FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe- art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud

Detection of Stock Manipulation Influencer Content using Supervised Learning

In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machine-learning and deep-learning-based techniques in advancing fraud detection systems

On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls

[EN] Many social media frauds related to finance can be summarized under what we consider promote-hit-and-run scams. Examples include rug pull scams also known as exit scams, pump-and-dump schemes or bogus crypto currency trading platforms. For scams of this kind to work they must be publicly advertised as lucrative investment opportunities disguising the fraudulent motivation behind them. Social media are key in this promotion. Here, fraudsters find platforms to persuade others investing into what later turns out to be a scam. Via social network analysis of Twitter screen names and their first-level contacts, our work investigates rug pulls. It is aimed at profiling social media communication around them with a special focus on the deployment of bots. Repeatedly bots have been identified in social media campaigns (Orabi et al., 2020). Bot deployment in the context of rug pulls, however, has not been studied yet. Our analysis of social data of 27 rug pulls reveals massive bot activity coordinated within and between rug pulls mainly targeting established finance news outlets, e.g., Bloomberg, Reuters. Among the conclusions of our work is that bot deployment may prove an early indicator for rug pulls and other promote-hit-and-run scams.Federal Ministry of Education and Research of Germany (BMBF)Janetzko, D.; Krauß, J.; Haase, F.; Rath, O. (2023). On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls. Editorial Universitat Politècnica de València. 187-194. https://doi.org/10.4995/CARMA2023.2023.1642818719

PERTURBATIONS IN THE SPECTRA OF HIGH RYDBERG STATES: CHANNEL INTERACTIONS, STARK AND ZEEMAN EFFECTS

Author Institution: Laboratorium fur Physikalische Chemie, ETH-Zurich, 8093 Zurich, SwitzerlandRydberg states of principal quantum number $n\gg50$ have been prepared by irradiation of an atomic beam of xenon with vacuum ultraviolet (VUV) radiation. Narrowband submillimeter-wave (THz) radiation was then used to record spectra of transitions from these Rydberg states to higher or lower-lying Rydberg states. The transitions were detected by selective field ionization and recording either the electrons or the ions, the latter offering the advantage of mass selection. Rydberg states of high principal quantum number are extremely sensitive to their environment, which can be exploited to characterize the experimental conditions under which the spectroscopic measurements are carried out nderline{\textbf{21}}, 385, (2002).}. The high resolution achieved in the experiments (better than 100~kHz) enabled the study of the spectral lineshapes and line positions in dependence of weak electric (down to below $100\mu$V/cm) and magnetic fields (down to a few $\mu$T), and of the density of Rydberg atoms and ions generated in the experimental volume. The experiments rely on the use of a pulsed, broadly tunable, laser-based source of THz radiation nderline{\textbf{93}}, 131105, (2008).} for survey scans, and of a phase- and frequency-stabilized submillimeter-wave backward-wave oscillator-based radiation source nderline{\textbf{39}}, 831, (2006).} for precision measurements. To illustrate the use of these sources, we present the results of the spectroscopic investigations of the hyperfine structure of $^{129}$Xe Rydberg states in spectral regions where s-d interactions are expected to play a role

Brain Volume Changes after COVID-19 Compared to Healthy Controls by Artificial Intelligence-Based MRI Volumetry.

peer reviewedCohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 (n = 51, MILD), a severe hospitalised course (n = 48, SEV), and healthy controls (n = 56, CTL) all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies

VIBRATIONAL SPECTRA OF CHLOROFORM, FREON-11 AND SELECTED ISOTOPOMERS IN THE TERAHERTZ REGION

Author Institution: Laboratorium fur Physikalische Chemie, ETH-Zurich, 8093 Zurich, SwitzerlandThe fundamental bands of the CCl$_3$ asymmetric deformation modes of selected isotopomers of chloroform (CHCl$_3$) and freon-11 (CFCl$_3$) have been measured between 7 and 8~THz in a static cell at ambient temperature using a laser-based source of tunable radiation in the terahertz region (0.1-10~THz) of the electromagnetic spectrum. Simulation of the rotational contour of the $\nu_6$ fundamental transition of $^{12}$CH$^{35}$Cl$_3$ confirmed previously suggested values for $C_6$ and $C_6\zeta_6$. nderline{\textbf{170}}, 215, (1995).} The fundamental frequencies were derived with a precision of 2~GHz for all compounds except CF$^{35}$Cl$_3$, where the precision amounted to 3~GHz. The frequencies are in agreement with values calculated \textit{ab initio}. Extension of the experimental setup to enable measurement of THz spectra of molecules in supersonic jet expansions and partially resolve their rotational structure is in progress

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN’s diagnostic accuracy and consistency using confusion matrices and McNemar’s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine