Detection and Prevention of Abuse in Online Social Networks

Adversaries leverage social networks to collect sensitive data about regular users and target them with abuse that includes fake news, cyberbullying, malware distribution, and propaganda. Such behavior is more effective when performed by the social network friends of victims. In two preliminary user studies we found that 71 out of 80 participants have at least 1 Facebook friend with whom (1) they never interact, either in Facebook or in real life, or whom they believe is (2) likely to abuse their posted photos or status updates, or (3) post offensive, false or malicious content. Such friend abuse is often considered to be outside the scope of online social network defenses. Several of our studies suggest that (1) perceived Facebook friend abuse as well as stranger friends are a significant problem; (2) users lack the knowledge or ability to address this problem themselves; and (3) when helped and educated, users are often willing to take defensive actions against abusive existing and pending friends, and strangers. Motivated by the rich, private information of users that is available to the Facebook friends, often the entry point of this vulnerability is the pending friends. In an exploratory study with a number of participants, we found that participants not only tend to accept invitations from perfect strangers but can even invent a narrative of common background to motivate their choice. Further, based on our conjecture that Facebook\u27s interface encourages users to accept pending friends, we develop new interfaces that seek to encourage users to explore the background of their pending friends and also to train them to avoid suspicious friends. The efficacy and implementation simplicity of the proposed modifications suggest that Facebook\u27s unwillingness to protect its users from abusive strangers is deliberate. This dissertation explores the friend abuse problem in online social networks like Facebook. We introduce two novel approaches to prevent friend abuse problem in Facebook. (1) First, we introduce AbuSniff which can detect already existing abusive friends in Facebook, and prevent the abusive friend from doing abuse by taking some protective actions against them. (2) Second, we introduce FLock to address the problem of abuse prevention during the time of friend invitation: by educating and training the Facebook users about the abusive friend from the list of pending friend invitations, and introducing new User Interface to help users reject the potentially abusive friend invitation, thus protecting the user from abuse in advance

An evaluation of identity in online social networking: distinguishing fact from fiction

Online social networks are understood to replicate the real life connections between people. As the technology matures, more people are joining social networking communities such as MySpace (www.myspace.com) and Facebook (www.facebook.com). These online communities provide the opportunity for individuals to present themselves and maintain social interactions through their profiles. Such traces in profiles can be used as evidence in deciding the level of trust with which to imbue individuals in making access control decisions. However, online profiles have serious implications over the reality of identity disclosure. There are many reasons why someone may choose not to reveal their true self, which sometimes leads to misidentification or deception. On one hand, the structure of online profiles allows anonymity, which gives users the opportunity to create a persona that may not represent their true identity. On the other hand, we often play multiple identities in different contexts where such behaviour is acceptable. However, realizing the context for each identity representation depends on the individual. As a result, some represented identities will be essentially real, if edited for public view, some will be disguised, and others will be fictitious or humorous. The millions of social network profiles, and billions of connections between them, make it difficult to formalize an automated approach to differentiate fact from fiction in online self-described identities. How can we be sure with whom we are interacting, and whether these individuals or groups are being truthful with the online identities they present to the rest of the community? What tools and techniques can be used to gather, organize, and explore the available data for informing the level of honesty that should be entrusted to an individual? Can we verify the validity of the identity automatically, based on the available information online? We aim to evaluate identity representation online and examine how identity can be verified in a less trusted online community. We propose a personality classifier model to identify a user‟s personality (such as expressive, valid, active, positive, popular, sociable and traceable) using traces of 2.2 million profile features collected from MySpace. We use data mining techniques and social network analysis to extract significant patterns in the data and network structure, and improve the classifier during the cycle of development. We evaluate our classifier model on profiles with known identities such as „real‟ and „fake‟. Our results indicate that by utilizing people‟s online, self-reported information, personality, and their network of friends and interactions, we are able to provide evidence for validating the type of identity in a manner that is both accurate and scalable

Risk assessment in centralized and decentralized online social network.

