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

Never-ending Learning of User Interfaces

Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks. For example, it is possible to guess if a UI element is "tappable" from a screenshot (i.e., based on visual signifiers) or from potentially unreliable metadata (e.g., a view hierarchy), but one way to know for certain is to programmatically tap the UI element and observe the effects. We built the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to discover new and challenging training examples to learn from. The Never-ending UI Learner has crawled for more than 5,000 device-hours, performing over half a million actions on 6,000 apps to train three computer vision models for i) tappability prediction, ii) draggability prediction, and iii) screen similarity

I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website

Advanced Data Mining Techniques for Compound Objects

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data collections. The most important step within the process of KDD is data mining which is concerned with the extraction of the valid patterns. KDD is necessary to analyze the steady growing amount of data caused by the enhanced performance of modern computer systems. However, with the growing amount of data the complexity of data objects increases as well. Modern methods of KDD should therefore examine more complex objects than simple feature vectors to solve real-world KDD applications adequately. Multi-instance and multi-represented objects are two important types of object representations for complex objects. Multi-instance objects consist of a set of object representations that all belong to the same feature space. Multi-represented objects are constructed as a tuple of feature representations where each feature representation belongs to a different feature space. The contribution of this thesis is the development of new KDD methods for the classification and clustering of complex objects. Therefore, the thesis introduces solutions for real-world applications that are based on multi-instance and multi-represented object representations. On the basis of these solutions, it is shown that a more general object representation often provides better results for many relevant KDD applications. The first part of the thesis is concerned with two KDD problems for which employing multi-instance objects provides efficient and effective solutions. The first is the data mining in CAD parts, e.g. the use of hierarchic clustering for the automatic construction of product hierarchies. The introduced solution decomposes a single part into a set of feature vectors and compares them by using a metric on multi-instance objects. Furthermore, multi-step query processing using a novel filter step is employed, enabling the user to efficiently process similarity queries. On the basis of this similarity search system, it is possible to perform several distance based data mining algorithms like the hierarchical clustering algorithm OPTICS to derive product hierarchies. The second important application is the classification and search for complete websites in the world wide web (WWW). A website is a set of HTML-documents that is published by the same person, group or organization and usually serves a common purpose. To perform data mining for websites, the thesis presents several methods to classify websites. After introducing naive methods modelling websites as webpages, two more sophisticated approaches to website classification are introduced. The first approach uses a preprocessing that maps single HTML-documents within each website to so-called page classes. The second approach directly compares websites as sets of word vectors and uses nearest neighbor classification. To search the WWW for new, relevant websites, a focused crawler is introduced that efficiently retrieves relevant websites. This crawler minimizes the number of HTML-documents and increases the accuracy of website retrieval. The second part of the thesis is concerned with the data mining in multi-represented objects. An important example application for this kind of complex objects are proteins that can be represented as a tuple of a protein sequence and a text annotation. To analyze multi-represented objects, a clustering method for multi-represented objects is introduced that is based on the density based clustering algorithm DBSCAN. This method uses all representations that are provided to find a global clustering of the given data objects. However, in many applications there already exists a sophisticated class ontology for the given data objects, e.g. proteins. To map new objects into an ontology a new method for the hierarchical classification of multi-represented objects is described. The system employs the hierarchical structure of the ontology to efficiently classify new proteins, using support vector machines

Developing a Sign Language Video Collection via Metadata and Video Classifiers

Video sharing sites have become a central tool for the storage and dissemination of sign language content. Sign language videos have many purposes, including sharing experiences or opinions, teaching and practicing a sign language, etc. However, due to limitations of term-based search, these videos can be hard to locate. This results in a diminished value of these sites for the deaf or hard-of-hearing community. As a result, members of the community frequently engage in a push-style delivery of content, sharing direct links to sign language videos with other members of the sign language community. To address this problem, we propose the Sign Language Digital Library (SLaDL). SLaDL is composed of two main sub-systems, a crawler that collects potential videos for inclusion into the digital library corpus, and an automatic classification system that detects and identifies sign language presence in the crawled videos. These components attempt to filter out videos that do not include sign language from the collection and to organize sign language videos based on different languages. This dissertation explores individual and combined components of the classification system. The components form a cascade of multimodal classifiers aimed at achieving high accuracy when classifying potential videos while minimizing the computational effort. A web application coordinates the execution of these two subsystems and enables user interaction (browsing and searching) with the library corpus. Since the collection of the digital library is automatically curated by the cascading classifier, the number of irrelevant results is expected to be drastically lower when compared to general-purpose video sharing sites. iii Video sharing sites have become a central tool for the storage and dissemination of sign language content. Sign language videos have many purposes, including sharing experiences or opinions, teaching and practicing a sign language, etc. However, due to limitations of term-based search, these videos can be hard to locate. This results in a diminished value of these sites for the deaf or hard-of-hearing community. As a result, members of the community frequently engage in a push-style delivery of content, sharing direct links to sign language videos with other members of the sign language community. To address this problem, we propose the Sign Language Digital Library (SLaDL). SLaDL is composed of two main sub-systems, a crawler that collects potential videos for inclusion into the digital library corpus, and an automatic classification system that detects and identifies sign language presence in the crawled videos. These components attempt to filter out videos that do not include sign language from the collection and to organize sign language videos based on different languages. This dissertation explores individual and combined components of the classification system. The components form a cascade of multimodal classifiers aimed at achieving high accuracy when classifying potential videos while minimizing the computational effort. A web application coordinates the execution of these two subsystems and enables user interaction (browsing and searching) with the library corpus. Since the collection of the digital library is automatically curated by the cascading classifier, the number of irrelevant results is expected to be drastically lower when compared to general-purpose video sharing sites. The evaluation involved a series of experiments focused on specific components of the system, and on analyzing how to best configure SLaDL. In the first set of experiments, we investigated three different crawling approaches, assessing how they compared in terms of both finding a large quantity of sign language videos and expanding the variety of videos in the collection. Secondly, we evaluated the performance of different approaches to multimodal classification in terms of precision, recall, F1 score, and computational costs. Lastly, we incorporated the best multimodal approach into cascading classifiers to reduce computation while preserving accuracy. We experimented with four different cascading configurations and analyzed their performance for the detection and identification of signed content. Given our findings of each experiment, we proposed the set up for an instantiation of SLaDL