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

Predicting Community Evolution in Social Networks

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments

The rapid evolution of technology directly impacts the skills and jobs needed in the next decade. Users can, intentionally or unintentionally, develop different skills by creating, interacting with, and consuming the content from online environments and portals where informal learning can emerge. These environments generate large amounts of data; therefore, big data can have a significant impact on education. Moreover, the educational landscape has been shifting from a focus on contents to a focus on competencies and capabilities that will prepare our society for an unknown future during the 21st century. Therefore, the main goal of this literature survey is to examine diverse technology-mediated environments that can generate rich data sets through the users’ interaction and where data can be used to explicitly or implicitly perform a data-driven evaluation of different competencies and capabilities. We thoroughly and comprehensively surveyed the state of the art to identify and analyse digital environments, the data they are producing and the capabilities they can measure and/or develop. Our survey revealed four key multimedia environments that include sites for content sharing & consumption, video games, online learning and social networks that fulfilled our goal. Moreover, different methods were used to measure a large array of diverse capabilities such as expertise, language proficiency and soft skills. Our results prove the potential of the data from diverse digital environments to support the development of lifelong and lifewide 21st-century capabilities for the future society

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

An exploration of the role of bloggers and blogger characteristics, in the consumer buying process for cosmetics in the Thai market

Consumers often use online information to help them make better buying decisions (Cheung, 2014; Lu et al., 2014). Blogs and bloggers’ opinion can be one of the most important sources of information for consumers evaluating products and services, reducing a consumer’s cognitive effort and uncertainty before making a purchase (Mrazek, 2010). Employing bloggers to spread information on products has become one of the most powerful word-of-mouth strategies for marketers (Kempe, 2003; Sussman, Siegal et al., 2003; Scoble, 2006). This thesis aims to understand how bloggers and social media influence consumers’ decision-making processes and to explore the characteristics of blogs and bloggers in term of trustworthiness and credibility in the environment of the marketing practices for beauty products in the Thai market. Qualitative research methods were used, including online observations and interviews. The interviewees covered 38 Thai women who have experience in using online beauty reviews. This thesis develops the Theory of Planned Behaviour Model (TPB) to understand how beauty bloggers have influenced the consumer’s decision-making process. Moreover, the characteristics of blogs and bloggers in terms of trustworthiness and credibility are explored and explained in relation to the dual processes (central route and peripheral route) from the Elaboration Likelihood Model (ELM). The thesis contributes new knowledge in relation to the content-based factors existing in the central route and their relationship to the peripheral route (sincerity, actual use, expertise, and experience). Therefore, bloggers can show their ability and potential to use a product and criticize its quality through writing, photography, and videos. Furthermore, the analysis found that factors from the Technology Acceptance Model (TAM) could be adapted and applied to explain further the consumer perception of bloggers

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse