Detecting and Characterizing Political Incivility on Social Media

Researchers of political communication study the impact and perceptions of political incivility on social media. Yet, so far, relatively few works attempted to automatically detect and characterize political incivility. In our work, we study political incivility in Twitter, presenting several research contributions. First, we present state-of-the-art incivility detection results using a large dataset, which we collected and labeled via crowd sourcing. Importantly, we distinguish between uncivil political speech that is impolite and intolerant anti-democratic discourse. Applying political incivility detection at large-scale, we derive insights regarding the prevalence of this phenomenon across users, and explore the network characteristics of users who are susceptible to disseminating uncivil political content online. Finally, we propose an approach for modeling social context information about the tweet author alongside the tweet content, showing that this leads to significantly improved performance on the task of political incivility detection. This result holds promise for related tasks, such as hate speech and stance detection

Motional Broadening in Ensembles With Heavy-Tail Frequency Distribution

We show that the spectrum of an ensemble of two-level systems can be broadened through `resetting' discrete fluctuations, in contrast to the well-known motional-narrowing effect. We establish that the condition for the onset of motional broadening is that the ensemble frequency distribution has heavy tails with a diverging first moment. We find that the asymptotic motional-broadened lineshape is a Lorentzian, and derive an expression for its width. We explain why motional broadening persists up to some fluctuation rate, even when there is a physical upper cutoff to the frequency distribution.Comment: 6 pages, 4 figure

GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering

Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation. While such systems can often be described as state space (SS) models, tracking graph signals via conventional tools based on the Kalman filter (KF) and its variants is typically challenging. This is due to the nonlinearity, high dimensionality, irregularity of the domain, and complex modeling associated with real-world dynamic systems of graph signals. In this work, we study the tracking of graph signals using a hybrid model-based/data-driven approach. We develop the GSP-KalmanNet, which tracks the hidden graphical states from the graphical measurements by jointly leveraging graph signal processing (GSP) tools and deep learning (DL) techniques. The derivations of the GSP-KalmanNet are based on extending the KF to exploit the inherent graph structure via graph frequency domain filtering, which considerably simplifies the computational complexity entailed in processing high-dimensional signals and increases the robustness to small topology changes. Then, we use data to learn the Kalman gain following the recently proposed KalmanNet framework, which copes with partial and approximated modeling, without forcing a specific model over the noise statistics. Our empirical results demonstrate that the proposed GSP-KalmanNet achieves enhanced accuracy and run time performance as well as improved robustness to model misspecifications compared with both model-based and data-driven benchmarks.Comment: Submitted for possible publication in the IEE

Precursors prior to Type IIn supernova explosions are common: precursor rates, properties, and correlations

There is a growing number of supernovae (SNe), mainly of Type IIn, which present an outburst prior to their presumably final explosion. These precursors may affect the SN display, and are likely related to some poorly charted phenomena in the final stages of stellar evolution. Here we present a sample of 16 SNe IIn for which we have Palomar Transient Factory (PTF) observations obtained prior to the SN explosion. By coadding these images taken prior to the explosion in time bins, we search for precursor events. We find five Type IIn SNe that likely have at least one possible precursor event, three of which are reported here for the first time. For each SN we calculate the control time. Based on this analysis we find that precursor events among SNe IIn are common: at the one-sided 99% confidence level, more than 50% of SNe IIn have at least one pre-explosion outburst that is brighter than absolute magnitude -14, taking place up to 1/3 yr prior to the SN explosion. The average rate of such precursor events during the year prior to the SN explosion is likely larger than one per year, and fainter precursors are possibly even more common. We also find possible correlations between the integrated luminosity of the precursor, and the SN total radiated energy, peak luminosity, and rise time. These correlations are expected if the precursors are mass-ejection events, and the early-time light curve of these SNe is powered by interaction of the SN shock and ejecta with optically thick circumstellar material.Comment: 15 pages, 20 figures, submitted to Ap