Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Essays in platform economics

In this thesis we present three papers which investigate informative content generated by consumers, aiming to improve the usefulness for matching high quality products at lower prices. Following a general perspective, we explore platform product listing, searchable through a decision making mechanism. In a more specialized perspective, we take into account a dropping price modality service, differentiating the consumer benefit in the case of high or low quality product matching. Chapter 1 Product quality on platform markets. Abstract Many studies have questioned the meaning of \u201cproduct quality\u201d, hanging between a characteristic interpretation of a product for improving consumer satisfaction, and scientific approach to measure its benefits. Starting from the historical quality setting as mirror image of the price, we investigate the adoption of new signals, developed over the years to adjust the original relationship. Recently, bootstrapping by emperor of e-commerce platforms, the rating system has emerged as a reference contribute for product quality informativeness. We study this tendency, to show its failure in the presence of low price market and new brands. For this purpose, we collect User Generated Contents from a well-known online retailing platform. We capture and distill meaningful features in order to adjust the rating assigned by reviewers, and propose a novel quality formula able to increase the accuracy of the information provided to the consumer. We suggest that our formula better captures product quality, and, when adopted by a platform for sorting the products, it increases the products variety and, consequently the satisfaction of the consumer. Our proposal suggests a way to facilitate the consumer search (as we will show in the second chapter). Moreover, it can be used as a measure of market efficiency in the case of voluntary opacity of the platform in exposing product quality signals.Chapter 2 Optimizing Product Quality in Online Search Abstract Exploiting an original definition of product quality, based on the information we can get from the User Generated Content, and driven by a statistical learning algorithm, we propose a new ordering mechanism for product search on platforms. This product quality formula is imported in a decision making mechanism which adopts an optimal Stopping Rule, in order to set the optimal time to terminate the search process and choose a good to purchase. We show how the consumer can benefit from the implementation of such a mechanism, demonstrating an improvement in terms of consumer utility at different levels of price, with respect to other sorting traditionally adopted by platforms. We propose a utility function fitted to a Gumbel distribution, and we demonstrate a stochastic dominance of our model. Experimental evidences on the camera market category put in relevance the efficiency of our quality index for ranking the effective quality compared to the more traditional rating system. This is particularly true for the low-price accessory market segment of products, in which we show higher utility dominance and slightly higher elasticity of demand.Chapter 3 Price Matching and Platform Pricing Abstract In this study we investigate the effects of Price Matching Guarantees (PMG) commercial policies on U.S. online consumer electronics daily prices. By applying a Diff-in-Diff identification strategy we find evidence in favor of price reductions occurring after the PMG policy is repealed. We further investigate if such effect is heterogeneous according to products characteristics, by exploiting User Generated Contents (products popularity and quality) and online search visibility measures (Google Search Rank). Estimates suggest that for high quality (visibility) products PMG policies harms competition by keeping prices high, while for low quality (visibility) products, prices decrease during the policy validity period

Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport

Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), Ayudas Fundación BBVA a Equipos de Investigación Científica 2018 (BigKnowledge), DeepText (KK-2020/00088), funded by the Basque Government and the COLAB19/19 project funded by the UPV/EHU. Rodrigo Agerri is also funded by the RYC-2017-23647 fellowship and acknowledges the donation of a Titan V GPU by the NVIDIA Corporation

Trust Management for P2P application in Delay Tolerant Mobile Ad-hoc Networks. An Investigation into the development of a Trust Management Framework for Peer to Peer File Sharing Applications in Delay Tolerant Disconnected Mobile Ad-hoc Networks.

