Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText 2017

A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach. To this end, we build on MaMaDroid, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AuntieDroid and instantiate it by using both automated (Monkey) and user-generated inputs. We find that combining both static and dynamic analysis yields the best performance, with F-measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and, finally, investigate the reasons for inconsistent misclassifications across methods.Accepted manuscrip

Evaluation of ERA-Interim reanalysis precipitation products using England and Wales observations

Precipitation forecast data from the ERA-Interim reanalysis (33 years) are evaluated using the daily England and Wales Precipitation (EWP) observations obtained from a rain gauge network. Observed and reanalysis daily precipitation data are both described well by Weibull distributions with indistinguishable shapes but different scale parameters, such that the reanalysis underestimates the observations by an average factor of 22%. The correlation between the observed and ERA-Interim time series of regional, daily precipitation is 0.91. ERA-Interim also captures the statistics of extreme precipitation including a slightly lower likelihood of the heaviest precipitation events (>15 mm day− 1 for the regional average) than indicated by the Weibull fit. ERA-Interim is also closer to EWP for the high precipitation events. Since these carry weight in longer accumulations, a smaller underestimation of 19% is found for monthly mean precipitation. The partition between convective and stratiform precipitation in the ERA-Interim forecast is also examined. In summer both components contribute equally to the total precipitation amount, while in winter the stratiform precipitation is approximately double convective. These results are expected to be relevant to other regions with low orography on the coast of a continent at the downstream end of mid-latitude stormtracks

Physical factors influencing regional precipitation variability attributed using an airmass trajectory method

A novel Lagrangian framework is developed to attribute monthly precipitation variability to physical processes. Precipitation variability is partitioned into a combination of 5 factors: air mass origin location, origin surface temperature variation, ascent intensity, mass fraction of ascending air and the number of ‘wet’ analysis times per month (> 1 mm/6hrs). Precipitation in a target region is linked to ‘origin’ locations of air masses where the water vapour mixing ratio was last set by boundary layer moistening and is a maximum along back trajectories. Applying the technique to the England and Wales region, the factors together account for 83-89% of the observed summer precipitation variability. The dominant contributor is the number of ‘wet’ analyses, which is shown to be associated with cyclone statistics. The wettest summer months are mainly associated with anomalous cyclone duration rather than the number of cyclones. In addition, surface temperature and saturation humidity at the ‘origin’ locations are found to be below their climatological averages (1979-2013). Therefore the direct thermodynamic effect of anomalous surface temperature on marine boundary layer humidity acts to reduce monthly precipitation anomalies. The decadal precipitation change between phases of the Atlantic Multidecadal Oscillation is approximately 20% of the interannual variability between summer months. Changes in cyclone statistics have an effect six times larger than the direct thermodynamic factor in both monthly and decadal precipitation variability

Understanding the Use of e-Prints on Reddit and 4chan’s Politically Incorrect Board

The dissemination and reach of scientific knowledge have increased at a blistering pace. In this context, e-Print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without waiting for the (lengthy) peer review process. While helping the scientific community in several ways, e-Print servers also provide scientific communicators and the general public with access to a wealth of knowledge without paying hefty subscription fees. This motivates us to study how e-Prints are positioned within Web community discussions. In this paper, we analyze data from two Web communities: 14 years of Reddit data and over 4 from 4chan’s Politically Incorrect board. Our findings highlight the presence of e-Prints in both science-enthusiast and general-audience communities. Real-world events and distinct factors influence the e-Prints people’s discussions; e.g., there was a surge of COVID-19-related research publications during the early months of the outbreak and increased references to e-Prints in online discussions. Text in e-Prints and in online discussions referencing them has a low similarity, suggesting that the latter are not exclusively talking about the findings in the former. Further, our analysis of a sample of threads highlights: 1) misinterpretation and generalization of research findings, 2) early research findings being amplified as a source for future predictions, and 3) questioning findings from a pseudoscientific e-Print. Overall, our work emphasizes the need to quickly and effectively validate non-peer-reviewed e-Prints that get substantial press/social media coverage to help mitigate wrongful interpretations of scientific outputs

Performance Factors in Dinghy Sailing: Laser Class

Despite the relationship between performance and anthropometric characteristics, strength, and endurance in the action of dinghy hiking, there is no equation to predict the position obtained in competition. The purpose of this study was to examine the effects of anthropometric characteristics, strength, and endurance on the performance of the sailor. Twenty-nine male sailors of the Laser class were evaluated according to age, navigation experience, strength and resistance tests in a simulator, body weight, size, sitting height, Body Mass Index (BMI), body fat percentage, trochanteric length, thigh length, tibial length, foot length, abdominal perimeter, and upper thigh perimeter. The results show that the variables were related to performance are age, navigation experience, height, and length of the thigh. The variables that are most related to performance are age and sailing experience. Seventy-six percent of the performance can be estimated using the following equation: 311.971 + (-1.089 x height) + (-1946 x age) + (-1.537 x thigh length). Performance in the Laser class will be determined by the tactics (age and sailing experience) and the morphological characteristics of the sailor (height and sitting height)

Modelling Nonlinear Sequence Generators in terms of Linear Cellular Automata

In this work, a wide family of LFSR-based sequence generators, the so-called Clock-Controlled Shrinking Generators (CCSGs), has been analyzed and identified with a subset of linear Cellular Automata (CA). In fact, a pair of linear models describing the behavior of the CCSGs can be derived. The algorithm that converts a given CCSG into a CA-based linear model is very simple and can be applied to CCSGs in a range of practical interest. The linearity of these cellular models can be advantageously used in two different ways: (a) for the analysis and/or cryptanalysis of the CCSGs and (b) for the reconstruction of the output sequence obtained from this kind of generators.Comment: 15 pages, 0 figure