A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Awareness of digital footprint management in the new media amongst youth

The industrial revolution creates a new era where everything can be accessed via the Internet. However, despite all the advantages, the new era of technology comes with disadvantages as well. Studies have shown that the youth category is the group that is a synonym to technology. Most of the youth nowadays enjoy using the features but lacked the information on the footprint of their Internet browsing history. When individuals engaged with the Internet, they generated a complex trail of these digital footprints that may include their various presentation of self, based on social profiles and comments, traces of their activities, interest, interaction and anything else they choose to share online. However, the extent to which netizens are aware that they are creating these tracks and traces and have some ability to manipulate and control them is unclear at present. Thus, the objective of this paper is to identify the level of awareness among youth about the digital footprint. The research started with a structured survey distributed to respondents as a data collection requirement for analysis. A quantitative research method is employed in this research. Survey was conducted to the three main areas namely urban, suburban and rural as a sample of the population. Results were presented in the form of description analysis with respondents demographic and perception of respondents based on digital footprint awareness

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

Patina : layering a history-of-use on digital objects

Thesis (S.M.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1998.Includes bibliographical references (leaves 57-59).by Ansel Arjan Schütte.S.M

A tourism overcrowding sensor using multiple radio techniques detection

The motivation for this dissertation came from the touristic pressure felt in the historic neighborhoods of Lisbon. This pressure is the result of the rise in the number of touristic arrivals and the proliferation of local accommodation. To mitigate this problem the research project in which this dissertation is inserted aims to disperse the pressure felt by routing the tourists to more sustainable locations and locations that are not crowded. The goal of this dissertation is then to develop a crowding sensor to detect, in real-time, the number of persons in its vicinity by detecting how many smartphones it observes in its readings. The proposed solution aims to detect the wireless trace elements generated by the normal usage of smartphones. The technologies in which the sensor will detect devices are Wi-Fi, Bluetooth and the mobile network. For testing the results gathered by the sensor we developed a prototype that was deployed on our campus and in a museum, during an event with strong attendance. The data gathered was stored in a time-series database and a data visualization tool was used to interpret the results. The overall conclusions of this dissertation are that it is possible to build a sensor that detects nearby devices thereby allowing to detect overcrowding situations. The prototype built allows to detect crowd mobility patterns. The composition of technologies and identity unification are topics deserving future research.A motivação para a presente dissertação surgiu da pressão turística sentida nos bairros históricos de Lisboa. Esta pressão é a consequência de um crescimento do número de turistas e de uma cada vez maior utilização e proliferação do alojamento local. Para mitigar este problema o projeto de investigação em que esta dissertação está inserida pretende dispersar os turistas por locais sustentáveis e que não estejam sobrelotados. O objetivo desta dissertação é o de desenvolver um sensor que consiga detetar, em tempo real, detetar quantas pessoas estão na sua proximidade com base nos smartphones que consegue detetar. A solução proposta tem como objetivo detetar os traços gerados pela normal utilização de um smartphone. As tecnologias nas quais o sensor deteta traços de utilização são Wi-Fi, Bluetooth e a rede móvel. Para realizar os testes ao sensor, foi desenvolvido um protótipo que foi instalado no campus e num museu durante um evento de grande afluência. Os dados provenientes destes testes foram guardados numa base de dados de séries temporais e analisados usando uma ferramenta de visualização de dados. As conclusões obtidas nesta dissertação são que é possível criar um sensor capaz de detetar dispositivos na sua proximidade e detetar situações de sobrelotação/apinhamento. O protótipo contruído permite detectar padrões de mobilidade de multidões. A composição de tecnologias e a unificação de identidade são problemas que requerem investigação futura

Graph Neural Network for spatiotemporal data: methods and applications

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed

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