197 research outputs found

    ๊ฐœ์ธ ์‚ฌํšŒ๋ง ๋„คํŠธ์›Œํฌ ๋ถ„์„ ๊ธฐ๋ฐ˜ ์˜จ๋ผ์ธ ์‚ฌํšŒ ๊ณต๊ฒฉ์ž ํƒ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ๊น€์ข…๊ถŒ.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits โ€“ for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.์ตœ๊ทผ ์šฐ๋ฆฌ๋Š” Facebook, Twitter, Weibo, LinkedIn ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์„ฑ์žฅํ•˜๋Š” ํ˜„์ƒ์„ ๋ชฉ๊ฒฉํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๊ฐ€ ๊ฐœ์ธ๊ณผ ๊ฐœ์ธ๊ฐ„์˜ ๊ด€๊ณ„ ๋ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ ํ˜•์„ฑ๊ณผ ๋‰ด์Šค ์ „ํŒŒ ๋“ฑ์˜ ์—ฌ๋Ÿฌ ์ด์ ์„ ์ œ๊ณตํ•ด ์ฃผ๊ณ  ์žˆ๋Š”๋ฐ ๋ฐ˜ํ•ด ๋ฐ˜๊ฐ‘์ง€ ์•Š์€ ํ˜„์ƒ ์—ญ์‹œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ŠคํŒจ๋จธ๋“ค์€ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๋ฅผ ๋™๋ ฅ ์‚ผ์•„ ์ŠคํŒธ์„ ๋งค์šฐ ๋น ๋ฅด๊ณ  ๋„“๊ฒŒ ์ „ํŒŒํ•˜๋Š” ์‹์œผ๋กœ ์•…์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์ŠคํŒธ์€ ์ˆ˜์‹ ์ž๊ฐ€ ์›์น˜ ์•Š๋Š” ๋ฉ”์‹œ์ง€๋“ค์„ ์ผ์ปฝ๋Š”๋ฐ ์ด๋Š” ์„œ๋น„์Šค์˜ ์‹ ๋ขฐ๋„์™€ ์•ˆ์ •์„ฑ์„ ํฌ๊ฒŒ ์†์ƒ์‹œํ‚จ๋‹ค. ๋”ฐ๋ผ์„œ, ์ŠคํŒจ๋จธ๋ฅผ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ํ˜„์žฌ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ๋งค์šฐ ๊ธด๊ธ‰ํ•˜๊ณ  ์ค‘์š”ํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ‘œ์ ์ธ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๋“ค ์ค‘ Twitter์™€ Weibo์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ŠคํŒจ๋ฐ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์œ ํ˜•์˜ ์ŠคํŒจ๋ฐ๋“ค์€ ๋ถˆํŠน์ • ๋‹ค์ˆ˜์—๊ฒŒ ๋ฉ”์‹œ์ง€๋ฅผ ์ „ํŒŒํ•˜๋Š” ๋Œ€์‹ ์—, ๋งŽ์€ ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž๋“ค์„ 'ํŒ”๋กœ์šฐ(๊ตฌ๋…)'ํ•˜๊ณ  ์ด๋“ค๋กœ๋ถ€ํ„ฐ '๋งž ํŒ”๋กœ์ž‰(๋งž ๊ตฌ๋…)'์„ ์ด๋Œ์–ด ๋‚ด๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๋•Œ๋กœ๋Š” link farm์„ ์ด์šฉํ•ด ํŠน์ • ๊ณ„์ •์˜ ํŒ”๋กœ์›Œ ์ˆ˜๋ฅผ ๋†’์ด๊ณ  ๋ช…์‹œ์  ์˜ํ–ฅ๋ ฅ์„ ์ฆ๊ฐ€์‹œํ‚ค๊ธฐ๋„ ํ•œ๋‹ค. ์ŠคํŒจ๋จธ์˜ ์˜จ๋ผ์ธ ๊ด€๊ณ„๋ง์ด ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž์˜ ์˜จ๋ผ์ธ ์‚ฌํšŒ๋ง๊ณผ ๋‹ค๋ฅผ ๊ฒƒ์ด๋ผ๋Š” ๊ฐ€์ • ํ•˜์—, ๋‚˜๋Š” ์ŠคํŒจ๋จธ๋“ค์„ ํฌํ•จํ•œ ์ผ๋ฐ˜์ ์ธ ์˜จ๋ผ์ธ ์‚ฌํšŒ๋ง ๊ณต๊ฒฉ์ž๋“ค์„ ํƒ์ง€ํ•˜๋Š” ๋ถ„๋ฅ˜ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋‚˜๋Š” ๋จผ์ € ๊ฐœ์ธ ์‚ฌํšŒ๋ง ๋‚ด ์‚ฌํšŒ ๊ด€๊ณ„์— ์ฃผ๋ชฉํ•˜๊ณ  ๋‘ ๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ๋ถ„๋ฅ˜ ํŠน์„ฑ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋“ค์€ ๊ฐœ์ธ ์‚ฌํšŒ๋ง์˜ Triad Significance Profile (TSP)์— ๊ธฐ๋ฐ˜ํ•œ ๊ตฌ์กฐ์  ํŠน์„ฑ๊ณผ Hierarchical homophily์— ๊ธฐ๋ฐ˜ํ•œ ๊ด€๊ณ„ ์˜๋ฏธ์  ํŠน์„ฑ์ด๋‹ค. ์‹ค์ œ Twitter์™€ Weibo ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์ด ๋งค์šฐ ์‹ค์šฉ์ ์ด๋ผ๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ œ์•ˆํ•œ ํŠน์„ฑ๋“ค์€ ์ „์ฒด ๋„คํŠธ์›Œํฌ๋ฅผ ๋ถ„์„ํ•˜์ง€ ์•Š์•„๋„ ๊ฐœ์ธ ์‚ฌํšŒ๋ง๋งŒ ๋ถ„์„ํ•˜๋ฉด ๋˜๊ธฐ ๋•Œ๋ฌธ์— scalableํ•˜๊ฒŒ ์ธก์ •๋  ์ˆ˜ ์žˆ๋‹ค. ๋‚˜์˜ ์„ฑ๋Šฅ ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์ด ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ๋น„ํ•ด true positive์™€ false positive ์ธก๋ฉด์—์„œ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค.1 Introduction 1 2 Related Work 6 2.1 OSN Spammer Detection Approaches 6 2.1.1 Contents-based Approach 6 2.1.2 Social Network-based Approach 7 2.1.3 Subnetwork-based Approach 8 2.1.4 Behavior-based Approach 9 2.2 Link Spam Detection 10 2.3 Data mining schemes for Spammer Detection 10 