PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.

Artificial Neural Network Research in Online Social Networks

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.

Review and Analysis on Filtering of Unwanted Multimedia Messages from Online Social Network User Walls

When it comes to sharing and exchanging various types of information, online social networks (OSNs) have become an increasingly popular and interactive medium in today's world. People who are connected to blogs and social networks see all of the publicly shared information, and it has a profound effect on the human mind. Messages or comments posted on a wall, a public or private area, may include unnecessary information or sensitive data. Thus, online social networks can benefit from information filtering, which can be used to help users organise messages written in public areas by removing unnecessary words. An information filtering system proposed in this paper may allow OSN users to control the posting and commenting on their walls directly. Every time a user posts a message, the message is intercepted by the filtered wall, which then applies Filtering and Black List Rules to it. The message will appear on the user's wall if it is not filtered or blacklisted. Keywords: Content Based Message Filtering, Demographic Filtering, Collaborative Filtering.

Employing Graph Theory on Social Networks by Data Encryption to Enhance Privacy

Now a day’s users of Online Social Network have been increased by the Internet usage. As huge number of online social Network users, it becomes more and more interactive and privacy becomes a matter of increasing concern. To solve this problem, graph structure, Proposed Algorithm, Encryption Algorithm used, which excludes the users which combine the information of the status of privacy users. By using these methods, the experiment can be done which helps to show how the server works in between the sender and receiver and to receive their information without knowing third parties. And it would be easier by using graph that shows it can be efficiently helps the users to improve their privacy disclosure. Keywords: Graph theory, social network, Privacy, Data Encryption, end to end encryption, Encryption Algorithm.

Re-Fake: Klasifikasi Akun Palsu di Sosial Media Online menggunakan Algoritma RNN

Online Social Network (OSN) adalah aplikasi social media yang memungkinkan komunikasi publik dan berbagi informasi. Namun, akun palsu di OSN dapat menyebarkan informasi palsu dengan sumber yang tidak diketahui. Ini adalah tugas yang menantang untuk mendeteksi akun berbahaya dalam sistem OSN yang besar. Keberadaan akun palsu atau akun yang tidak dikenal di OSN dapat menjadi masalah serius dalam menjaga privasi data. Berbagai komunitas telah mengusulkan banyak teknik untuk menangani akun palsu di OSN, termasuk teknik hitam-putih berbasis aturan hingga pendekatan pembelajaran. Oleh karena itu, dalam penelitian ini kami mengusulkan model klasifikasi menggunakan RNN untuk mendeteksi akun palsu secara akurat dan efektif. Kami melakukan penelitian ini dalam beberapa langkah, termasuk mengumpulkan dataset, pra-pemrosesan, ekstraksi, melatih model kami menggunakan RNN. Berdasarkan hasil eksperimen, model yang kami usulkan dapat menghasilkan akurasi yang lebih tinggi daripada model pembelajaran konvensional.

Influence Maximization Based on Backward Reasoning in Online Social Networks

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising.

The Association Between Online Social Network Support and Fear of COVID-19 Among Older Adults

Public health concerns related to the COVID-19 health crisis are particularly salient among older adults. Fear surrounding COVID-19 has also been associated with increased spread, morbidity, and mortality of the disease. Prior to the pandemic, loneliness and social isolation were already a concern for older adults, and the pandemic further constrained how older adults may socially connect with others because of public health safety precautions. Online social networks are a valuable form of support for older adults, and usage of online social networks during the pandemic may have expanded. Thus, the purpose of this study is to examine the association between online social networks and fear of COVID-19 among older adults. A convenience sample (n = 239) of adults 60+ years of age in the U.S. completed a 20-minute, online survey. The independent variable utilized the Lubben Social Network Scale (four items), focusing on online support. The dependent variable was measured by the Fear of COVID-19 scale (eight items). Results of ordinary least squares regression show that increased online social network support was significantly associated with decreased fear of COVID-19 (p < 0.05), while holding constant age, sex, race, marital status, education, whether a respondent lives alone, and self-rated health. Findings highlight the importance of online social networks for older adults during the COVID-19 crisis. Existing online networks which engage older adults should be expanded, and efforts should be made to provide older adults with online forms of social support who may experience barriers or inequities related to accessing technology.