Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.
New technologies in communications and networking have shaped the way political movements can be mobilised and coordinated in important ways. Recent uprisings have shown dramatically how a people can communicate its cause effectively beyond borders, through online social networking channels and mobile phone technologies. Hannah Arendt, as an eminent scholar of power and politics in the modern era, offers a relevant lens with which to theoretically examine the implications and uses of online social networks and their impact on politics as praxis. This article creates an account of how Arendt might have evaluated virtual social networks in the context of their potency to create power, spaces and possibilities for political action. With an Arendtian lens the article examines whether these virtual means of 'shared appearances' facilitate or frustrate efforts in the formation of political power and the creation of new beginnings. Based on a contemporary reading of her writings, the article concludes that Arendt's own assessment of online social networks, as spheres for political action, would likely have been very critical. ; Peer-reviewed ; Post-print
International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.
International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.
International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.
Nowadays the communication between people is highly influenced by the Online Social Networks (OSNs). Immense number of personal, professional and political thoughts are shared online every single day. Thus, OSNs are attractive for cyber criminals who are trying to exploit their weaknesses and vulnerabilities. Fake accounts on OSNs have become a basic threat used in different online attacks. And even if some of these attacks are harmless like generating fake accounts for "likes" on Facebook, followers on Twitter and views on YouTube, other attacks are more serious and can be dangerous online. Influence on trending topics, spread spam advertisements and false political content are just some of the examples how attackers are able to wreak havoc online by using fake profiles. With the increasing number of security and privacy threats, some of the OSNs have adopted security measures to stop the mass creation of the fake accounts. However, those measures are often ineffective by the many tools available on the underground marketplaces that allow people to cheaply acquire fake accounts. In this regard, this thesis aims to detect the fake profiles on a very popular OSN, Twitter, with the help of machine learning algorithms. The first key contribution is the research on the appropriate machine learning techniques. Support Vector Machine, Random Forest and k-Nearest Neighbour were the three supervised learning algorithms. In addition to them, one clustering algorithm was tested, namely k-Means. The next contribution is the acquisition of labelled data related to real and fake profiles in Twitter for the training phase. After analysing the behaviour of the users and their tweets activities, an extensive dataset of 12 features was created. The named Fake profile´s detection dataset plays key role in distinguishing fake accounts among real ones and it is applied on the machine learning algorithms. Analysis of the results has been performed in five different scenarios. The classifiers achieve accuracy score of around 92% for separating the fake profiles from the real ones and the clustering algorithm is able to detect all fake profiles. Finally, for testing purposes were analysed some of the followers of Donald Trump with the already trained models.
Nowadays the communication between people is highly influenced by the Online Social Networks (OSNs). Immense number of personal, professional and political thoughts are shared online every single day. Thus, OSNs are attractive for cyber criminals who are trying to exploit their weaknesses and vulnerabilities. Fake accounts on OSNs have become a basic threat used in different online attacks. And even if some of these attacks are harmless like generating fake accounts for "likes" on Facebook, followers on Twitter and views on YouTube, other attacks are more serious and can be dangerous online. Influence on trending topics, spread spam advertisements and false political content are just some of the examples how attackers are able to wreak havoc online by using fake profiles. With the increasing number of security and privacy threats, some of the OSNs have adopted security measures to stop the mass creation of the fake accounts. However, those measures are often ineffective by the many tools available on the underground marketplaces that allow people to cheaply acquire fake accounts. In this regard, this thesis aims to detect the fake profiles on a very popular OSN, Twitter, with the help of machine learning algorithms. The first key contribution is the research on the appropriate machine learning techniques. Support Vector Machine, Random Forest and k-Nearest Neighbour were the three supervised learning algorithms. In addition to them, one clustering algorithm was tested, namely k-Means. The next contribution is the acquisition of labelled data related to real and fake profiles in Twitter for the training phase. After analysing the behaviour of the users and their tweets activities, an extensive dataset of 12 features was created. The named Fake profile´s detection dataset plays key role in distinguishing fake accounts among real ones and it is applied on the machine learning algorithms. Analysis of the results has been performed in five different scenarios. The classifiers achieve accuracy score of around 92% for separating the fake profiles from the real ones and the clustering algorithm is able to detect all fake profiles. Finally, for testing purposes were analysed some of the followers of Donald Trump with the already trained models.
Online social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way.
