On the Aggression Diffusion Modeling and Minimization in Twitter

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Estimating Sound Exposure Levels Due to a Broadband Source over Large Areas of Shallow Sea

3D sound propagation modeling in the context of acoustic noise monitoring problems is considered. A technique of effective source spectrum reconstruction from a reference single-hydrophone measurement is discussed, and the procedure of simulation of sound exposure level (SEL) distribution over a large sea area is described. The proposed technique is also used for the modeling of pulse signal waveforms at other receiver locations, and results of a direct comparison with the pulses observed in the experimental data is presented.

Experimental Approach on the Study of Ground-Motion Amplification at the Cerro Prieto Volcano, Mexicali Valley, Baja California, Mexico

We conducted experimental work to explain the large peak ground accelerations observed at the Cerro Prieto volcano in Mexicali Valley, Mexico. Using ambient noise and earthquake data, we compared horizontal-to-vertical spectral ratios (HVSRs) computed for sites on the volcano against those calculated for locations outside it. High-HVSR values (∼11 at ∼2 Hz) were obtained on the top of the volcano at 183 m of altitude, decreasing for sites located at lower elevations. We calculated a median HVSR of ∼1 at 2 Hz from HVSRs computed for nine sites located along an N18°E transect and at an average elevation of ∼25 m. The earlier comparison suggests a relative amplification on the volcano. In addition, we calculated HVSRs from accelerograms generated by 62 earthquakes (2.6≤ML≤5.4; 4.6≤Mw≤7.2) recorded at four locations: two on the volcano (at 194 and 110 m of elevation) and two outside it. These last two sites, located up to 6 km away in a north-northwest and south-southwest direction relative to the volcano, are at an average altitude of 22 m. For the four locations, we also computed the HVSRs from ambient noise data. Although the HVSR results derived from both types of data are slightly different, we also found high HVSRs for the two sites on the volcano and low HVSRs for the two sites outside it, corroborating the relative amplification on the volcano. Using the 1D wave propagation modeling, based on the stiffness matrix method, we modeled the experimental HVSRs to analyze the local site effects. Therefore, we propose that the ground-motion amplification at the Cerro Prieto volcano may be due to a combination of its topography and shallow site effects.

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Propagation modeling of small/big pebbles and air-dry/wet sand environments for wireless sensor networks has not been extensively studied in the 5G frequency band. This study is necessary for the proper coverage planning and efficient operation of wireless sensors in various applications such as monitoring summer sporting activities, and environmental/ground surveillance on coastal pebble/sand environments, or tracking pebble mobility and including the rescue of the flood-type avalanche in Gravel-Bed Rivers. In this study, empirical path loss models are proposed for wireless sensor networks in pebble/sand environments at two discrete frequencies, namely 3.5 and 4.2 GHz. The theoretical models and proposed models are compared to indicate the accuracy of proposed models in predicting the path loss in these environments. Additionally, R-squared and RMSE values of eight different generated models are calculated in the range of 0.931–0.877 and 2.284–2.837, respectively. These comparisons indicate that empirical model parameters have a significant effect on the path loss model.