One of the main concerns in centralized and decentralized OSNs is related to the fact that OSNs users establish new relationships with unknown people with the result of exposing a huge amount of personal data. This can attract the variety of attackers that try to propagate malwares and malicious items in the network to misuse the personal information of users. Therefore, there have been several research studies to detect specific kinds of attacks by focusing on the topology of the graph [159, 158, 32, 148, 157]. On the other hand, there are several solutions to detect specific kinds of attackers based on the behavior of users. But, most of these approaches either focus on just the topology of the graph [159, 158] or the detection of anomalous users by exploiting supervised learning techniques [157, 47, 86, 125]. However, we have to note that the main issue of supervised learning is that they are not able to detect new attacker's behaviors, since the classifier is trained based on the known behavioral patterns. Literature also offers approaches to detect anomalous users in OSNs that use unsupervised learning approaches [150, 153, 36, 146] or a combination of supervised and unsupervised techniques [153]. But, existing attack defenses are designed to cope with just one specific type of attack. Although several solutions to detect specific kinds of attacks have been recently proposed, there is no general solution to cope with the main privacy/security attacks in OSNs. In such a scenario, it would be very beneficial to have a solution that can cope with the main privacy/security attacks that can be perpetrated using the social network graph. Our main contribution is proposing a unique unsupervised approach that helps OSNs providers and users to have a global understanding of risky users and detect them. We believe that the core of such a solution is a mechanism able to assign a risk score to each OSNs account. Over the last three years, we have done significant research efforts in analyzing user's behavior to detect risky users included some kinds of well known attacks in centralized and decentralized online social networks. Our research started by proposing a risk assessment approach based on the idea that the more a user behavior diverges from normal behavior, the more it should be considered risky. In our proposed approach, we monitor and analyze the combination of interaction or activity patterns and friendship patterns of users and build the risk estimation model in order to detect and identify those risky users who follow the behavioral patterns of attackers. Since, users in OSNs follow different behavioral patterns, it is not possible to define a unique standard behavioral model that fits all OSNs users' behaviors. Towards this goal, we propose a two-phase risk assessment approach by grouping users in the first phase to find similar users that share the same behavioral patterns and, then in the second phase, for each identified group, building some normal behavior models and compute for each user the level of divergency from these normal behaviors. Then, we extend this approach for Decentralized Online Social Networks (i.e., DOSNs). In the following of this approach, we propose a solution in defining a risk measure to help users in OSNs to judge their direct contacts so as to avoid friendship with malicious users. Finally, we monitor dynamically the friendship patterns of users in a large social graph over time for any anomalous changes reflecting attacker's behaviors. In this thesis, we will describe all the solutions that we proposed

Reflections on friendster, trust and intimacy

ABSTRACT By asking users to articulate and negotiate intimate information about themselves and their relationships, Friendster.com positions itself as a site for identity-driven intimate computing. Yet, trust issues are uncovered as users repurpose the site for playful intimacy and creativity. To flesh out the tension between purpose and desire, i reflect on Friendster's architecture, population and usage

