Twitter is used for a variety of reasons, including informa- tion dissemination, marketing, political organizing and to spread propaganda, spamming, promotion, conversations, and so on. Characterizing these activities and categoriz- ing associated user generated content is a challenging task. We present a information-theoretic approach to classica- tion of user activity on Twitter. We focus on tweets that contain embedded URLs and study their collective 'retweet- ing' dynamics. We identify two features, time-interval and user entropy, which we use to classify retweeting activity. We achieve good separation of dierent activities using just these two features and are able to categorize content based on the collective user response it generates. We have identi- ed ve distinct categories of retweeting activity on Twitter: automatic robotic activity, newsworthy information dissem- ination, advertising and promotion, campaigns, and para- sitic advertisement. In the course of our investigations, we have shown how Twitter can be exploited for promotional and spam-like activities. The content-independent, entropy- based activity classication method is computationally e- cient, scalable and robust to sampling and missing data. It has many applications, including automatic spam-detection, trend identication, trust management, user-modeling, so- cial search and content classication on online social media.
Most of the existing spam detection @cite_6 and trust management systems @cite_13 are based on content and structure but do not look at collective dynamics. Besides, they usually require additional constraints like labelled up-to-date annotation of resources, access to content and cooperation of search engine. Satisfiability of so many constraints is difficult especially when one takes the diversity and astronomical size of online social media into account. Our method on the other hand, while having no such constraints, may be able to detect spams with an accuracy close to humans.
@cite_13 @cite_6
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Proposing a novel model to detect spam reviews efficiently.Demonstrating the integral role of burst patterns in detection of spam reviews.Comparing the approach with two common methods to show how significant it is. Today's e-commerce is highly depended on increasingly growing online customers' reviews posted in opinion sharing websites. This fact, unfortunately, has tempted spammers to target opinion sharing websites in order to promote and demote products. To date, different types of opinion spam detection methods have been proposed in order to provide reliable resources for customers, manufacturers and researchers. However, supervised approaches suffer from imbalance data due to scarcity of spam reviews in datasets, rating deviation based filtering systems are easily cheated by smart spammers, and content based methods are very expensive and majority of them have not been tested on real data hitherto.The aim of this paper is to propose a robust review spam detection system wherein the rating deviation, content based factors and activeness of reviewers are employed efficiently. To overcome the aforementioned drawbacks, all these factors are synthetically investigated in suspicious time intervals captured from time series of reviews by a pattern recognition technique. The proposed method could be a great asset in online spam filtering systems and could be used in data mining and knowledge discovery tasks as a standalone system to purify product review datasets. These systems can reap benefit from our method in terms of time efficiency and high accuracy. Empirical analyses on real dataset show that the proposed approach is able to successfully detect spam reviews. Comparison with two of the current common methods, indicates that our method is able to achieve higher detection accuracy (F-Score: 0.86) while removing the need for having specific fields of Meta data and reducing heavy computation required for investigation purposes. Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A "flu infection" concept is formulated and used to reduce spurious and potentially confounding features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12 and 28.7 , respectively. The diffusion of fake images and videos on social networks is a fast growing problem. Commercial media editing tools allow anyone to remove, add, or clone people and objects, to generate fake images. Many techniques have been proposed to detect such conventional fakes, but new attacks emerge by the day. Image-to-image translation, based on generative adversarial networks (GANs), appears as one of the most dangerous, as it allows one to modify context and semantics of images in a very realistic way. In this paper, we study the performance of several image forgery detectors against image-to-image translation, both in ideal conditions, and in the presence of compression, routinely performed upon uploading on social networks. The study, carried out on a dataset of 36302 images, shows that detection accuracies up to 95 can be achieved by both conventional and deep learning detectors, but only the latter keep providing a high accuracy, up to 89 , on compressed data. To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95 and a coverage over 90 , which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.