hannah_arendt: An Arendtian Critique of Online Social Networks
In: Millennium: journal of international studies, Band 43, Heft 1, S. 165-186
ISSN: 0305-8298
In: Millennium: journal of international studies, Band 43, Heft 1, S. 165-186
ISSN: 0305-8298
In: Intelligence and Security Informatics; Studies in Computational Intelligence, S. 249-273
In: Government information quarterly: an international journal of policies, resources, services and practices, Band 29, Heft 2, S. 169-181
ISSN: 0740-624X
In: Government information quarterly: an international journal of policies, resources, services, and practices, Band 29, Heft 2, S. 169-182
ISSN: 0740-624X
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.
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In: http://hdl.handle.net/2117/123038
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.
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In: ESSACHESS - Journal for Communication Studies, Band 9, Heft 2, S. 33-42
Seeking to understand the influence of culture on the expression of emotions in online social networks, we analyzed four Facebook groups - two from Quebec, Canada and two from Colombia - created following unexpected deaths. Comparison of the messages posted in these groups reveals a stronger tendency to maintain a virtual relationship with the deceased by Canadians than by Columbians. Among the former, the deceased is more often asked to and thanked for watching over the living, and testimonies of love addressed to the deceased are more numerous than among the latter. Among the latter, the strength of the links maintained with the deceased justifying the present pain and evocation of the mourners' Catholic beliefs are relatively more frequent. Finally, while the Canadian Quebecers' messages would presumably be written in French and those of Colombians in Spanish, it is interesting to observe a certain presence of English to express feelings of loss and love in the four groups, as well as a certain affinity between North American virtual bereavement practices and those seemingly more characteristic of women, the main contributors in all four groups.
In the past, online social networks (OSN) like Facebook and Twitter became powerful instruments for communication and networking. Unfortunately, they have also become a welcome target for socialbot attacks. Therefore, a deep understanding of the nature of such attacks is important to protect the Eco-System of OSNs. In this extended abstract we propose a categorization scheme of social bot attacks that aims at providing an overview of the state of the art of techniques in this emerging field. Finally, we demonstrate the usefulness of our categorization scheme by characterizing recent socialbot attacks according to our categorization scheme.
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.
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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.
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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.
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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.
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SSRN
In: The International Journal of Environmental, Cultural, Economic, and Social Sustainability: Annual Review, Band 7, Heft 4, S. 47-64
This study argues that Facebook only generates bridging social capital through driving people to offline events. Other indicators on Facebook such as Facebook friends or Facebook group membership do not appear associated with social capital. Beyond that, political positions posted on Facebook appear to be reasonably accurate but influenced by what the user's Facebook friends have on their profiles.
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