approaches to data collection based on hashtags or keywords capture only a small fraction of replies about any given topic. With the introduction of the conversation_ID by Twitter it is now possible to collect all replies to original tweets, resulting in much larger collections of replies. We illustrate an approach that focuses on reciprocal communication through replies with the construction of the #debate2020 dataset. Here, original tweets and replies are represented in a tree structure as threaded reciprocal communication. We argue that it is in threaded replies where we might find evidence for echo chambers in patterns of mutual affirmation or contestation and delegitimization of dissenting positions

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation.

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks

For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio, and slope of the grain axis relative to the rolling direction are applied. However, usually simple distribution functions like log normal or gamma distribution are used. Yet, these do not take the interdependencies between the microstructural parameters into account. To fully describe any metallic microstructure though, these interdependencies between the singular parameters need to be accounted for. To accomplish this representation, a machine learning approach was applied in this study. By implementing a Wasserstein generative adversarial network, the distribution, as well as the interdependencies could accurately be described. A validation scheme was applied to verify the excellent match between microstructure input data and synthetically generated output data