Reputational Privacy and the Internet: A Matter for Law?

Reputation - we all have one. We do not completely comprehend its workings and are mostly unaware of its import until it is gone. When we lose it, our traditional laws of defamation, privacy, and breach of confidence rarely deliver the vindication and respite we seek due, primarily, to legal systems that cobble new media methods of personal injury onto pre-Internet laws. This dissertation conducts an exploratory study of the relevance of law to loss of individual reputation perpetuated on the Internet. It deals with three interrelated concepts: reputation, privacy, and memory. They are related in that the increasing lack of privacy involved in our online activities has had particularly powerful reputational effects, heightened by the Internet’s duplicative memory. The study is framed within three research questions: 1) how well do existing legal mechanisms address loss of reputation and informational privacy in the new media environment; 2) can new legal or extra-legal solutions fill any gaps; and 3) how is the role of law pertaining to reputation affected by the human-computer interoperability emerging as the Internet of Things? Through a review of international and domestic legislation, case law, and policy initiatives, this dissertation explores the extent of control held by the individual over her reputational privacy. Two emerging regulatory models are studied for improvements they offer over current legal responses: the European Union’s General Data Protection Regulation, and American Do Not Track policies. Underscoring this inquiry are the challenges posed by the Internet’s unique architecture and the fact that the trove of references to reputation in international treaties is not making its way into domestic jurisprudence or daily life. This dissertation examines whether online communications might be developing a new form of digital speech requiring new legal responses and new gradients of personal harm; it also proposes extra-legal solutions to the paradox that our reputational needs demand an overt sociality while our desire for privacy has us shunning the limelight. As we embark on the Web 3.0 era of human-machine interoperability and the Internet of Things, our expectations of the role of law become increasingly important

International overview on the legal framework for highly automated vehicles

The evolution of Autonomous and automated technologies during the last decades has been constant and maintained. All of us can remember an old film, in which they shown us a driverless car, and we thought it was just an unreal object born of filmmakers imagination. However, nowadays Highly Automated Vehicles are a reality, even not in our daily lives. Hardly a day we don’t have news about Tesla launching a new model or Google showing the new features of their autonomous car. But don’t have to travel far away from our borders. Here in Europe we also can find different companies trying, with more or less success depending on with, not to be lagged behind in this race. But today their biggest problem is not only the liability of their innovative technology, but also the legal framework for Highly Automated Vehicles. As a quick summary, in only a few countries they have testing licenses, which not allow them to freely drive, and to the contrary most nearly ban their use. The next milestone in autonomous driving is to build and homogeneous, safe and global legal framework. With this in mind, this paper presents an international overview on the legal framework for Highly Automated Vehicles. We also present de different issues that such technologies have to face to and which they have to overcome in the next years to be a real and daily technology

ICE-B 2010:proceedings of the International Conference on e-Business

The International Conference on e-Business, ICE-B 2010, aims at bringing together researchers and practitioners who are interested in e-Business technology and its current applications. The mentioned technology relates not only to more low-level technological issues, such as technology platforms and web services, but also to some higher-level issues, such as context awareness and enterprise models, and also the peculiarities of different possible applications of such technology. These are all areas of theoretical and practical importance within the broad scope of e-Business, whose growing importance can be seen from the increasing interest of the IT research community. The areas of the current conference are: (i) e-Business applications; (ii) Enterprise engineering; (iii) Mobility; (iv) Business collaboration and e-Services; (v) Technology platforms. Contributions vary from research-driven to being more practical oriented, reflecting innovative results in the mentioned areas. ICE-B 2010 received 66 submissions, of which 9% were accepted as full papers. Additionally, 27% were presented as short papers and 17% as posters. All papers presented at the conference venue were included in the SciTePress Digital Library. Revised best papers are published by Springer-Verlag in a CCIS Series book

Video Vortex reader : responses to Youtube

The Video Vortex Reader is the first collection of critical texts to deal with the rapidly emerging world of online video – from its explosive rise in 2005 with YouTube, to its future as a significant form of personal media. After years of talk about digital convergence and crossmedia platforms we now witness the merger of the Internet and television at a pace no-one predicted. These contributions from scholars, artists and curators evolved from the first two Video Vortex conferences in Brussels and Amsterdam in 2007 which focused on responses to YouTube, and address key issues around independent production and distribution of online video content. What does this new distribution platform mean for artists and activists? What are the alternatives

Transparency reporting on terrorist and violent extremist content online: An update on the global top 50 content sharing services

This benchmarking report explores the degree to which the world’s top 50 online content-sharing services’ approaches to terrorist and violent extremist content (TVEC) online have evolved since a first report in 2020. This new edition finds there has been tangible progress: 11 services have issued TVEC-specific transparency reports over the past year (6 more than in 2020); and the 5 services that already issued such reports now provide additional information. However, transparency reports expressly addressing TVEC remain uncommon and services continue to use different metrics, definitions and reporting frequencies. It remains difficult to gain an industry-wide perspective on the efficacy of companies’ measures to combat TVEC online and how they may affect human rights. Meanwhile, there is a growing risk of regulatory fragmentation due to unco-ordinated transparency requirements across jurisdictions. There is an urgent need for increased, and more comparable, TVEC reporting

Sentiment analysis as a service

This research focuses on the design and development of a service composition based framework that enables the execution of services for social media based sentiment analysis. Our research develops novel analytical models, composition techniques and algorithms which use services as a mean for sentiment abstraction, processing and analysis from large scale social media data. Current sentiment analysis techniques require specialized skill of data science and machine learning. Moreover, traditional approaches rely on laborious and time-consuming activities such as manual dataset labelling, data model training and validation. This makes overall sentiment analysis process a challenging task. In comparison, services are `ready-made' software solutions that can be composed on-demand for developing complex applications without indulging in the domain specific details. This thesis investigates a novel approach that transforms traditional social media based sentiment analysis process into a service composition driven solution. In this thesis, we begin by developing a novel service framework that replaces the traditional sentiment analysis tasks with online services. Our framework includes a new service model to present services required for sentiment analysis. We develop a semantic service composition model and algorithm that dynamically composes various services for data collection, noise filtering and sentiment extraction. In particular, we focus on abstracting sentiment based on location and time. We then focus on enhancing the flexibility of our proposed service framework to compose appropriate sentiment analysis services for highly dynamic and changing features of social media platforms. In addition, we aim to efficiently process and analyze large scale social media data. In order to enhance our service composition framework, we propose a novel approach to formalize social media platforms as cloud enabled services. We develop a functional and quality of service (QoS) model that captures various dynamic features of social media platforms. In addition, we devise a cloud based service model to access social media platforms as services by using the Ontology Web Language for Service (OWL-S). Secondly, we integrate the QoS model into our existing composition framework. It enables our framework to dynamically assess the QoS of multiple social media platforms, and simultaneously compose appropriate services to extract, process, analyze and integrate the sentiment results from large scale data. Finally, we concentrate on efficient utilization of the sentiment analysis extracted from large scale data. We formulate a meta-information composition model that transforms and stores sentiment obtained from large streams of data into re-usable information. Later, the re-usable information is on-demand integrated and delivered to end users. To demonstrate the performance and test the effectiveness of our proposed models, we develop prototypes to evaluate our composition framework. We design several scenarios and conduct a series of experiments using real-world social media datasets. We present the results and discuss the outcomes which validate the performance of our research

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management