197 research outputs found
๊ฐ์ธ ์ฌํ๋ง ๋คํธ์ํฌ ๋ถ์ ๊ธฐ๋ฐ ์จ๋ผ์ธ ์ฌํ ๊ณต๊ฒฉ์ ํ์ง
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ๊น์ข
๊ถ.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.์ต๊ทผ ์ฐ๋ฆฌ๋ Facebook, Twitter, Weibo, LinkedIn ๋ฑ์ ๋ค์ํ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ํญ๋ฐ์ ์ผ๋ก ์ฑ์ฅํ๋ ํ์์ ๋ชฉ๊ฒฉํ์๋ค. ํ์ง๋ง ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ๊ฐ์ธ๊ณผ ๊ฐ์ธ๊ฐ์ ๊ด๊ณ ๋ฐ ์ปค๋ฎค๋ํฐ ํ์ฑ๊ณผ ๋ด์ค ์ ํ ๋ฑ์ ์ฌ๋ฌ ์ด์ ์ ์ ๊ณตํด ์ฃผ๊ณ ์๋๋ฐ ๋ฐํด ๋ฐ๊ฐ์ง ์์ ํ์ ์ญ์ ๋ฐ์ํ๊ณ ์๋ค. ์คํจ๋จธ๋ค์ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ฅผ ๋๋ ฅ ์ผ์ ์คํธ์ ๋งค์ฐ ๋น ๋ฅด๊ณ ๋๊ฒ ์ ํํ๋ ์์ผ๋ก ์
์ฉํ๊ณ ์๋ค. ์คํธ์ ์์ ์๊ฐ ์์น ์๋ ๋ฉ์์ง๋ค์ ์ผ์ปฝ๋๋ฐ ์ด๋ ์๋น์ค์ ์ ๋ขฐ๋์ ์์ ์ฑ์ ํฌ๊ฒ ์์์ํจ๋ค. ๋ฐ๋ผ์, ์คํจ๋จธ๋ฅผ ํ์งํ๋ ๊ฒ์ด ํ์ฌ ์์
๋ฏธ๋์ด์์ ๋งค์ฐ ๊ธด๊ธํ๊ณ ์ค์ํ ๋ฌธ์ ๊ฐ ๋์๋ค. ์ด ๋
ผ๋ฌธ์ ๋ํ์ ์ธ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ค ์ค Twitter์ Weibo์์ ๋ฐ์ํ๋ ์คํจ๋ฐ์ ๋ค๋ฃจ๊ณ ์๋ค. ์ด๋ฌํ ์ ํ์ ์คํจ๋ฐ๋ค์ ๋ถํน์ ๋ค์์๊ฒ ๋ฉ์์ง๋ฅผ ์ ํํ๋ ๋์ ์, ๋ง์ ์ผ๋ฐ ์ฌ์ฉ์๋ค์ 'ํ๋ก์ฐ(๊ตฌ๋
)'ํ๊ณ ์ด๋ค๋ก๋ถํฐ '๋ง ํ๋ก์(๋ง ๊ตฌ๋
)'์ ์ด๋์ด ๋ด๋ ๊ฒ์ ๋ชฉ์ ์ผ๋ก ํ๊ธฐ๋ ํ๋ค. ๋๋ก๋ link farm์ ์ด์ฉํด ํน์ ๊ณ์ ์ ํ๋ก์ ์๋ฅผ ๋์ด๊ณ ๋ช
์์ ์ํฅ๋ ฅ์ ์ฆ๊ฐ์ํค๊ธฐ๋ ํ๋ค. ์คํจ๋จธ์ ์จ๋ผ์ธ ๊ด๊ณ๋ง์ด ์ผ๋ฐ ์ฌ์ฉ์์ ์จ๋ผ์ธ ์ฌํ๋ง๊ณผ ๋ค๋ฅผ ๊ฒ์ด๋ผ๋ ๊ฐ์ ํ์, ๋๋ ์คํจ๋จธ๋ค์ ํฌํจํ ์ผ๋ฐ์ ์ธ ์จ๋ผ์ธ ์ฌํ๋ง ๊ณต๊ฒฉ์๋ค์ ํ์งํ๋ ๋ถ๋ฅ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ๋๋ ๋จผ์ ๊ฐ์ธ ์ฌํ๋ง ๋ด ์ฌํ ๊ด๊ณ์ ์ฃผ๋ชฉํ๊ณ ๋ ๊ฐ์ง ์ข
๋ฅ์ ๋ถ๋ฅ ํน์ฑ์ ์ ์ํ์๋ค. ์ด๋ค์ ๊ฐ์ธ ์ฌํ๋ง์ Triad Significance Profile (TSP)์ ๊ธฐ๋ฐํ ๊ตฌ์กฐ์ ํน์ฑ๊ณผ Hierarchical homophily์ ๊ธฐ๋ฐํ ๊ด๊ณ ์๋ฏธ์ ํน์ฑ์ด๋ค. ์ค์ Twitter์ Weibo ๋ฐ์ดํฐ์
์ ๋ํ ์คํ ๊ฒฐ๊ณผ๋ ์ ์ํ ๋ฐฉ๋ฒ์ด ๋งค์ฐ ์ค์ฉ์ ์ด๋ผ๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค. ์ ์ํ ํน์ฑ๋ค์ ์ ์ฒด ๋คํธ์ํฌ๋ฅผ ๋ถ์ํ์ง ์์๋ ๊ฐ์ธ ์ฌํ๋ง๋ง ๋ถ์ํ๋ฉด ๋๊ธฐ ๋๋ฌธ์ scalableํ๊ฒ ์ธก์ ๋ ์ ์๋ค. ๋์ ์ฑ๋ฅ ๋ถ์ ๊ฒฐ๊ณผ๋ ์ ์ํ ๊ธฐ๋ฒ์ด ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๋นํด true positive์ false positive ์ธก๋ฉด์์ ์ฐ์ํ๋ค๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค.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
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
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
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
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
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
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
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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