Multimedia Social Networks Research Articles

Discovering why people believe disinformation about healthcare.

Disinformation-false information intended to cause harm or for profit-is pervasive. While disinformation exists in several domains, one area with great potential for personal harm from disinformation is healthcare. The amount of disinformation about health issues on social media has grown dramatically over the past several years, particularly in response to the COVID-19 pandemic. The study described in this paper sought to determine the characteristics of multimedia social network posts that lead them to believe and potentially act on healthcare disinformation. The study was conducted in a neuroscience laboratory in early 2022. Twenty-six study participants each viewed a series of 20 either honest or dishonest social media posts, dealing with various aspects of healthcare. They were asked to determine if the posts were true or false and then to provide the reasoning behind their choices. Participant gaze was captured through eye tracking technology and investigated through "area of interest" analysis. This approach has the potential to discover the elements of disinformation that help convince the viewer a given post is true. Participants detected the true nature of the posts they were exposed to 69% of the time. Overall, the source of the post, whether its claims seemed reasonable, and the look and feel of the post were the most important reasons they cited for determining whether it was true or false. Based on the eye tracking data collected, the factors most associated with successfully detecting disinformation were the total number of fixations on key words and the total number of revisits to source information. The findings suggest the outlines of generalizations about why people believe online disinformation, suggesting a basis for the development of mid-range theory.

Deep Learning-Based Community Detection Approach on Multimedia Social Networks

Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.

Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.

Are People Really Social in Porn 2.0?

Social Web 2.0 features have become a vital component in a variety of multimedia systems, e.g., YouTube, Last.fm. Interestingly, adult video websites are also starting to adopt these Web 2.0 principles, giving rise to the term "Porn 2.0". This paper examines a large Porn 2.0 social network, through data covering 563k users. We explore a number of unusual behavioural aspects that set this apart from more traditional multimedia social networks. We particularly focus on the role of gender and sexuality, to understand how these different groups behave. A number of key differences are discovered relating to social demographics, modalities of interaction and content consumption habits, shedding light on this understudied area of online activity.

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Application of clustering algorithm in social network user scenario prediction

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks

The past decade has witnessed the emergence and progress of multimedia social networks (MSNs), which have explosively and tremendously increased to penetrate every corner of our lives, leisure and work. Moreover, mobile Internet and mobile terminals enable users to access to MSNs at anytime, anywhere, on behalf of any identity, including role and group. Therefore, the interaction behaviors between users and MSNs are becoming more comprehensive and complicated. This paper primarily extended and enriched the situation analytics framework for the specific social domain, named as SocialSitu , and further proposed a novel algorithm for users’ intention serialization analysis based on classic Generalized Sequential Pattern (GSP). We leveraged the huge volume of user behaviors records to explore the frequent sequence mode that is necessary to predict user intention. Our experiment selected two general kinds of intentions: playing and sharing of multimedia, which are the most common in MSNs, based on the intention serialization algorithm under different minimum support threshold ( Min_Support ). By using the users’ microscopic behaviors analysis on intentions, we found that the optimal behavior patterns of each user under the Min_Support , and a user's behavior patterns are different due to his/her identity variations in a large volume of sessions data.

Give Attention to Overlapping Network Detection in Networks for Multimedia

Finding overlapping agencies from multimedia social networks is an thrilling and important trouble in records mining and recommender systems but, existing overlapping network discovery often generates overlapping community structures with superfluous small groups. Network detection in a multimedia and social network is a conducive difficulty in the network gadget and it helps to understand and learn the overall network shape in element. Those are essentially the dividing wall of network nodes into a few subgroups in which nodes within these subgroups are densely linked, but the connections are sparser in between the subgroups. Social network analysis is widely widespread domain which draws the attention of many information mining experts. Some wide variety of actual community common characteristics which it shares are facebook, Twitter show off the idea of network shape inside the community. Social network is represented as a community graph. Detecting the groups entails locating the densely linked nodes.

Prediction of User Behavior Pattern in Multimedia Social Network

Prediction of User Behavior Pattern in Multimedia Social Network

A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach’s effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.

A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users’ interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users’ current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users’ current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t) . Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.

Extreme events management using multimedia social networks

Although various researches have demonstrated that Social Networks offer a great opportunity to detect and manage emergencies and disasters in real life applications, in this paper we will show how the use of Multimedia Big Data extracted from modern On-Line Social Nets may enhance both the real time detection and the alerts diffusion in a well defined geographic area. In particular, we propose a Multimedia Big Data system that use both an incremental clustering event detection approach enriched with the analysis of multimedia content and a bio-inspired influence analysis technique, to support alert spread over a social network. Experiments are carried out on a real case study.

Correction to: Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand

Recovering a 3D human-pose in the form of an abstracted skeleton from a 2D image suffers from loss of depth information. Assuming the projected human-pose is represented by a set of 2D landmarks capturing the human-pose limbs, recovering back the original 3D locations is an ill posed problem. To recover a 3D configuration, camera localization in 3D space plays a major role, an inaccurate camera localization might mislead the recovery process. In this paper, we propose a 3D camera localization model using only human-pose appearance in a 2D image (i.e., the set of 2D landmarks). We apply a supervised multi-class logistic regression to assign the camera location in 3D space. In the learning process, we assume a set of predefined labeled camera locations. The features we train consist of relative length of limbs and 2D shape context. The goal is to build a relation between these projected landmarks and the camera location in 3D space. This kind of analysis allows us to reconstruct 3D human-poses based on the 2D projection only without any predefined camera parameters. Also, makes real-time multimedia exchange more reliable specially for human-pose related tasks. We test our model on a set of real images showing a variety of camera locations.

Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand

Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand

Lattice based signature with outsourced revocation for Multimedia Social Networks in cloud computing

Identity-based signature schemes enable any pair of users to communicate securely and to verify each other’s identity without exchanging private or public keys, without keeping key directories, and without using the services of a third party. Such paradigms are very suitable for an emerging scenario of Multimedia Social Networks (MSNs), in which there are a large number of users, dynamic interaction and huge content sharing. A revocable identity-based signature(RIBS) scheme, proposed by Tsai et al., provides a revocation mechanism for controlling user’s access dynamically. To capture a realistic and efficient scenario, In 2017, XiaoYing Jia et al. introduced an additional important component, called Cloud Revocation Server(CRS), where most of the computations needed during key-updates are of loaded to the CRS. With the surprising development of quantum computation technology in recent years, IBS schemes mentioned above, based on conventional number theory problem, would become vulnerable. Recently, lattice-based cryptography schemes were proved to be secure against quantum attacks. Although such efficient RIBS scheme based on Computational Diffle-Hellam Problem(CDH) assumption has been proposed, all the lattice-based RIBS do not achieve this realistic and efficient property. In this paper, we propose the first lattice-based RIBS with outsourced Cloud Service Provider(CSP). In our scheme, a user’s private key is composed of both an partial private key and a time update key. The time update key is periodically updated by CSP and is transmitted over a public channel. Based on the hardness assumption of Short Integer Solution (SIS), we demonstrate that the proposed lattice-based RIBS scheme with outsourced revocation in cloud computing provides existential unforgeability against adaptive chosen-message attacks in the random oracle. As compared to the existing IBS schemes over lattices, our RIBS scheme has better performance in terms of energy consumption, signature size, signing key size, and the revocation mechanism with public channels. As the underlying lattice problem is intractable even for quantum computers, our scheme would work well in the quantum age.

A Novel Authorization Scheme for Multimedia Social Networks Under Cloud Storage Method by Using MA-CP-ABE

This article finds that with the development of network technology and the change of a users' social habits, the existing multimedia social network, whether it is one-to-one authorization or one-to-many authorization, are having difficulty meeting the needs of users and cloud storage service. In order to solve this problem, this article proposes a new authorization scheme of multimedia community, based on attribute encryption. Use MA-CP-ABE solution to enhance system service efficiency. This program contributes to two aspects: (1) combining user trust with attribute-based authorizations, and (2) enhancing the flexibility and security of the solution. The scheme has been applied in the prototype system and achieved good results. Results prove that the program has good application value for cloud storage technology, IoT and many other fields.

New Certificateless Aggregate Signature Scheme for Healthcare Multimedia Social Network on Cloud Environment

With the application of sensor technology in the field of healthcare, online data sharing in healthcare industry attracts more and more attention since it has many advantages, such as high efficiency, low latency, breaking the geographical location, and time constraints. However, due to the direct involvement of patient health information, the privacy and integrity of medical data have become a matter of much concern to the healthcare industry. To retain data privacy and integrity, a number of digital signature schemes have been introduced in recent years. Unfortunately, most of them suffer serious security attacks and do not perform well in terms of computation overhead and communication overhead. Very recently, Pankaj Kumar et al. proposed a certificateless aggregate signature scheme for healthcare wireless sensor network. They claimed that their signature scheme was able to withstand a variety of attacks. However, in this paper, we find that their scheme fails to achieve its purpose since it is vulnerable to signature forgery attack and give the detailed attack process. Then, we propose a new certificateless aggregate signature scheme to fix the security flaws and formally prove that our proposed scheme is secure under the computationally hard Diffie-Hellman assumption. Security analysis and performance evaluation demonstrate that the security of our proposal is improved while reducing the computation cost. Compared with Pankaj Kumar et al.'s scheme, our proposed scheme is more efficient and suitable for the healthcare wireless sensor networks (HWSNs) to maintain security at various levels.

Analytic Method for Estimating the User Behavior Patterns in Multimedia Social Networks

Now a days the multimedia social networks plays major role in our daily life. All the earlier MSNs are validated and developed very well. The past decade has witnessed the emergence and progress of multimedia social networks (MSNs), which have explosively and tremendously increased to penetrate every corner of our lives, leisure and work. As well as, the users are enabled by Mobile internet & terminals for accessing the MSNs where ever they are and when they want with the help of any identity. It may be a group or a role. So, it become very complicated & comprehensive to provide the behavior’s interaction between MSNs as well as in users. The implemented system having the advancements and developed framework of the analytics in a particular domain; which is called as SocialSitu, And We implemented an algorithm which is named as novel for the analysis of the serialized users intention according to the typical GSP which is the short form of Generalized Sequential Pattern. An enormous number of user’s behavior records were broken for exploring the usual sequence mode. It is mandatory for guessing the intention of the user. We considered the two types of intentions. Those are playing multimedia & sharing multimedia. These 2 are widely used in regular MSNs with the help of intention serialization algorithm in control of various min support threshold (Min_Support). With the help of microscopic behavior analysis of the users, we find out the each user behavior patterns which are in optimized manner in control of the Min_Support. Based on the different identities of the user, the behavior patterns of the users may be varied in session data which is very large.

Multimedia story creation on social networks

The paper aims at proposing an original summarization technique from Online Social Networks (OSNs) for multimedia stories’ creation. In particular, for each Multimedia Social Network (MuSN) – i.e. an OSN focusing on the management and sharing of multimedia information – we leverage a graph-based modeling approach and exploit influence analysis methodologies to detect the most important multimedia objects related to one or more topics of interest. Consecutively, from the list of candidate objects we obtain a multimedia summary on the basis of a novel summarization model and some heuristics, whose purpose is to generate multimedia stories with priority (w.r.t. some user keywords), continuity, variety and not receptiveness features. The effectiveness of the proposed approach is shown by the performed experiments on Flickr.