Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM Conference on Web Science, June 30-July 3, 2019, Boston, U

PPAR genomics and pharmacogenomics: Implications for cardiovascular disease

The peroxisome proliferator-activated receptors (PPARs) consist of three related transcription factors that serve to regulate a number of cellular processes that are central to cardiovascular health and disease. Numerous pharmacologic studies have assessed the effects of specific PPAR agonists in clinical trials and have provided insight into the clinical effects of these genes while genetic studies have demonstrated clinical associations between PPAR polymorphisms and abnormal cardiovascular phenotypes. With the abundance of data available from these studies as a background, PPAR pharmacogenetics has become a promising and rapidly advancing field. This review focuses on summarizing the current state of understanding of PPAR genetics and pharmacogenetics and the important implications for the individualization of therapy for patients with cardiovascular diseases

An X-ray/SDSS sample (II): outflowing gas plasma properties

Galaxy-scale outflows are nowadays observed in many active galactic nuclei (AGNs); however, their characterisation in terms of (multi-) phase nature, amount of flowing material, effects on the host galaxy, is still unsettled. In particular, ionized gas mass outflow rate and related energetics are still affected by many sources of uncertainties. In this respect, outflowing gas plasma conditions, being largely unknown, play a crucial role. Taking advantage of the spectroscopic analysis results we obtained studying the X-ray/SDSS sample of 563 AGNs at z $<0.8$ presented in our companion paper, we analyse stacked spectra and sub-samples of sources with high signal-to-noise temperature- and density-sensitive emission lines to derive the plasma properties of the outflowing ionized gas component. For these sources, we also study in detail various diagnostic diagrams to infer information about outflowing gas ionization mechanisms. We derive, for the first time, median values for electron temperature and density of outflowing gas from medium-size samples ($\sim 30$ targets) and stacked spectra of AGNs. Evidences of shock excitation are found for outflowing gas. We measure electron temperatures of the order of $\sim 1.7\times10^4$ K and densities of $\sim 1200$ cm$^{-3}$ for faint and moderately luminous AGNs (intrinsic X-ray luminosity $40.5<log(L_X)<44$ in the 2-10 keV band). We caution that the usually assumed electron density ($N_e=100$ cm$^{-3}$) in ejected material might result in relevant overestimates of flow mass rates and energetics and, as a consequence, of the effects of AGN-driven outflows on the host galaxy.Comment: 16 pages, 10 figures. Accepted for publication in A&

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science Track, Perth, Australia, 3-7 April, 2017

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

Nuclear star formation in the quasar PG1126-041 from adaptive optics assisted spectroscopy

We present adaptive optics assisted spectroscopy of three quasars obtained with NACO at VLT. The high angular resolution achieved with the adaptive optics (~0.08"), joined to the diagnostic power of near-IR spectroscopy, allow us to investigate the properties of the innermost 100 pc of these quasars. In the quasar with the best adaptive optics correction, PG1126-041, we spatially resolve the Pa-alpha emission within the nuclear 100 pc. The comparison with higher excitation lines suggests that the narrow Pa-alpha emission is due to nuclear star formation. The inferred intensity of the nuclear star formation (13 M(sun)/yr) may account for most of the far-IR luminosity observed in this quasar.Comment: 4 pages, 4 figures. Accepted for publication in A&