Today's Social Networks Research Articles

Modified Caesar Cipher and Card Deck Shuffle Rearrangement Algorithm for Image Encryption

ABSTRACT In today's social networking era, information security breaches are at an all-time high, especially when it comes to texts and images, which should come as no surprise. Integrity is therefore crucial for information sharing over an unprotected connection. As a result, image/text encryption is a crucial preprocessing step in today's information transmission. Sensitive information may be sent via image encryption, which facilitates a variety of procedures. Here the proposed method is a combination of the Modified Caesar Cipher and the Card Deck Shuffle Algorithm for image encryption. The Card Deck Shuffle Algorithm rearranges the pixels from the result of the Modified Caesar approach, which encrypts the pixel value of each image. The method employs an n + 8 variable bit key (n is frequently more than 18 in real-world applications), which needs more than 2 26 brute force attacks to be successful. The method described here may be used to secure images in a variety of multimedia applications.

Application of artificial intelligence in network security defense

With the development of today's social network, people use the Internet more frequently and become more reliant on it. However, as a result of the development of the network, network attacks are becoming more frequent, and people have an imperative need for a secure and dependable computer network. Meanwhile, the technology of artificial intelligence is progressively maturing, so the application of artificial intelligence in network security defense will be a more effective means of addressing network security's hidden dangers. This paper focuses primarily on the benefits of the application of artificial intelligence to network security defense, as well as summarizing and analyzing the application of artificial intelligence to network security defense. This paper concludes, by analyzing the most recent literature and evaluations, that AI plays a crucial role in resolving network security issues and can effectively withstand certain network attack threats.

Potential friendship discovery in social networks based on hybrid ensemble multiple collaborative filtering models in a 5G network environment

At present, 5G network technology is being applied to various social network modes, and it can provide technical and traffic support for social networks. Potential friendship discovery technology in 5G-enabled social networks is beneficial for users to make potential friends and expand their range of activities and social hierarchy, which is highly sought after in today's social networks and has great economic and application value. However, the sparsity of the dominant user association dataset in 5G-enabled social networks and the limitations of traditional collaborative filtering algorithms are two major challenges for the friend recommendation problem. Therefore, in order to overcome these problems regarding previous models, we propose a Hybrid Ensemble Multiple Collaborative Filtering Model (HEMCF) for discovering potential buddy relationships. The HEMCF model draws on a special autoencoder method that can effectively exploit the association matrix between friends and additional information to extract a hidden representation of users containing global structural information. Then, it uses the random walk-based graph embedding algorithm DeepWalk to extract another hidden representation of users in the buddy network containing local structural information. Finally, in the output module, the HEMCF model stacks and multiplies the two types of hidden representations of users to ensure that the information mentioned above is concentrated in the final output to generate the final prediction value. The magnitude of the prediction value represents the probability of the users being friends, with larger values representing a high probability of the two users being friends, and vice versa. Experimental results show that the proposed method boosts the accuracy of the relationship prediction over baselines on 3 real-world public datasets dramatically.

ОРТА ЖАСТАҒЫ АДАМДАРДЫҢ ӘЛЕУМЕТТІК ЖЕЛІЛЕРГЕ ТӘУЕЛДІЛІГІНІҢ ПСИХОЛОГИЯЛЫҚ АСПЕКТІЛЕРІ

The article presents the results of an empirical study aimed at studying the relationship between dependence on social networks and people's communication abilities. The study involved middle-aged people from 30 to 40 years old. The relevance of the topic is that today social networks are growing, which attract more and more people every day. In the modern world, many contacts are created via the Internet. Thanks to social networks, people from all over the world can communicate with each other. Social networks are becoming a space for the formation and approval of various cultural stereotypes. Social networks have become significant and valuable for people, and it has become possible to establish connections that meet their interests. The Internet forms its own specific environment, which affects many psychological aspects. Social networks practically do not allow themselves to be controlled externally, they do not have a single center, so everyone has the right to act in them at their own discretion. The Internet as a modern means of mass communication is turning from a passive listener into an active participant.

