The phenomenon of incest obscenity: Social network and sentiment analysis on YouTube

Chapter 3 - Privacy-Preserving Social Network Integration, Analysis, and Mining

Social Network Analysis and Mining (SNAM) techniques have drawn significant attention in the recent years due to the popularity of online social media. With the advance of Web 2.0 and SNAM techniques, tools for aggregating, sharing, investigating, and visualizing social network data have been widely explored and developed. SNAM is effective in supporting intelligence and law enforcement force to identify suspects and extract communication patterns of terrorists or criminals. In our previous work, we have shown how social network analysis and visualization techniques are useful in discovering patterns of terrorist social networks. Attribute to the advance of SNAM techniques, relationships among social actors can be visualized through network structures explicitly and implicit patterns can be discovered automatically. Despite the advance of SNAM, the utility of a social network is highly affected by its d completeness. Missing edges or nodes in a social network will reduce the utility of the network. For example, SNAM techniques may not be able to detect groups of social actors if some of the relationships among these social actors are not available. Similarly, SNAM techniques may overestimate the distance between two social actors if some intermediate nodes or edges are missing. Unfortunately, it is common that an organization only have a partial social network due to its limited information sources. In public safety domain, each law enforcement unit has its own criminal social network constructed by the data available from the criminal intelligence and crime database but this network is only a part of the global criminal social network, which can be obtained by integrating criminal social networks from all law enforcement units. However, due to the privacy policy, law enforcement units are not allowed to share the sensitive information of their social network data. A naive and yet practical approach is anonymizing the social network data before publishing or sharing it. However, a modest privacy gains may reduce a substantial SNAM utility. It is a challenge to make a balance between privacy and utility in social network data sharing and integration. In order to share useful information among different organizations without violating the privacy policies and preserving sensitive information, we propose a generalization and probabilistic approach of social network integration in this paper. Particularly, we propose generalizing social networks to preserve privacy and integrating the probabilistic models of the shared information for SNAM. To preserve the identity of sensitive nodes in social network, a simple approach in the literature is removing all node identities. However, it only allows us to investigate of the structural properties of such anonymized social network, but the integration of multiple anonymized social networks will be impossible. To make a balance between privacy and utility, we introduce a social network integration framework which consists of three major steps: (i) constructing generalized sub-graph, (ii) creating generalized information for sharing, and (iii) social networks integration and analysis. We also propose two sub-graph generalization methods namely, edge betweenness based (EBB) and K-nearest neighbor (KNN). We evaluated the effectiveness of these algorithms on the Global Salafi Jihad terrorist social network.

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

The social network model is powerful enough to provide for the analysis and study of a variety of application domains from daily life, including health care and health informatics. After the widespread appearance of automated tools capable of deriving and analyzing social networks, social network analysis (SNA) and mining in the health care domain has recently received considerable attention for its key role in understanding how various bodies within the health care system form communities and how they are socially connected with each other. This understanding helps enhance the organizational structures and process flows, among others. In this article, we show how SNA techniques can solve issues in the medical referral system in the Canadian health care system and the like, by analyzing the social network of general practitioners (GPs) and specialists (SPs). One of the main targets is to optimize the communication between GPs and SPs with hopes of decreasing the waiting time of patients to be seen by SPs. Various SNA and mining techniques are described and analyzed, backed by reporting some experimental results.

ObjectivesSocial network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social...

ObjectivesSocial network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social...

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models.

This article presents the results of a study aimed at understanding the value of lecture recordings to student learning. We analysed transcripts of discussions on social media (Facebook) that students generated on the value of lecture recordings. Students discussed whether recording lectures and making them available should be compulsory. While the efficacy of lecture recording has been studied using conventional methods (e.g. questionnaires and interviews) on highly structured data, we employed social network and sentiment analysis techniques to examine individual messages posted on the Student Union’s Facebook page. We chose to employ social network and sentiment analysis because these methods are useful in examining semi-structured and unstructured social media data. Overall findings suggest students generally view lecture recordings as resources for supplementing live lectures rather than replacing them. Students stated that lecture recordings could facilitate the creation of an inclusive learning environment and inculcate a positive learning experience. Work presented in this article adds to the growing debate on the institutional deployment of lecture recordings and their impact on students’ engagement and learning. It also demonstrated how educational researchers could utilise social network and sentiment analysis to examine critical issues in education.

