Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine

The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media. However, the strategy behind the dissemination of propaganda has remained unclear, particularly how the online discourse was strategically shaped by the propagandists' community. Here, we analyze the strategy of the Twitter community using an inverse reinforcement learning (IRL) approach. Specifically, IRL allows us to model online behavior as a Markov decision process, where the goal is to infer the underlying reward structure that guides propagandists when interacting with users with a supporting or opposing stance toward the invasion. Thereby, we aim to understand empirically whether and how between-user interactions are strategically used to promote the proliferation of Russian propaganda. For this, we leverage a large-scale dataset with 349,455 posts with pro-Russian propaganda from 132,131 users. We show that bots and humans follow a different strategy: bots respond predominantly to pro-invasion messages, suggesting that they seek to drive virality; while messages indicating opposition primarily elicit responses from humans, suggesting that they tend to engage in critical discussions. To the best of our knowledge, this is the first study analyzing the strategy behind propaganda from the 2022 Russian invasion of Ukraine through the lens of IRL

Causal Understanding of Why Users Share Hate Speech on Social Media

Hate speech on social media threatens the mental and physical well-being of individuals and is further responsible for real-world violence. An important driver behind the spread of hate speech and thus why hateful posts can go viral are reshares, yet little is known about why users reshare hate speech. In this paper, we present a comprehensive, causal analysis of the user attributes that make users reshare hate speech. However, causal inference from observational social media data is challenging, because such data likely suffer from selection bias, and there is further confounding due to differences in the vulnerability of users to hate speech. We develop a novel, three-step causal framework: (1) We debias the observational social media data by applying inverse propensity scoring. (2) We use the debiased propensity scores to model the latent vulnerability of users to hate speech as a latent embedding. (3) We model the causal effects of user attributes on users' probability of sharing hate speech, while controlling for the latent vulnerability of users to hate speech. Compared to existing baselines, a particular strength of our framework is that it models causal effects that are non-linear, yet still explainable. We find that users with fewer followers, fewer friends, and fewer posts share more hate speech. Younger accounts, in return, share less hate speech. Overall, understanding the factors that drive users to share hate speech is crucial for detecting individuals at risk of engaging in harmful behavior and for designing effective mitigation strategies

Integrating Syntactic and Prosodic Information for the Efficient Detection of Empty Categories

We describe a number of experiments that demonstrate the usefulness of prosodic information for a processing module which parses spoken utterances with a feature-based grammar employing empty categories. We show that by requiring certain prosodic properties from those positions in the input where the presence of an empty category has to be hypothesized, a derivation can be accomplished more efficiently. The approach has been implemented in the machine translation project VERBMOBIL and results in a significant reduction of the work-load for the parser.Comment: To appear in the Proceedings of Coling 1996, Copenhagen. 6 page

Russian propaganda on social media during the 2022 invasion of Ukraine

The Russian invasion of Ukraine in February 2022 was accompanied by a large-scale propaganda campaign. Here, we analyze the spread of Russian propaganda on social media. For this, we collected N = 349,455 messages from Twitter with pro-Russian content. Our findings suggest that pro-Russian messages were mainly disseminated through a systematic, coordinated propaganda campaign. Overall, pro-Russian content received ~251,000 retweets and thereby reached around 14.4 million users, primarily in countries such as India, South Africa, and the United States. We further provide evidence that bots played a disproportionate role in the dissemination of propaganda and amplified its proliferation. Overall, 20.28% of the spreaders are classified as bots, most of which were created in the beginning of the invasion. Together, our results highlight the new threats to society that originate from coordinated propaganda campaigns on social media in modern warfare. Our results also suggest that curbing bots may be an effective strategy to mitigate such campaigns

Discrete-time Analysis of Multicomponent GI/GI/1 Queueing Networks

In this work, we provide initial insights regarding the error introducedinto multicomponent queueing systems by assuming the departure processes of arbitraryGI/GI/1-oo queues to be renewal processes. To this end, we compute the sojourntime distribution as well as departure distributions of a linear chain of queueingcomponents and compare the results to a simulation of the same system. By applyingthe renewal approximation, potential autocorrelations of the departure processesare lost. We investigate the magnitude of this error regarding both the sojourn timeas well as interdeparture time distributions for a broad set of parameters. Althoughmore indepth studies are needed, our results show that both distributions can beclosely approximated, which allows the application of the model to asses the performanceof real world NFV function chains

A high resolution head-related transfer function database including different orientations of head above the torso

DFG, 174776315, FOR 1557: Simulation and Evaluation of Acoustical Environments (SEACEN

Endoscopic submucosal dissection for superficial esophageal squamous cell carcinoma: long-term follow-up in a Western center

Background/Aims Endoscopic submucosal dissection (ESD) has been established as a treatment modality for superficial esophageal squamous cell carcinoma (ESCC). Long-term follow-up data are lacking in Western countries. The aim of this study was to analyze long-term survival in a Western center. Methods Patients undergoing ESD for ESCC were included. The analysis was performed retrospectively using a prospectively collected database. Results R0 resection rate was 96.7% (59/61 lesions in 58 patients). Twenty-seven patients (46.6%) fulfilled the curative resection criteria (M1/M2) (group A), 11 patients (19.0%) had M3 lesions without lymphovascular invasion (LVI) (group B), and 20 patients (34.5%) had lesions with submucosal invasion or LVI (group C). Additional treatment was recommended after non-curative resection. It was not performed in 20/31 patients (64.5%), mainly because of comorbidities (75%). Twenty-nine out of 58 (50.0%) patients died during a mean follow-up of 3.7 years. Death was related to ESCC in 17.2% (5/29) of patients. The disease-specific survival rate after curative resection was 100%. Overall survival rates after 5 years were 61.5%, 63.6% and 28.1% for groups A, B, and C, respectively. The overall survival was significantly worse after non-curative resection (p=0.038). Conclusions Non-curative resection is frequent after ESD for ESCC in Western patients. The long-term prognosis is limited and mainly determined by comorbidity. Early diagnosis and pre-interventional assessments need to be improved

Transport through (Ga,Mn)As nanoislands: Coulomb-blockade and temperature dependence of the conductance

We report on magnetotransport measurements of nanoconstricted (Ga,Mn)As devices showing very large resistance changes that can be controlled by both an electric and a magnetic field. Based on the bias voltage and temperature dependent measurements down to the millikelvin range we compare the models currently used to describe transport through (Ga,Mn)As nanoconstrictions. We provide an explanation for the observed spin-valve like behavior during a magnetic field sweep by means of the magnetization configurations in the device. Furthermore, we prove that Coulomb-blockade plays a decisive role for the transport mechanism and show that modeling the constriction as a granular metal describes the temperature and bias dependence of the conductance correctly and allows to estimate the number of participating islands located in the constriction.Comment: 5 pages, 3 figures, completed affiliations and corrected typo

DX5+NKT cells display phenotypical and functional differences between spleen and liver as well as NK1.1-Balb/c and NK1.1+ C57Bl/6 mice

These results show that DX5+NKT cells are a heterogeneous population, depending on the dedicated organ and mouse strain, that has diverse functional capacity

The FABIAN head-related transfer function data base

This data base includes head-related transfer functions (HRTFs), headphone transfer functions (HpTFs), and 3D-meshes of the FABIAN head and torso simulator. More detailed information is provided in the documentation within the data base.DFG, WE 4057/3-1, Simulation and Evaluation of Acoustical Environments (SEACEN