Comparative study of Existing Models for Online Social Network

Today, Online Social Networks becomes the first choice for businesses to broadcast their campaigns for branding, publicity, strategies, advertising, marketing, social influence and so many other areas. Social Network is a platform for communicating with social actors and Social Media is used by companies for broadcasting information. Online Social Network used by businesses for number of purposes but the primary concern is build new social connections that helps to target most audiences for successful campaign purposes. In OSNs sites the social objects are represented by nodes and the term edge used for connection between nodes under the graph theory. Today Social Network sites have becomes most exploded as compared to traditional sites because of impact of so many influence models over traditional models.Some of popular websites of OSN such as MySpace, Facebook, Flickr, YouTube, Google Video, Orkut, LinkedIn, Live Journal and BlogSpot have great impact on customer when targeting the sales marketing funnel for businesses. Adjacent users sometimes called engaged users tend to have more trust level as compared to random pairs users on the social media sites. Already have so much research that helps to calculate the trust factor using influence modeling. So influence models play a vital role to predict the behavior of the customer that helps to fulfill the goal of the business. The key contribution of this work is study of online social networking models.

Machine Learning Methods for Analysing the Impact of Social Media on Students Academic Performance

Today Social networking is a common apporch, mostly along with youngster. The social media impact on the academic performance of students become fundamental part to determine. Social Networking Sites (SNS) for example, twitter facebook are presently an vital medium that are used to connect peoples and different associations approximately the world Technology is thriving quick with the passage of time, and the youngsters are trapped in this swift revolutionize. In this paper we find out in which ways social sites affects student’s academic performance and recognize the impact of social sites on our educational system. The reason at the back huge use of social networking sites. Social sites networking (SNS) have grow from being merely libertine stages for personl use to awesome progressive structure that utilized both blend and inside and remotely for relationship and joined exertion with shafts separated stakeholders. Generally among youthful understudies, Social sites networks is an unmistakable example today In this paper by using student-por.csv and student-mat.csv dataset, we work on the classifier on the social media impact on students academic performances. For this purposes we choose K nearest-neighbor (KNN), support vector machine (SVM) and Linear regression algorithm using python that predict slightly better and give right prediction about the student’s academic performance. In social networking model our work has enhanced our imminent. The results reveal that there is strong relationship between social media and student’s academic performance. This paper shows that mostly students have cell phones and also have internet availability so they use 40 mints form 4 hours daily and not give attentions to their studies, all these activities on internet effect the student’s academic performance. This study will commence with introduction and background of existing studies. After that on the basis of background there will be a research question.

Bullshit in a network structure: the two-sided influence of self-generated signals.

In today’s social network age, information flowing in networks does not derive solely from external sources; people in the network also independently generate signals. These self-generated signals may not be deliberate lies, but they may not bear any relationship with the truth, either. Following the philosopher Harry G. Frankfurt, we refer to such self-generated signals as bullshit. We present an information diffusion model that allows nodes which hold no value to spread information, capturing the diffusion of bullshit information. The presence of self-generated signals (i.e., bullshit) increases the amount of information available for transmission in the network. However, participants in the spread process respond to the existence of such self-generated information by receiving data from internal sources with caution. These two contradictory forces—the increase in information transmission on the one hand, and in suspicion on the other—result in a two-sided effect of bullshit on the total spread time. We first take a numerical approach, simulating our model on Watts–Strogatz networks and building a decision tree to characterize the effects of bullshit given different network structures. We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.

Hybrid job offer recommender system in a social network

AbstractRecommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.

Differentially-Private and Trustworthy Online Social Multimedia Big Data Retrieval in Edge Computing

The explosive growth of multimedia contents (MCs) in today's mobile social networks has pushed edge computing to face severe security and online big data-processing problems. On the one hand, the edge nodes (ENs) should help mobile users find, cache, and share MCs in the presence of an ever-increasing scale of multimedia big data. On the other hand, how to provide secure MC retrieval schemes to exclude dishonest-and-malicious untrusted ENs and to prevent privacy breaches from honest-but-curious ENs and users is a challenging issue. To tackle these problems, we study the privacy-preserving and trustworthy MCs retrieval system to make personalized MC recommendations from ENs to users with big data support. In our framework, each EN is modeled as a distributed context-aware online learner. ENs collaborate to learn users’ preferences based on their contexts and previous behaviors and social intimacy. To support big data analytics, we establish an MC-cluster tree from top to the bottom to handle the dynamically varying cached MC datasets. A differentially private algorithm is proposed to preserve the data privacy among honest-but-curious ENs and users. To guarantee trustworthy edge computing, a trust evaluation mechanism is designed to evaluate the reliability of ENs. We further consider the structure of edge networks to improve the performance of our algorithm. Experimental results validate that our new framework can support increasing multimedia big datasets while striking a balance among privacy-preserving level, Trustworthy level, and caching MC prediction accuracy.