With billions of users, Online Social Networks(OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and worldwide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy of those measures is either not thoroughly validated or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focus on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms, and trending topic security in this dissertation. We first examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user's basic profile and connection information starting from some leaked connections on the user's homepage. Based on the dataset we collected on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes with different amount of public information. The evaluation results show that (1) a user's private basic profile can be inferred with high accuracy and (2) connections can be revealed in a significant portion based on even a small number of directly leaked connections. Secondly, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioral features to portray a user's social behavior. A user's statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collected from Facebook, we observe that each user's activities are highly likely to conform to its behavioral profile while two different user's profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user's behavioral profile can be built the more accurately compromisation can be detected. Finally, we investigate the manipulation of OSN trending topics. Based on the dataset we collected from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. at last we demonstrate the threats of compromised accounts and sybil accounts to trending through simulation and discuss countermeasures against trending manipulation.
More and more researchers focus on the role of social networks in election campaigns. This article represents a case study of the parliamentary elections from 2015 in Spain and 2016 in Romania, with the aim of comparing the two online political campaigns. We describe how both parties in Romania and Spain used Facebook during the last parliamentary elections, in order to see how the political parties communicate and the online reactions generated by their messages. With the help of content and statistical analysis we take a closer look the messages published in the Facebook profiles of candidates and political parties during the general elections. The results indicate that, during parliamentary elections, unlike the presidential ones, the voters' attention is not directed to a candidate, but to a group of candidates. As a result, the communication strategy is different, focusing on increasing the notoriety of the candidates. The low interest in parties and parliamentary elections leads to using social networks mainly for disseminating information about the candidates and less as tool for mobilizing voters.
Online Social Networks (OSNs) are integrated into business, entertainment, politics, and education; they are integrated into nearly every facet of our everyday lives. They have played essential roles in milestones for humanity, such as the social revolutions in certain countries, to more day-to-day activities, such as streaming entertaining or educational materials. Not surprisingly, social networks are the subject of study, not only for computer scientists, but also for economists, sociologists, political scientists, and psychologists, among others. In this dissertation, we build a model that is used to classify content on the OSNs of Reddit, 4chan, Flickr, and YouTube according the types of lifespan their content have and the popularity tiers that the content reaches. The proposed model is evaluated using 10-fold cross-validation, using data mining techniques of Sequential Minimal Optimization (SMO), which is a support vector machine algorithm, Decision Table, Naïve Bayes, and Random Forest. The run times and accuracies are compared across OSNs, models, and data mining algorithms. The peak/death category of Reddit content can be classified with 64% accuracy. The peak/death category of 4Chan content can be classified with 76% accuracy. The peak/death category of Flickr content can classified with 65% accuracy. We also used 10-fold cross-validation to measure the accuracy in which the popularity tier of content can be classified. The popularity tier of content on Reddit can be classified with 84% accuracy. The popularity tier of content on 4chan can be classified with 70% accuracy. The popularity tier of content on Flickr can be classified with 66% accuracy. The popularity tier of content on YouTube can be classified with only 48% accuracy. Our experiments compared the runtimes and accuracy of SMO, Naïve Bayes, Decision Table, and Random Forest to classify the lifespan of content on Reddit, 4chan, and Flickr as well as classify the popularity tier of content on Reddit, 4chan, Flickr, and YouTube. The ...