Security is essential to communication between entities in the internet. Delay tolerant and disconnected Mobile Ad Hoc Networks (MANET) are a class of networks characterized by high end-to-end path latency and frequent end-to-end disconnections and are often termed as challenged networks. In these networks nodes are sparsely populated and without the existence of a central server, acquiring global information is difficult and impractical if not impossible and therefore traditional security schemes proposed for MANETs cannot be applied. This thesis reports trust management schemes for peer to peer (P2P) application in delay tolerant disconnected MANETs. Properties of a profile based file sharing application are analyzed and a framework for structured P2P overlay over delay tolerant disconnected MANETs is proposed. The framework is implemented and tested on J2ME based smart phones using Bluetooth communication protocol. A light weight Content Driven Data Propagation Protocol (CDDPP) for content based data delivery in MANETs is presented. The CDDPP implements a user profile based content driven P2P file sharing application in disconnected MANETs. The CDDPP protocol is further enhanced by proposing an adaptive opportunistic multihop content based routing protocol (ORP). ORP protocol considers the store-carry-forward paradigm for multi-hop packet delivery in delay tolerant MANETs and allows multi-casting to selected number of nodes. Performance of ORP is compared with a similar autonomous gossiping (A/G) protocol using simulations. This work also presents a framework for trust management based on dynamicity aware graph re-labelling system (DA-GRS) for trust management in mobile P2P applications. The DA-GRS uses a distributed algorithm to identify trustworthy nodes and generate trustable groups while isolating misleading or untrustworthy nodes. Several simulations in various environment settings show the effectiveness of the proposed framework in creating trust based communities. This work also extends the FIRE distributed trust model for MANET applications by incorporating witness based interactions for acquiring trust ratings. A witness graph building mechanism in FIRE+ is provided with several trust building policies to identify malicious nodes and detect collusive behaviour in nodes. This technique not only allows trust computation based on witness trust ratings but also provides protection against a collusion attack. Finally, M-trust, a light weight trust management scheme based on FIRE+ trust model is presented

Enhancing trustability in MMOGs environments

Massively Multiplayer Online Games (MMOGs; e.g., World of Warcraft), virtual worlds (VW; e.g., Second Life), social networks (e.g., Facebook) strongly demand for more autonomic, security, and trust mechanisms in a way similar to humans do in the real life world. As known, this is a difficult matter because trusting in humans and organizations depends on the perception and experience of each individual, which is difficult to quantify or measure. In fact, these societal environments lack trust mechanisms similar to those involved in humans-to-human interactions. Besides, interactions mediated by compute devices are constantly evolving, requiring trust mechanisms that keep the pace with the developments and assess risk situations. In VW/MMOGs, it is widely recognized that users develop trust relationships from their in-world interactions with others. However, these trust relationships end up not being represented in the data structures (or databases) of such virtual worlds, though they sometimes appear associated to reputation and recommendation systems. In addition, as far as we know, the user is not provided with a personal trust tool to sustain his/her decision making while he/she interacts with other users in the virtual or game world. In order to solve this problem, as well as those mentioned above, we propose herein a formal representation of these personal trust relationships, which are based on avataravatar interactions. The leading idea is to provide each avatar-impersonated player with a personal trust tool that follows a distributed trust model, i.e., the trust data is distributed over the societal network of a given VW/MMOG. Representing, manipulating, and inferring trust from the user/player point of view certainly is a grand challenge. When someone meets an unknown individual, the question is “Can I trust him/her or not?”. It is clear that this requires the user to have access to a representation of trust about others, but, unless we are using an open source VW/MMOG, it is difficult —not to say unfeasible— to get access to such data. Even, in an open source system, a number of users may refuse to pass information about its friends, acquaintances, or others. Putting together its own data and gathered data obtained from others, the avatar-impersonated player should be able to come across a trust result about its current trustee. For the trust assessment method used in this thesis, we use subjective logic operators and graph search algorithms to undertake such trust inference about the trustee. The proposed trust inference system has been validated using a number of OpenSimulator (opensimulator.org) scenarios, which showed an accuracy increase in evaluating trustability of avatars. Summing up, our proposal aims thus to introduce a trust theory for virtual worlds, its trust assessment metrics (e.g., subjective logic) and trust discovery methods (e.g., graph search methods), on an individual basis, rather than based on usual centralized reputation systems. In particular, and unlike other trust discovery methods, our methods run at interactive rates.MMOGs (Massively Multiplayer Online Games, como por exemplo, World of Warcraft), mundos virtuais (VW, como por exemplo, o Second Life) e redes sociais (como por exemplo, Facebook) necessitam de mecanismos de confiança mais autónomos, capazes de assegurar a segurança e a confiança de uma forma semelhante à que os seres humanos utilizam na vida real. Como se sabe, esta não é uma questão fácil. Porque confiar em seres humanos e ou organizações depende da percepção e da experiência de cada indivíduo, o que é difícil de quantificar ou medir à partida. Na verdade, esses ambientes sociais carecem dos mecanismos de confiança presentes em interacções humanas presenciais. Além disso, as interacções mediadas por dispositivos computacionais estão em constante evolução, necessitando de mecanismos de confiança adequados ao ritmo da evolução para avaliar situações de risco. Em VW/MMOGs, é amplamente reconhecido que os utilizadores desenvolvem relações de confiança a partir das suas interacções no mundo com outros. No entanto, essas relações de confiança acabam por não ser representadas nas estruturas de dados (ou bases de dados) do VW/MMOG específico, embora às vezes apareçam associados à reputação e a sistemas de reputação. Além disso, tanto quanto sabemos, ao utilizador não lhe é facultado nenhum mecanismo que suporte uma ferramenta de confiança individual para sustentar o seu processo de tomada de decisão, enquanto ele interage com outros utilizadores no mundo virtual ou jogo. A fim de resolver este problema, bem como os mencionados acima, propomos nesta tese uma representação formal para essas relações de confiança pessoal, baseada em interacções avatar-avatar. A ideia principal é fornecer a cada jogador representado por um avatar uma ferramenta de confiança pessoal que segue um modelo de confiança distribuída, ou seja, os dados de confiança são distribuídos através da rede social de um determinado VW/MMOG. Representar, manipular e inferir a confiança do ponto de utilizador/jogador, é certamente um grande desafio. Quando alguém encontra um indivíduo desconhecido, a pergunta é “Posso confiar ou não nele?”. É claro que isto requer que o utilizador tenha acesso a uma representação de confiança sobre os outros, mas, a menos que possamos usar uma plataforma VW/MMOG de código aberto, é difícil — para não dizer impossível — obter acesso aos dados gerados pelos utilizadores. Mesmo em sistemas de código aberto, um número de utilizadores pode recusar partilhar informações sobre seus amigos, conhecidos, ou sobre outros. Ao juntar seus próprios dados com os dados obtidos de outros, o utilizador/jogador representado por um avatar deve ser capaz de produzir uma avaliação de confiança sobre o utilizador/jogador com o qual se encontra a interagir. Relativamente ao método de avaliação de confiança empregue nesta tese, utilizamos lógica subjectiva para a representação da confiança, e também operadores lógicos da lógica subjectiva juntamente com algoritmos de procura em grafos para empreender o processo de inferência da confiança relativamente a outro utilizador. O sistema de inferência de confiança proposto foi validado através de um número de cenários Open-Simulator (opensimulator.org), que mostrou um aumento na precisão na avaliação da confiança de avatares. Resumindo, a nossa proposta visa, assim, introduzir uma teoria de confiança para mundos virtuais, conjuntamente com métricas de avaliação de confiança (por exemplo, a lógica subjectiva) e em métodos de procura de caminhos de confiança (com por exemplo, através de métodos de pesquisa em grafos), partindo de uma base individual, em vez de se basear em sistemas habituais de reputação centralizados. Em particular, e ao contrário de outros métodos de determinação do grau de confiança, os nossos métodos são executados em tempo real