Innovative social networks modelling for population simulation

Today social networks are an important element of society that requires developing new models as well as guidelines to create interoperable simulators covering these aspects. These new simulators are expected to enable researches and developments focused on people reactions to simulated events via social networks. This research area results are promising, however, it is necessary to address also several challenging issues including proper development of new conceptual models as well as validation and verification processes and definition of human behaviour modifiers. The authors present a new methodological framework for modelling individuals and populations by means of web social networks, in order to reproduce their dynamics. The article proposes to create interoperable models of social networks along with a real case study related to training and decision making process for authorities. The paper includes experimentations over a complex scenario where human factors and social networks play a crucial role.

Caching at the web scale

Today's web applications and social networks are serving billions of users around the globe. These users generate billions of key lookups and millions of data object updates per second. A single user's social network page load requires hundreds of key lookups. This scale creates many design challenges for the underlying storage systems. First, these systems have to serve user requests with low latency. Any increase in the request latency leads to a decrease in user interest. Second, storage systems have to be highly available. Failures should be handled seamlessly without affecting user requests. Third, users consume an order of magnitude more data than they produce. Therefore, storage systems have to be optimized for read-intensive workloads. To address these challenges, distributed in-memory caching services have been widely deployed on top of persistent storage. In this tutorial, we survey the recent developments in distributed caching services. We present the algorithmic and architectural efforts behind these systems focusing on the challenges in addition to open research questions.

Social Network Analysis (SNA) to Analyse Bollywood Movies

In today's world Social Network is the mostly used media for communication among the people. The service provided by social network allows people to use and engage with the Internet and with each other. Assessment of these interactions allows critical analysis of trends or opinion. Assessment requires analysis. That is the main reason for the popularity of Social Network Analysis. Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. Social network analysis has been a popular research area recently thanks to the availability of many large dataset. The main problem in social network analysis is to recover the underlying network structure given information about how nodes in the network interact with each other in the past. Graph analytics have proven to be valuable tools in Social Network Analysis. This work introduces a novel approach that uses social network analysis for study and analysis of the different stakeholders of Bollywood movie industry. The Indian movie industry is a big business prospect of India. Even the movies released in one year is much higher that any other film industry. Some of the stake holders of the movie industry like stars, directors, music directors are not only renowned in society but also financially prospective. To test this approach, experiments have been conducted on Internet movie database (Imdb) of a particular era. The results obtained from the analysis are encouraging for predicting future trends of the different stake holders of Bollywood movies.

User Perception of Social Networking Sites in the University of Swaziland

Internet has experiences tremendous growth in today's world and social networking sites have become highly significant in our lives. The present comparative study between the students of Agriculture and Health Science concentrate more precisely on the students who come to the library. The research explores how the students create identity for themselves in the virtual world and how they relate to others online. It analyzes the cultural differences from the youth perspective and discusses whether social networking sites isolate youngsters from the society or help them to build relationships; the participation in these sites is also explored. The study is based on a survey. Students completed a questionnaire which assessed personal use of social media when communicating with friends and family. The findings disclosed that social networking is gaining popularity among them. Facebook is the most popular and used social networking site followed by WhatsApp and Google plus.

Algorithm to Monitor Suspicious Activity on Social Networking Sites using Data Mining Techniques

today's world social networks have become a platform to express opinions or feelings related to current events or any other topics. But sometimes provocative posts related to renowned people, religion, sexuality, countries or any other sensitive topics create havoc in the society. Such posts must be administered and removed before they spread and hurt people's feelings resulting into tension in the society and possible riots. This paper discusses about the techniques to identify such suspicious posts and report them to curb the spread of provoking posts.

Emotion recognition and affective computing on vocal social media

Vocal media has become a popular method of communication in today's social networks. While conveying semantic information, vocal messages usually also contain abundant emotional information; this emotional information represents a new focus for data mining in social media analytics. This paper proposes a computational method for emotion recognition and affective computing on vocal social media to estimate complex emotion as well as its dynamic changes in a three-dimensional PAD (Position–Arousal–Dominance) space; furthermore, this paper analyzes the propagation characteristics of emotions on the vocal social media site WeChat.

Trust and Privacy Exploitation in Online Social Networks

Online social networks have been typically created for convenience--so they haven't been built from the ground up with security in mind. They often have confusing privacy settings and are susceptible to various kinds of attacks that exploit users' trust and privacy. In this article, the authors discuss security pitfalls in today's social networks, briefly introducing common attack methods. They implemented a proof-of-concept Facebook app, which is actually a harmless malware that uses common attack methods to demonstrate the vulnerability of online social networks. Although today's online social networks commonly offer users a variety of security settings, users tend to trust the information obtained from online social networks regardless of the settings. This kind of user mentality can be more crucial than technical aspects in determining the level of security in online social networks.

