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

Calendars, rituals, and astral science in India: a case-study

This paper analyses the complex variety that characterises the Indian calendric system and its relation to culture, history, and society. The aim is to understand the role played in contemporary India by traditional knowledge of astral science. For this purpose, I shall investigate the information provided by a modern Hindi pañcāṅga. This denotes a traditional almanac that goes back to a well-established practice of calendar making attested in Sanskrit literature. In medieval India, the pañcāṅga forecasted celestial phenomena such as the weather and solar eclipses and was commonly used to establish the dates for religious festivals, to know auspicious moments to undertake activities such as trading, marriage, traveling, and to set up the performance of vratas (religious ceremonies) and saṃskāras (Hindu initiation rituals at important occasions of life). Different versions of pañcāṅgas are published nowadays in all the Indian regional languages and even in English by jyotiṣīs or ‘astrologers’. Since early times, festivals, rituals, and religious ceremonies have been marked in India lunar months and the passage of the seasons. The present paper shows that astrology still plays a major part in every sphere of human life and that, in the course of a month, the changing phases of the moon coincide with the ritual observance of ancient religious practices

Team automata for security analysis

We show that team automata (TA) are well suited for security analysis by reformulating the Generalized Non-Deducibility on Compositions (GNDC) schema in terms of TA. We then use this to show that integrity is guaranteed for a case study in which TA model an instance of the Efficient Multi-chained Stream Signature (EMSS) protocol

A project for an ERP study in the expression of surprise and surprise disapproval

In these pages, I present a project for an ERP study aiming to investigate the neural processing underlying the expression of surprise and surprise-disapproval, with special reference to gesture. So far, electrophysiological investigations dealt with the semantic (in)congruence between gesture and speech, the influence of gesture in disambiguation and the effect of the context on the processing of gesture and speech. Not much is known on non-iconic gestures, and still less on how the different types of gestures affect linguistic-gesture integration. Studies like the one I am presenting here could be useful in contributing to the detailed neural processing of gestures. My project aims to understand how the brain works when it has to process a special type of gestures, namely those gestures that are not aligned with the onset of a target word and do not specifically refer to any word in the sentence. These gestures take their meaning from the (emotional) context they rely on, whereas they do not show a correspondence with the semantics of the linguistic expression associated with them. In fact, these gestures turned out to be regularly aligned with syntax (and not with the onset of a specific target word) in that they usually are realized in correspondence with the nuclear syllable of the verbal form and/or with the negation in case of yes/no counter-expectational surprise questions and with negation and/or with the wh-phrase in case of wh- surprise-disapproval questions

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

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