The research aimed to explore the status, usage, and roles of social networking sites in intercultural communication of different regional groups in different contexts, and the impacts on interpersonal communication in Thai culture and on communication across cultures, including guidelines towards effective and appropriate usage of social networking sites in Thai society. The study was conducted by documentary research, focus group interview and survey questionnaires with students, governmental officers, and multinational company staffs, including in-depth interviews with scholars and professionals. The results are as follows: 1) The subjects use "facebook" the most (88.75%) at "seldom" level (30.00%) to contact their present foreign friends or colleagues at school or in the workplace (46.80%). The most mentioned purpose of using SNS is to maintain their relationship (84.31%). 2) Increased roles of SNS usage in intercultural and cross cultural communication are found in various contexts and circles, i.e internal communication and organizational management, learning enhancement in educational and business circles, utilization for marketing and public relations, expansion of network and cooperation in social and political movement, and tools for mass media work across borders. 3) SNS usage is found to affect Thai people's values and personality. On the other hand, it enhances their cultural learning and increase their positive attitude in intercultural communication. 4) A number of guidelines towards effective and appropriate SNS usage in Thai society are proposed in this study. ; การวิจัยนี้มีวัตถุประสงค์เพื่อสำรวจสถานการณ์ ลักษณะการใช้และบทบาทการใช้เว็บไซต์เครือข่ายสังคมในการสื่อสารระหว่างวัฒนธรรมในบริบทต่างๆ ของคนกลุ่มต่างๆ ในแต่ละภูมิภาคในสังคมไทย ศึกษาผลกระทบของการใช้เว็บไซต์และเครือข่ายสังคมออนไลน์ต่อรูปแบบและวิธีการสื่อสารระหว่างบุคคลในวัฒนธรรมไทยและต่อการสื่อสารกับคนต่างวัฒนธรรมรวมถึงหาแนวทางการใช้เครือข่ายสังคมออนไลน์ในปริบทการสื่อสารระหว่างวัฒนธรรมอย่างมีประสิทธิภาพและเหมาะสมในสังคมไทย โดยศึกษาจากเอกสาร สัมภาษณ์กลุ่มเฉพาะ และสำรวจด้วยแบบสอบถามจาก กลุ่มตัวอย่าง ได้แก่ นักเรียน/นักศึกษา ข้าราชการ และพนักงานบริษัทเอกชนข้ามชาติ รวมถึงสัมภาษณ์เชิงลึกนักวิชาการและนักวิชาชีพ ผลการวิจัยพบว่า 1) กลุ่มตัวอย่างใช้เฟซบุ๊กมากที่สุด (ร้อยละ 88.75) กับเพื่อนต่างชาติที่สถานศึกษาหรือที่ทำงานในปัจจุบัน (ร้อยละ 46.80) ในระดับนานๆ ครั้ง (ร้อยละ 30.00) เพื่อรักษาความสัมพันธ์มากที่สุด (ร้อยละ 84.31) 2) มีการใช้เว็บไซต์เครือข่ายสังคมในการสื่อสารระหว่างวัฒนธรรมและข้ามวัฒนธรรมในบริบทและวงการต่างๆ มากขึ้น เช่น การบริหารและสื่อสารภายในองค์กร การเพิ่มช่องทางการเรียนรู้เพื่อการศึกษาวัฒนธรรมทั้งในวงการศึกษาและทางธุรกิจ การใช้เป็นเครื่องมือทางตลาดและประชาสัมพันธ์ การขยายเครือข่ายและความร่วมมือในการเคลื่อนไหวทางสังคมและการเมือง รวมถึงการใช้เป็นเครื่องมือในการทำงานของสื่อมวลชนข้ามชาติ ฯลฯ 3) พบการใช้เว็บไซต์เครือข่ายสังคมมีผลต่อการเปลี่ยนแปลงค่านิยมและบุคลิกภาพของคนไทย ขณะเดียวกันมีผลต่อการเรียนรู้และสร้างทัศนคติเชิงบวกในการสื่อสารกับคนต่างวัฒนธรรมมากขึ้น 4) แนวทางในการใช้เว็บไซต์เครือข่ายสังคมอย่างมีประสิทธิภาพและเหมาะสมในสังคมไทยหลายประการได้ถูกนำเสนอในงานวิจัยนี้
Enormous popularity of Social Networking Sites has introduced a great number of privacy risks. Even the most popular of all the social networking sites have characterized access control policies in terms of explicit tracking of the interpersonal relationships between the subjects, objects and their inter relation. In this paper we present a novel paradigm that accounts for a secure, yet sociable information flow model based on access control policies. We took advantage of real time success of the access control security policies in operating systems by implementing them on online social networks at the mandatory level so that the user's privacy does not have to be at stake by the growth of social network and activities or by the level of user understanding of the privacy settings provided by the social networking sites based on discretionary access control. We used Facebook and Google+ as case study and implemented the security policy in SecureWall to mitigate possible privacy leakage scenarios observed. We have implemented Chinese wall policy for community level privacy, Bell la-Padulla access control model to assure confidentiality to the user and Biba Access control model for providing Integrity. Since Bell la-Padulla and Biba models are basically meant to serve military security and therefore can risk sociability, we have combined the two models using Lipner Security Matrix in order to provide security without risking sociability. Our research can be adopted by online social networking sites for the mandatory level security especially for social networking in organizational specific activities.
The phenomenon of online social networking during the age of the web creates an era known as the 'Online Social Network Era'. Whilst the advantages of the online social network are numerous, the drawbacks of online social network are also worrying. The explosion of the use of online social networks creates avenues for cyber criminals to commit crimes online, due to the rise of information technology and Internet use, which results in the growth of the Internet society which includes the children. The children, who are in need of 'extra' protection, are among the community in the online social network, and they are exposed to the cyber crimes which may be committed against them. This article seeks to explore and analyse the position on the protection of the children in the online society; and the focus is in Malaysia while other jurisdictions are referred as source of critique. The position in Malaysia is looked into before the introduction of the Sexual Offences Against Children Act 2017. It is found that, in the Online Social Network Era, there are inadequate protections for children in the Malaysian legal framework before the introduction of the Act. The effectiveness of the Act which is already passed by the Parliament but yet to be enforced, is yet to be seen.
(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. ; [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. ; This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. ; Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723 ; S ; 143631 ; 143645 ; 7
