Detecting Social Spamming on Facebook Platform

Tänapäeval toimub väga suur osa kommunikatsioonist elektroonilistes suhtlusvõrgustikes. Ühest küljest lihtsustab see omavahelist suhtlemist ja uudiste levimist, teisest küljest loob see ideaalse pinnase sotsiaalse rämpsposti levikuks. Rohkem kui kahe miljardi kasutajaga Facebooki platvorm on hetkel rämpsposti levitajate üks põhilisi sihtmärke. Platvormi kasutajad puutuvad igapäevaselt kokku ohtude ja ebameeldivustega nagu pahavara levitavad lingid, vulgaarsused, vihakõned, kättemaksuks levitatav porno ja muu. Kuigi uurijad on esitanud erinevaid tehnikaid sotsiaalmeedias rämpspostituste vähendamiseks, on neid rakendatud eelkõige Twitteri platvormil ja vaid vähesed on seda teinud Facebookis. Pidevalt arenevate rämpspostitusmeetoditega võitlemiseks tuleb välja töötada järjest uusi rämpsposti avastamise viise. Käesolev magistritöö keskendub Facebook platvormile, kuhu on lõputöö raames paigutatud kümme „meepurki” (ingl honeypot), mille abil määratakse kindlaks väljakutsed rämpsposti tuvastamisel, et pakkuda tõhusamaid lahendusi. Kasutades kõiki sisendeid, kaasa arvatud varem mujal sotsiaalmeedias testitud meetodid ja informatsioon „meepurkidest”, luuakse andmekaeve ja masinõppe meetoditele tuginedes klassifikaator, mis suudab eristada rämpspostitaja profiili tavakasutaja profiilist. Nimetatu saavutamiseks vaadeldakse esmalt peamisi väljakutseid ja piiranguid rämpsposti tuvastamisel ning esitletakse varasemalt tehtud uuringuid koos tulemustega. Seejärel kirjeldatakse rakenduslikku protsessi, alustades „meepurgi” ehitusest, andmete kogumisest ja ettevalmistamisest kuni klassifikaatori ehitamiseni. Lõpuks esitatakse „meepurkidelt” saadud vaatlusandmed koos klassifikaatori tulemustega ning võrreldakse neid uurimistöödega teiste sotsiaalmeedia platvormide kohta. Selle lõputöö peamine panus on klassifikaator, mis suudab eristada Facebooki kasutaja profiilid spämmerite omast. Selle lõputöö originaalsus seisneb eesmärgis avastada erinevat sotsiaalset spämmi, mitte ainult pahavara levitajaid vaid ka neid, kes levitavad roppust, massiliselt sõnumeid, heakskiitmata sisu jne.OSNs (Online Social Networks) are dominating the human interaction nowadays, easing the communication and spreading of news on one hand and providing a global fertile soil to grow all different kinds of social spamming, on the other. Facebook platform, with its 2 billions current active users, is currently on the top of the spammers' targets. Its users are facing different kind of social threats everyday, including malicious links, profanity, hate speech, revenge porn and others. Although many researchers have presented their different techniques to defeat spam on social media, specially on Twitter platform, very few have targeted Facebook's.To fight the continuously evolving spam techniques, we have to constantly develop and enhance the spam detection methods. This research digs deeper in the Facebook platform, through 10 implemented honeypots, to state the challenges that slow the spam detection process, and ways to overcome it. Using all the given inputs, including the previous techniques tested on other social medias along with observations driven from the honeypots, the final product is a classifier that distinguish the spammer profiles from legitimate ones through data mining and machine learning techniques. To achieve this, the research first overviews the main challenges and limitations that obstruct the spam detection process, and presents the related researches with their results. It then, outlines the implementation steps, from the honeypot construction step, passing through the data collection and preparation and ending by building the classifier itself. Finally, it presents the observations driven from the honeypot and the results from the classifier and validates it against the results from previous researches on different social platforms. The main contribution of this thesis is the end classifier which will be able to distinguish between the legitimate Facebook profiles and the spammer ones. The originality of the research lies in its aim to detect all kind of social spammers, not only the spreading-malware spammers, but also spamming in its general context, e.g. the ones spreading profanity, bulk messages and unapproved contents

