134 research outputs found

    Detecting Sockpuppets in Deceptive Opinion Spam

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    This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.Comment: 18 pages, Accepted at CICLing 2017, 18th International Conference on Intelligent Text Processing and Computational Linguistic

    ID8.12 Network Management Tool API

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    Internal Deliverable 8.12. Description of API for the network management toolThe work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

    An Army of Me: Sockpuppets in Online Discussion Communities

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    In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

    Expert System for Auditing Data Communications

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    An expert system for auditing data communications was developed. This system assists an auditor in performing an Electronic Data Processing (EDP) audit of a data communication network. The expert system has several features specific to the auditing profession. These features include documentation of work performed, presentation of preliminary . findings, drafting the audit report, and determination of the audit opinion. A copy of the expert system developed for this study is available through the Computing and Information Science Department of Oklahoma State University.Computing and Information Science

    SRC Model to Identify Beguiling Reviews

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    Today, e-trade sites are giving colossal number of a platform to clients in which they can express their perspectives,  their suppositions and post their audits about the items on the web. Such substance helped by clients is accessible for different clients and makers as a significant wellspring of data.  This data is useful in taking imperative business choices.  Despite the fact that this data impact the purchasing choice of a client, however quality control on this client created information is not guaranteed, as audit area is an open stage accessible to all. anybody  can  compose  anything  on  web  which may incorporate surveys which are not true. as the prevalence of e-commerce destinations are hugely expanding, nature of the surveys is deteriorating step by step subsequently influencing clients’ purchasing choices. This has turned into an enormous social issue.  From numerous years, email spam and web spam were the two primary highlighted social issues. at the same time these days, because of notoriety of clients’ enthusiasm toward internet shopping and their reliance on the online audits, it turned into a real focus for audit spammers to delude clients by composing sham surveys for target items. To the best of our insight, very little study is accounted for in regards to this issue reliability of online reviews. To begin with paper was distributed in 2007 by NITIN  JINDAL  &  BING  LIU in regards to  review Spam detection.  In the past few years, variety of techniques has been recommended by researchers to accord with this trouble. This paper intends to introduce Suspicious review Classifier model (SrC) for identifying suspicious review, review spammers and their group

    Classifying Dialogue Acts in Multi-party Live Chats

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    MongoDB Incidence Response

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    NoSQL (Not only SQL) databases have been gaining some popularity over the last few years. Such big companies as Expedia, Shutterfly, MetLife, and Forbes use NoSQL databases to manage data on different projects. These databases can contain a variety of information ranging from nonproprietary data to personally identifiable information like social security numbers. Databases run the risk of cyber intrusion at all times. This paper gives a brief explanation of NoSQL and thoroughly explains a method of Incidence Response with MongoDB, a NoSQL database provider. This method involves an automated process with a new self-built software tool that analyzing MongoDB audit log\u27s and generates an html page with indicators to show possible intrusions and activities on the instance of MongoDB. When dealing with NoSQL databases there is a lot more to consider than with the traditional RDMS\u27s, and since there is not a lot of out of the box support forensics tools can be very helpful
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