Abstract- The YouTube Comments and Videos Sentiment Analysis project is an advanced system that automates the sentiment classification of both comments and video transcripts associated with YouTube videos. Using Natural Language Processing (NLP) and cutting-edge deep learning models, the system determines whether the sentiments expressed are positive, negative, or neutral. The system integrates tools such as the YouTube Data API to extract comments and the YouTube Transcript API to retrieve video transcripts. Advanced pre-processing techniques like tokenization, lemmatization, and stopword removal are applied to prepare the text for sentiment analysis. The project employs pre-trained models, including BERT for comments sentiment analysis and LSTM and GRU for video transcript sentiment analysis, ensuring high accuracy in sentiment classification. Users interact with the system through a web-based interface where they can input a YouTube video URL. The system then retrieves comments and transcripts, performs sentiment analysis, and visualizes the results in an intuitive graphical format. To ensure the system delivers reliable results, various performance indicators are used to evaluate how well it identifies and interprets sentiment. These measures help verify the system’s effectiveness and consistency. This project aims to offer real-time analysis of public opinion, providing valuable insights to content creators, marketers, and businesses. As a result, they can make smarter and faster decisions based on current trends and audience reactions. Keywords- Sentiment, YouTube Comments, YouTube URL, LSTM, GRU, BERT, NLP.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking.

As a non-ministerial government institution in charge of statistics, BPS-Statistics Indonesia must be able to provide quality statistical data through integrated and international standard statistical activities. Integrated statistical activities require a process of statistical activities that does not silo thinking. This silo thinking can be detrimental to BPS-Statistics Indonesia, especially in terms of overcoming the problem of statistical activities that have actually occurred in previous statistical activities. From these problems it is necessary to identify important knowledge that is useful to be able to be accessed and used as a lesson learned by other statistical activities. In Knowledge Management there is a tool to help identify important knowledge, namely Knowledge Map, which is assisted by analysis using the Social Network Analysis (SNA) technique. To solve the problem, BPS-Statistics Indonesia needs to conduct a study on the application of this knowledge maps with SNA technique. In this study, a study literature on knowledge maps with SNA was carried out. This literature study is in the form of Systematic Literature Review (SLR). From this literature study, an overview of the fields used as research subjects on knowledge maps with SNA, description of objectives and research suggestions currently used by researchers. This study found that the most used fields of research were non-business fields, especially in the fields of education and research. Findings regarding the purpose of the study are divided into making a design / framework or analyzing a problem. From the synthesis of SLR papers it was found that research on Knowledge Map with SNA, the purpose mostly to analyzing a problem. In this study it was also found that the suggestions for most subsequent research were suggestions for modification or refinement of research methods on knowledge maps with SNA. From these findings, BPS-Statistics Indonesia is expected to be able to take advantage and can make this literature study as the basis for the implementation of knowledge maps with SNA to solve the problem of silo thinking.

The Scientometric, Social Network and Scientographic Analysis of an Industrial Engineering Department

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semanticSNA, information diffusion, viral marketing, and influential node mining.WIREs Data Mining Knowl Discov2013, 3:408–444. doi: 10.1002/widm.1105This article is categorized under:Algorithmic Development > Web MiningCommercial, Legal, and Ethical Issues > Social ConsiderationsTechnologies > Structure Discovery and Clustering

The seventh SNA-KDD workshop is proposed as the seventh in a successful series of workshops on social network mining and analysis co-held with KDD, soliciting experimental and theoretical work on social network mining and analysis in both online and offline social network systems. In recent years, social network research has advanced significantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale offline social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are increasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topological properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining social networks. These issues have important implications on community discovery, anomaly detection, trend prediction and can enhance applications in multiple domains such as information retrieval, recommendation systems, security and so on. The past SNA-KDD workshops have achieved significant attentions from the world-wide researchers working in different aspects of social network analysis, including knowledge discovery and data mining in social network, social network modeling, multi-agent based social network simulation, complex generic network analysis and other related studies that can bring inspirations or be directly applied to social network analysis. Each year we received more than 30 submissions. The average acceptance rate is around 1/3.