2.4 Sybil Detection 12 3 Triad Significance Profile Analysis 14 3.1 Motivation 14 3.2 Twitter Dataset 18 3.3 Indegree and Outdegree of Dataset 20 3.4 Twitter spammer Detection with TSP 22 3.5 TSP-Filtering 27 3.6 Performance Evaluation of TSP-Filtering 29 4 Hierarchical Homophily Analysis 33 4.1 Motivation 33 4.2 Hierarchical Homophily in OSN 37 4.2.1 Basic Analysis of Datasets 39 4.2.2 Status gap distribution and Assortativity 44 4.2.3 Hierarchical gap distribution 49 4.3 Performance Evaluation of HH-Filtering 53 5 Overall Performance Evaluation 58 6 Conclusion 63 Bibliography 65Docto

    Social spammer detection: A multi-relational embedding approach

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    ยฉ Springer International Publishing AG, part of Springer Nature 2018. Since the relation is the main data shape of social networks, social spammer detection desperately needs a relation-dependent but content-independent framework. Some recent detection method transforms the social relations into a set of topological features, such as degree, k-core, etc. However, the multiple heterogeneous relations and the direction within each relation have not been fully explored for identifying social spammers. In this paper, we make an attempt to adopt the Multi-Relational Embedding (MRE) approach for learning latent features of the social network. The MRE model is able to fuse multiple kinds of different relations and also learn two latent vectors for each relation indicating both sending role and receiving role of every user, respectively. Experimental results on a real-world multi-relational social network demonstrate the latent features extracted by our MRE model can improve the detection performance remarkably

    Man vs machine โ€“ Detecting deception in online reviews

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    This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material

    Seminar Users in the Arabic Twitter Sphere

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    We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

    Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

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    This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and othersโ€™ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1

    Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

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    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

    From past to present: spam detection and identifying opinion leaders in social networks

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    On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.This work is supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) through grant number 118E315 and grant number 120E187. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of TUBITAK.Publisher's VersionEmerging Sources Citation Index (ESCI)Q4WOS:00080858480001

    Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

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    Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intents. Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection
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