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

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

Fame for sale: efficient detection of fake Twitter followers

$\textit{Fake followers}$ are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere - hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel $\textit{Class A}$ classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers

DNA-inspired online behavioral modeling and its application to spambot detection

We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks

Effects of Vertical Seismic Accelerations on Slope Displacements

The behaviour of earth slopes under seismic conditions can be effectively studied by evaluating the suffered displacements, which are key parameters in the recent performance design approaches (Pianc/PTCII/WG34, 2001). Different methodologies have been developed to evaluate displacements (Whitman, 1993). Slope instabilities caused by yielding due to seismic inertial forces can be studied by methods based on the well-known model of a rigid block sliding on a plane surface (Newmark 1965). These methodologies utilise accelerometric time histories which allow to take into account the real characteristics of the seismic motion (Simonelli and Viggiani 1995). Displacement analyses are traditionally performed adopting only horizontal accelerograms. The aim of this paper is to evaluate the effects of vertical components of seismic motions. In this case, the critical acceleration depends on the direction of the resulting motion (Sarma 1975, 1999). The analyses have been performed for three major Italian earthquakes. The case of an indefinite slope in dry cohesionless soil has been examined. The results of the analyses have been synthesised in diagrams, which show that the displacement variations induced by vertical accelerations are negligible for the range of displacements values (of the order of centimetres), which are of interest from an engineering point of view

Le strutture cognitive della comunicazione politica

I linguaggi politici nella modernità hanno subito, ancor più fortemente di altri, l’emergere della dimensione multimediale frutto dello sviluppo tecnologico. Il codice propriamente verbale-linguistico riveste ormai solo una parte del nucleo significativo dell’accadere politico. La comunicazione politica, ora deve comprendere qualsiasi evento in grado di essere trasmesso e quindi guardato da un soggetto/elettore. Servono dunque strumenti concettuali diversi. Proprio perché la comunicazione politica comprende ogni “manifestazione ostensiva”, essa deve comprendere una visione dell’uomo e del suo stare al mondo moderna e coerente che ci permetta di capire il più possibile le dinamiche attraverso cui l’uomo dà senso e significato a un messaggio, non in quanto enunciato linguistico ma in quanto “segno”, qualsiasi sia la sua dimensione contestuale. Il progetto di ricerca consiste in tre parti principali. Nella prima si analizza il paradigma della mente descritto dalle scienze cognitive di recente formazione creando le basi per l’analisi dei fenomeni appartenenti a ciò che oggi è da ritenersi comunicazione politica operando una integrazione tra approcci diversi in una prospettiva integrata. Nella seconda parte si svolge un’analisi estensiva delle elezioni politiche del 2012-2013 mettendo in luce le strutture ricorrenti che rappresentano una particolare identità soprattutto analizzando l’uso dell’immagine e di un linguaggio sincretico di tipo metaforico che ben si applica ai mezzi di comunicazione più moderni che in tempi recenti rivestono un’importanza sempre maggiore. Nella terza parte si traggono le conclusioni mostrando i risultati dell’analisi svolta e proponendo futuri sviluppi nel campo di ricerca in oggetto

New stratigraphical data on the Middle-Late Jurassic biosiliceous sediments from the Sicanian basin, Western Sicily (Italy)

The reported data present the stratigraphy of several sections across a Middle–Late Jurassic Radiolaritic Unit, well exposed in different thrust sheets pertaining to the Maghrebian chain of Southwestern Sicily. The aim was to define the chronostratigraphical distribution of the Jurassic biosiliceous sedi- mentation in the Sicanian palaeogeographical zone, a deep water basin belonging to the Southern Tethys continental margin. The radiolarian biostratigraphy indicates that the switching from carbonate to siliceous sedimentation in the Sicanian Basin is referable to the Bajocian, as shown by the section of Campofiorito, near Corleone. The biostratigraphical dataset allows the correlation between the onset of biosiliceous sedimentation and the fall of biodiversity in the Sicanian basin with the carbonate productivity crisis, indicated by the highest eutrophication that affected Western Tethys during Middle Jurassic times

Strong Dependencies between Software Components

Component-based systems often describe context requirements in terms of explicit inter-component dependencies. Studying large instances of such systems?such as free and open source software (FOSS) distributions?in terms of declared dependencies between packages is appealing. It is however also misleading when the language to express dependencies is as expressive as boolean formulae, which is often the case. In such settings, a more appropriate notion of component dependency exists: strong dependency. This paper introduces such notion as a first step towards modeling semantic, rather then syntactic, inter-component relationships. Furthermore, a notion of component sensitivity is derived from strong dependencies, with ap- plications to quality assurance and to the evaluation of upgrade risks. An empirical study of strong dependencies and sensitivity is presented, in the context of one of the largest, freely available, component-based system