AI and extremism in social networks

Studien utforsker hvordan midler som kunstig intelligens, AI- drevne chatbots, kan være kilder man kan regne med som moralske aktører på digitale plattformer og som kan være identifiserbare opprørsmodeller til bekjempelse av ekstremistiske og voldsforherligende ytringer på sosiale medieplattformer. Fremveksten av digital nettverkskommunikasjon har lettet prosessen med sosiale bevegelser, noe fenomenet «Den arabiske våren» tydelig demonstrerer. Sosiale medier har vært et verdifullt verktøy når det gjelder å utvikle kollektive identiteter med en felles ideologi for å fremme et bestemt mål eller en sak og gi alternative plattformer for undertrykte samfunn. Imidlertid forblir virkningen og konsekvensene av sosiale medier i samfunn der maktbalansen forrykkes gjennom fundamentale endringer et bekymringsfullt fenomen. Radikaliserte individer og grupper har også hevdet sin tilstedeværelse på sosiale medieplattformer gjennom å fremme fordommer, hat og vold. Ekstremistiske grupper bruker ulike taktikker for å utøve makten sin på disse plattformene. Bekjempelsen av voldelig ekstremisme på sosiale medieplattformer blir som regel ikke koordinert av aktuelle aktører som regjeringer, sosiale medieselskaper, FN eller andre private organisasjoner. I tillegg har fremdeles ikke forsøk på å konstituere AI til bekjempelse av voldelig ekstremisme blitt gjennomført, men lovende resultater har blitt oppnådd gjennom noen initiativer. Prosjektet som en ‘case study’ ser på den nylige reformen i Etiopia som ble gjennomført av Nobels fredsprisvinner 2019 Abiy Ahmed etter at han tiltrådte som statsminister i Etiopia i april 2018. Etter flere tiår med undertrykkelse har den nye maktovertakelsen der det politiske rommet ble åpnet opp og ytringsfrihet ble tillatt, uventet ført til et skred av etniske gruppers polarisering. Nye etno-ekstremister har dukket frem fra alle kriker og kroker av landet og også fra sin tilværelse i diaspora. Studien ser videre på hvilken rolle sosiale medier til tider spiller ved direkte å presse på for å påvirke til og dermed forårsake voldelige handlinger på grasrota.Ved å bruke en kvalitativ forskningsmetode for ustrukturerte intervjuer med etiopiske brukere av sosiale medier, journalister og aktivister, identifiserer studien kjerneaspektene ved konfliktene og foreslår initiativer som kan brukes til å motvirke voldelig etnisk ekstremisme. Ved å bruke relevant litteratur ser prosjektet videre på innarbeidelsen av kunstig intelligens (AI) i «moralske handlinger» på sosiale medier og hvordan den kan utformes slik at den av seg selv kan ta i bruk moralske beslutningsevner i nettverket. I tillegg ser studien på mulighetene videre for bekjempelse av voldelig ekstremisme og skisserer den spesifikke rollen ikke menneskelige aktører som profesjonelle troll og bots på sosiale medier bør spille for å slåss mot radikalisering som kan føre til voldelige handlinger.Mastergradsoppgave i digital kulturMAHF-DIKULDIKULT35

Facebook, the Media and Democracy

Facebook, the Media and Democracy examines Facebook Inc. and the impact that it has had and continues to have on media and democracy around the world. Drawing on interviews with Facebook users of different kinds and dialogue with politicians, regulators, civil society and media commentators, as well as detailed documentary scrutiny of legislative and regulatory proposals and Facebook’s corporate statements, the book presents a comprehensive but clear overview of the current debate around Facebook and the global debate on the regulation of social media in the era of ‘surveillance capitalism.’ Chapters examine the business and growing institutional power of Facebook as it has unfolded over the fifteen years since its creation, the benefits and meanings that it has provided for its users, its disruptive challenge to the contemporary media environment, its shaping of conversations, and the emerging calls for its further regulation. The book considers Facebook’s alleged role in the rise of democratic movements around the world as well as its suggested role in the election of Donald Trump and the UK vote to leave the European Union. This book argues that Facebook, in some shape or form, is likely to be with us into the foreseeable future and that how we address the societal challenges that it provokes, and the economic system that underpins it, will define how human societies demonstrate their capacity to protect and enhance democracy and ensure that no corporation can set itself above democratic institutions. This is an important research volume for academics and researchers in the areas of media studies, communications, social media and political science

The Dynamics of Influencer Marketing

YouTube, Instagram, Facebook, Vimeo, Twitter, etc. have their own logics, dynamics and different audiences. This book analyses how the users of these social networks, especially those of YouTube and Instagram, become content prescribers, opinion leaders and, by extension, people of influence. What influence capacity do they have? Why are intimate or personal aspects shared with unknown people? Who are the big beneficiaries? How much is vanity and how much altruism? What business is behind these social networks? What dangers do they contain? What volume of business can we estimate they generate? How are they transforming cultural industries? What legislation is applied? How does the legislation affect these communications when they are sponsored? Is the privacy of users violated with the data obtained? Who is the owner of the content? Are they to blame for ""fake news""? In this changing, challenging and intriguing environment, The Dynamics of Influencer Marketing discusses all of these questions and more. Considering this complexity from different perspectives: technological, economic, sociological, psychological and legal, the book combines the visions of several experts from the academic world and provides a structured framework with a wide approach to understand the new era of influencing, including the dark sides of it. It will be of direct interest to marketing scholars and researchers while also relevant to many other areas affected by the phenomenon of social media influence