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

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

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