Ontology-based blog collection and profile-based personalised ranking

Blogging plays a major role in today's social networking. Large numbers of blogs are available in the web. This becomes a tedious task for the user and the blogger, to search for a technical blog from a list of randomly appearing blogs. Thus a method is required to rank the blogs for the user, according to the relevance of the query. The existing works focus either on the behaviour of the blogger or the linking of one blog with another blog. This paper introduces a novel method of ranking blogs based on the relevance of the content to the query. The semantics of the query is analysed to rank the blogs. The proposed method also uses concept-based profiling, to enable the user to retrieve personalised content for the query. The experimental analysis shows that the proposed personalised semantic blog ranking PSBR is efficient in ranking the technical blogs for the user.

Slipping Racism into the Mainstream: A Theory of Information Laundering

Many studies in recent years have addressed the notable ways that Internet features such as blogs and search engines have democratized the community of information seekers and providers, however, fewer investigations have addressed the darker element that has emerged from that same democratic sphere. That is, the huge resurgence and transformation of racist communities across cyberspace. This article presents a new theory of information laundering to explain the process by which racial hate speech is becoming legitimized through a borrowed network of online associations. This Internet-specific theory builds upon research of “information-based” racist propaganda to explain how today's search engines, social networks, and political blogs unwittingly enable purveyors of bigotry to infiltrate into mainstream spaces of public discourse.

REX

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and OLAP queries, but most are not robust enough to scale to large clusters. Conversely, "cloud" platforms like MapReduce execute chains of batch tasks across clusters in a fault tolerant way, but have too much overhead to support ad hoc queries. Moreover, both classes of platform incur significant overhead in executing iterative data analysis algorithms. Most such iterative algorithms repeatedly refine portions of their answers, until some convergence criterion is reached. However, general cloud platforms typically must reprocess all data in each step. DBMSs that support recursive SQL are more efficient in that they propagate only the changes in each step --- but they still accumulate each iteration's state, even if it is no longer useful. User-defined functions are also typically harder to write for DBMSs than for cloud platforms. We seek to unify the strengths of both styles of platforms, with a focus on supporting iterative computations in which changes , in the form of deltas , are propagated from iteration to iteration, and state is efficiently updated in an extensible way. We present a programming model oriented around deltas, describe how we execute and optimize such programs in our REX runtime system, and validate that our platform also handles failures gracefully. We experimentally validate our techniques, and show speedups over the competing methods ranging from 2.5 to nearly 100 times.

Virtual history: a socially networked pedagogy of enlightenment

Background: Twenty-first-century undergraduates often find eighteenth-century culture difficult to access and, influenced by popular assumptions about the period in current media theory, characterise the century as individualist, underestimating the cultural significance of social networking in literary and political history. Purpose: This study set out to teach the history of social networking as culturally significant in the production of literary texts during the eighteenth century as well as to demonstrate the intellectual and compositional potential of today's social networking technologies. A virtual reconstruction of eighteenth-century London in Second Life and a semester project requiring the student recreation of 3D social spaces like coffee houses and gardens tested the uses of social networking tools to teach research methods and build disciplinary knowledge. Sources of evidence: Evidence of student learning outcomes is provided by three undergraduate courses in eighteenth-century culture, with 68 students total, that participated in Second Life re-enactment assignments on Island 18 during the autumn of 2008 and spring of 2009. The results of the student projects as well as student-completed evaluations and self-reflective essays about their experiences using virtual reality to learn history are consulted. Theoretical evidence by scholars of new media, eighteenth-century history and education provides a background for the study's impetus and goals. Main argument: Virtual reality provides an opportunity for educators of eighteenth-century culture to teach students, through the reflective and critical use of the social networking tools Second Life makes available, the significance of social networking in the history of ideas of that period. We dismantle the generalisations of scholars working outside of the period who characterise the eighteenth century as a solely individualist era, during which singular genius defined the enlightenment and propose a more culturally viable model of social collaboration, supported by communication technologies not so different from twenty-first-century instant messaging, blogging, twittering and emailing. Conclusions: Contrary to the recent findings of humanities educators who claim that digital social networking tools are distracting undergraduates from more valuable academic writing, we find that a task-oriented semester project requiring the construction of 3D historical social spaces teaches investigative research skills, deterritorialises disciplinary knowledge and promotes revision as an ongoing process.