개인 사회망 네트워크 분석 기반 온라인 사회 공격자 탐지

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 김종권.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 – 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.최근 우리는 Facebook, Twitter, Weibo, LinkedIn 등의 다양한 사회 관계망 서비스가 폭발적으로 성장하는 현상을 목격하였다. 하지만 사회 관계망 서비스가 개인과 개인간의 관계 및 커뮤니티 형성과 뉴스 전파 등의 여러 이점을 제공해 주고 있는데 반해 반갑지 않은 현상 역시 발생하고 있다. 스패머들은 사회 관계망 서비스를 동력 삼아 스팸을 매우 빠르고 넓게 전파하는 식으로 악용하고 있다. 스팸은 수신자가 원치 않는 메시지들을 일컽는데 이는 서비스의 신뢰도와 안정성을 크게 손상시킨다. 따라서, 스패머를 탐지하는 것이 현재 소셜 미디어에서 매우 긴급하고 중요한 문제가 되었다. 이 논문은 대표적인 사회 관계망 서비스들 중 Twitter와 Weibo에서 발생하는 스패밍을 다루고 있다. 이러한 유형의 스패밍들은 불특정 다수에게 메시지를 전파하는 대신에, 많은 일반 사용자들을 '팔로우(구독)'하고 이들로부터 '맞 팔로잉(맞 구독)'을 이끌어 내는 것을 목적으로 하기도 한다. 때로는 link farm을 이용해 특정 계정의 팔로워 수를 높이고 명시적 영향력을 증가시키기도 한다. 스패머의 온라인 관계망이 일반 사용자의 온라인 사회망과 다를 것이라는 가정 하에, 나는 스패머들을 포함한 일반적인 온라인 사회망 공격자들을 탐지하는 분류 방법을 제시한다. 나는 먼저 개인 사회망 내 사회 관계에 주목하고 두 가지 종류의 분류 특성을 제안하였다. 이들은 개인 사회망의 Triad Significance Profile (TSP)에 기반한 구조적 특성과 Hierarchical homophily에 기반한 관계 의미적 특성이다. 실제 Twitter와 Weibo 데이터셋에 대한 실험 결과는 제안한 방법이 매우 실용적이라는 것을 보여준다. 제안한 특성들은 전체 네트워크를 분석하지 않아도 개인 사회망만 분석하면 되기 때문에 scalable하게 측정될 수 있다. 나의 성능 분석 결과는 제안한 기법이 기존 방법에 비해 true positive와 false positive 측면에서 우수하다는 것을 보여준다.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

Combating Threats to the Quality of Information in Social Systems

Many large-scale social systems such as Web-based social networks, online social media sites and Web-scale crowdsourcing systems have been growing rapidly, enabling millions of human participants to generate, share and consume content on a massive scale. This reliance on users can lead to many positive effects, including large-scale growth in the size and content in the community, bottom-up discovery of “citizen-experts”, serendipitous discovery of new resources beyond the scope of the system designers, and new social-based information search and retrieval algorithms. But the relative openness and reliance on users coupled with the widespread interest and growth of these social systems carries risks and raises growing concerns over the quality of information in these systems. In this dissertation research, we focus on countering threats to the quality of information in self-managing social systems. Concretely, we identify three classes of threats to these systems: (i) content pollution by social spammers, (ii) coordinated campaigns for strategic manipulation, and (iii) threats to collective attention. To combat these threats, we propose three inter-related methods for detecting evidence of these threats, mitigating their impact, and improving the quality of information in social systems. We augment this three-fold defense with an exploration of their origins in “crowdturfing” – a sinister counterpart to the enormous positive opportunities of crowdsourcing. In particular, this dissertation research makes four unique contributions: • The first contribution of this dissertation research is a framework for detecting and filtering social spammers and content polluters in social systems. To detect and filter individual social spammers and content polluters, we propose and evaluate a novel social honeypot-based approach. • Second, we present a set of methods and algorithms for detecting coordinated campaigns in large-scale social systems. We propose and evaluate a content- driven framework for effectively linking free text posts with common “talking points” and extracting campaigns from large-scale social systems. • Third, we present a dual study of the robustness of social systems to collective attention threats through both a data-driven modeling approach and deploy- ment over a real system trace. We evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. • Finally, we study the underlying ecosystem of crowdturfing for engaging in each of the three threat types. We present a framework for “pulling back the curtain” on crowdturfers to reveal their underlying ecosystem on both crowdsourcing sites and social media

A systematic literature review on spam content detection and classification

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e ., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection