Social Network Analysis

https://cyberleninka.org/article/n/854933.pdf

Social media analytics – Challenges in topic discovery, data collection, and data preparation

Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

The recent development of big data-oriented wireless technologies in terms of emerging 5G, edge computing, interconnected devices of the Internet of Things (IoT), and data analytics, as well as techniques, have enabled connected healthcare services for a happier and healthier life. Although, the quality of the healthcare services can be enhanced through big data-oriented wireless technologies, however, the challenges remain for not considering emotional care, especially for children, elderly, and mentally ill people. In this paper, we propose an emotion-aware connected healthcare system using a powerful emotion detection module. Different IoT devices are used to capture speech and image signals of a patient in a smart home scenario. These signals are used as the input to the emotion detection module. Speech and image signals are processed separately, and classification scores using these signals are fused to produce a final score to take a decision about the emotion. If the emotion is detected as pain, caregivers can visit the patient. Several experiments were performed to validate the proposed system, and good accuracies, up to 99.87%, were achieved for emotion detection. The proposed framework would greatly contribute personalized and seamless emotion-aware healthcare services toward 5G.

Emotion-Aware Connected Healthcare Big Data Towards 5G

Persuasive games and gamified systems are effective tools for motivating behavior change using various persuasive strategies. Research has shown that tailoring these systems can increase their efficacy. However, there is little knowledge on how game-based persuasive systems can be tailored to individuals of various personality traits. To advance research in this area, we conducted a large-scale study of 660 participants to investigate how different personalities respond to various persuasive strategies that are used in persuasive health games and gamified systems. Our results reveal that people's personality traits play a significant role in the perceived persuasiveness of different strategies. Conscientious people tend to be motivated by goal setting, simulation, self-monitoring and feedback; people who are more open to experience are more likely to be demotivated by rewards, competition, comparison, and cooperation. We contribute to the CHI community by offering design guidelines for tailoring persuasive games and gamified designs to a particular group of personalities.

Towards Personality-driven Persuasive Health Games and Gamified Systems

The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online. For example, this problem can affect content publishing, creation of friendships, messaging, profile browsing, and commenting. Moreover, fake identities are often created to disseminate spam, use the private information of other users, commit fraud, and so on. A malicious person can generate numerous fake accounts for these purposes to reach a large number of trustworthy users. Thus, these types of malicious accounts must be detected and deactivated as quickly as possible. However, this objective is challenging, because a fake account can exhibit trustworthy behaviors and have a type of name that will prevent it from being detected by the security system. In this paper, we provide a comprehensive survey of literature from 2006 to 2016 on Sybil attacks in online social networks and use of social networks as a tool to analyze and prevent these attack types. We first review existing Sybil attack definitions, including those in the context of online social networks. We then discuss a new taxonomy of Sybil attack defense schemes and methodologies. Finally, we compare the literature and identify areas for further research in Sybil attacks in online social networks.

Sybil Defense Techniques in Online Social Networks: A Survey

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.

A Credibility Analysis System for Assessing Information on Twitter

The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for the decision-making processes. Information credibility defines a measure of the fitness of the information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. In addition, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human–computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter. The known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in a social media context.

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Credibility in Online Social Networks: A Survey

With the enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. We attempted a novel approach regarding the process of data extraction and classification to contextualize large-scale networks in a proper manner. We also collected a significant number of user profiles from Twitter and YouTube, along with around 13 million channel activities. Extensive evaluations were conducted on real-world datasets of user activities for both social networks. The evaluation results show the effectiveness and utility of the proposed approach.

Leveraging Analysis of User Behavior to Identify Malicious Activities in Large-Scale Social Networks

Summary
This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph-based, considering the relationships among users to predict the spread information; human-based, using human perspectives to determine reliable sources; or machine learning-based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.

Muhammad Al-Qurishi

Papers

Sybil Defense Techniques in Online Social Networks: A Survey

A Credibility Analysis System for Assessing Information on Twitter

Credibility in Online Social Networks: A Survey

Leveraging Analysis of User Behavior to Identify Malicious Activities in Large-Scale Social Networks

Reputation‐based credibility analysis of Twitter social network users