Social Network Analysis

The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation.We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread.We next move to misinformation at the level of the individual, and review ...

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Misinformation and Its Correction: Continued Influence and Successful Debiasing

Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

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Content-based Recommender Systems: State of the Art and Trends

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.

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Frequent pattern mining: current status and future directions

With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

A survey on opinion mining and sentiment analysis

Deepfake videos are videos where the features of a person are replaced with the features of another person. Videos can be manipulated using powerful Deep Learning techniques. This technology may be used maliciously as a means of misinformation, manipulation, and persuasion. There are currently not many solutions to identify products of Deepfake technology, although there is significant research being conducted to tackle this problem. One often researched deep learning technology is the Generative Adversarial Network (GAN). These networks are commonly used to generate Deepfake videos but not used for their detection. In this work, we explore solutions based on GAN discriminators as a means to detect Deepfake videos. Using MesoNet as a baseline, we train a GAN and extract the discriminator as a dedicated module to detect Deepfakes. We test several discriminator architectures using multiple datasets to explore how the efficacy of the discriminator varies with different setups and training methods. Finally, we propose a model to boost the efficacy of a group of GAN discriminators using ensemble methods. Our results show that GAN discriminators, even augmented by ensemble methods, do not perform well on videos from unknown sources.

Deepfake Detection using GAN Discriminators

For a long time researchers have focused on the binary classification of domain names sent to DNS servers for resolutions to IP addresses. The objective is to identify malicious domains versus legitimate ones to protect networks from attacks. For legitimate domains, an emerging interest is to classify them into content categories to enable DNS servers deployed in an organization to monitor and potentially block the resolution of irrelevant domains. For example, a financial organization wants to flag gaming related domains, and an elementary school wants to block suspicious adult domains. Classifying a domain by just the domain name is a challenging task. Currently, there exist no publicly available datasets that include an extensive mapping of domains to content categories, since this is usually proprietary information. Our focus is three-fold in this work: a) to develop a data collection methodology and create rich labelled datasets that are appropriate for training such predictive models, b) to share the datasets with the research community by making them publicly available, and c) to evaluate and identify appropriate machine learning and deep learning algorithms for this problem domain. We consider two different datasets. The first is created following a SERP (Search Engine Response Page)-mining approach, having a set of content categories as input. The second is an enhancement of the DMOZ dataset that is publicly available, including both domains and category names as input. In addition to the dataset creation input and methodology, these two datasets differ in the content category number and distribution, yielding different results in our analysis. Overall, we observe that the deep learning-based approach carefully considers the key features of the input data and hence outperforms existing traditional machine learning pipelines, achieving 98.37% and 79.29% accuracy on the respective datasets.

An evaluation of machine learning methods for domain name classification

A social network represents ties between people. Modeling social interactions to understand the underlying mechanism of herd behavior has been a topic of interest for the past several decades. Our focus in this work is to investigate the phenomenon of extreme, unexpected growth and gain in a social network, and how different diffusion models can be used to identify the "key players" in such a process. In social network analysis, it is a tedious task to find the most influential entities, and how ideas or news spread through the network. A popular method to solve this task is to represent the social network as a graph. In a dynamic or an ever-growing social graph, discovering when an idea starts spreading, and not when it started first, reflects the real nature of how viral ideas catch-on in society. Also, instead of locating the root of an idea, it is crucial to see who propelled the idea to the surface, and triggered the spread. In this paper, we develop a method to identify the set of nodes who are responsible for extraordinary change in graph growth. We propose a methodology that allows us to identify the phenomenon called "tipping point", both in terms of the the role of a node or a community in graph growth, but also with respect to the time frame it occurred. Our framework is employing different influence propagation approaches, to locate critical nodes or communities and indicate their role in the early stages of the sudden graph growth. We demonstrate how the methodology is applied using a real-life social network. We aspire that the proposed framework will help researchers better understand this phenomenon and be able to identify agents responsible for spreading trends, beliefs, opinions, etc. in society.

The Tipping Point In Social Networks: Investigating the Mechanism Behind Viral Information Spreading

Identific ation of Influe ntial Social Networkers

In this paper we present a graph-based recommender system that is not relying on explicit item ratings to generate recommendations. Instead, it employs neighborhood-based and graph mining techniques to generate item profiles using their reviews' text. The proposed algorithm uses user review feedback to find products related to each other and tries to find a balance between similar products and highly popular products. It achieves this balance by ranking the products based on the similarity to the target product as well as its connectivity among similar products. The algorithm breaks the entire dataset into subgroups of similar products, which makes the proposed algorithm scalable as well. We present a proof-of-concept implementation of the proposed algorithm in the food product domain and present some preliminary results.

Magdalini Eirinaki

Papers

Deepfake Detection using GAN Discriminators

An evaluation of machine learning methods for domain name classification

The Tipping Point In Social Networks: Investigating the Mechanism Behind Viral Information Spreading

Identific ation of Influe ntial Social Networkers

A Graph-Based Recommender System for Food Products