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

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

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

The Mass-Metallicity and the Fundamental Metallicity Relation revisited on a fully Te-based abundance scale for galaxies

The relationships between stellar mass, gas-phase metallicity and star formation rate (i.e. the Mass-Metallicity, MZR, and the Fundamental Metallcity Relation, FMR) in the local Universe are revisited by fully anchoring the metallicity determination for SDSS galaxies on the Te abundance scale de ned exploiting the strong-line metallicity calibrations presented in Curti et al. (2017). Self-consistent metallicity measurements allow a more unbiased assessment of the scaling relations involving M, Z and SFR, which provide powerful constraints for the chemical evolution models. We paramet-rise the MZR with a new functional form which allows us to better characterise the turnover mass. The slope and saturation metallicity are in good agreement with pre- vious determinations of the MZR based on the Te method, while showing signi cantly lower normalisation compared to those based on photoionisation models. The Z-SFR dependence at xed stellar mass is also investigated, being particularly evident for highly star forming galaxies, where the scatter in metallicity is reduced up to a factor of 30%. A new parametrisation of the FMR is given by explicitly introducing the SFR-dependence of the turnover mass into the MZR. The residual scatter in metal-licity for the global galaxy population around the new FMR is 0:054 dex. The new FMR presented in this work represents a useful local benchmark to compare theor-etical predictions and observational studies (of both local and high-redshift galaxies) whose metallicity measurements are tied to the abundance scale de ned by the Te method, hence allowing to properly assess its evolution with cosmic time.ERC; STF

Regulación de los contratos Joint Venture en la legislación peruana

La presente investigación tiene como problemática la falta de regulación normativa de los contratos Joint Venture, principalmente porque esta ejecución contractual puede solucionar diversos problemas empresariales que suceden entre la competencia y la necesidad de unir fuerzas empresariales para una mejor obtención de resultados es así que se determina que la contratación del Joint Venture dentro de la legislación peruana como una eficaz internacionalizándose a través de una actividad transfronteriza de las empresas internacionales, en donde se puede generar como un mecanismo para poder compartir riesgos extranjeros y locales a través de todo el mundo, pues para ello se requiere tomar en cuenta aspectos metodológicos basados en un tipo aplicado y en un diseño no experimental, ante esto la población cuenta con abogados y jueces especiales en materia civil dentro de la región Lambayeque, llegando a la conclusión que dentro de la legislación peruana, se logró regular bajo la ley general de sociedades los contratos de Joint Venture, con la finalidad de que las empresas cuenten con una mejor estrategia empresarial y flexibilidad económica entre empresas.TesisCiencias jurídica

Fake accounts detection on Twitter

Fake followers are those Twitter accounts created to inflate the number of followers of a target account. Fake followers are dangerous to 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 provide several contributions. First, we review the most relevant existing criteria (proposed by Academia and Media) for anomalous Twitter accounts detection, and later we assess their capability to detect fake followers. In particular, we contribute with the creation of a gold standard of verified human, as well as with a set of known fake accounts. We test the above cited criteria against these two data sets, showing that the analyzed mechanisms provide unsatisfactory performance in revealing fake followers. Moreover, building upon these results, we also introduce a novel taxonomy to discriminate fake followers from legitimate ones and spammers. The findings reported in this paper, other than being supported by a thorough experimental methodology and being interesting on their own, also pave the way for further investigation

A Fake Follower Story: improving fake accounts detection on Twitter

Fake followers are those Twitter accounts created to inflate the number of followers of a target account. Fake followers are dangerous to 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 gold standard of verified human and fake accounts. Then, we exploit the gold standard to train a set of machine-learning classifiers built over the reviewed rules and features. Most of the rules provided by Media provide unsatisfactory performance in revealing fake followers, while features provided by Academia for spam detection result in good performance. Building on the most promising features, we optimise the classifiers both in terms of reduction of overfitting and costs for gathering the data needed to compute the features.<br>The final result is a "Class A" classifier, that is general enough to thwart overfitting and that uses the less costly features, while being able to correctly classify more than 95% of the accounts of the training set.<br>The findings reported in this paper, other than being supported by a thorough experimental methodology and being interesting on their own, also pave the way for further investigatio

A Survey on Computational Propaganda Detection

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.Comment: propaganda detection, disinformation, misinformation, fake news, media bia