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

System support for robust data collection in wireless sensing systems

This dissertation studied how to provide system support for robust data collection in wireless sensing systems through addressing a few urgent design issues in the existing systems. A wireless sensing system may suffer issues arising at the sensors, during the data transmission, and during the data access by applications. Due to the unique characteristics of wireless sensing systems, certain conventional solutions for networked systems may not work well with these issues. We developed approaches to resolve these urgent problems in the design of wireless sensing systems. Specially, we have achieved the following: (1) we developed a resilient trust model to effectively detect faulty data in wireless sensing systems due to either sensor malfunctioning or malicious attempts to report false data; (2) we developed a low-cost, self-contained, accurate localization system for small-sized ground robotic vehicles, which enhances the wireless sensing systems containing mobile sensors by providing more accurate and highly available location data, with only limited overhead in economic cost and management; (3) we designed and implemented a robust trust-aware routing framework to secure multi-hop routing through a set of sensors in wireless sensing systems; (4) we developed a privacy-preserving wireless sensing system, which protects the user privacy while allowing arbitrary third-party applications to extract knowledge from the collected data

Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network

A risk-level assessment system based on the STRIDE/DREAD model for digital data marketplaces

Security is a top concern in digital infrastructure and there is a basic need to assess the level of security ensured for any given application. To accommodate this requirement, we propose a new risk assessment system. Our system identifies threats of an application workflow, computes the severity weights with the modified Microsoft STRIDE/DREAD model and estimates the final risk exposure after applying security countermeasures in the available digital infrastructures. This allows potential customers to rank these infrastructures in terms of security for their own specific use cases. We additionally present a method to validate the stability and resolution of our ranking system with respect to subjective choices of the DREAD model threat rating parameters. Our results show that our system is stable against unavoidable subjective choices of the DREAD model parameters for a specific use case, with a rank correlation higher than 0.93 and normalised mean square error lower than 0.05