Understanding Vulnerability to Online Misinformation March 2021
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Prof Helen MARGETTS Director and Professor of Society and the Internet, Oxford Internet Institute, University of Oxford
Prof Helen MARGETTS Director and Professor of Society and the Internet, Oxford Internet Institute, University of Oxford Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics, investigating political behaviour, digital government and government-citizen interactions in the age of the internet, social media and big data. She has published over a hundred books, articles and major research reports in this area, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, scott Hale and Taha Yasseri, 2015); Paradoxes of Modernization (with Perri 6 and Christopher Hood, 2010); Digital Era Governance (with Patrick Dunleavy, 2006); and The Tools of Government in the Digital Age (with Christopher Hood, 2007). In 2003 she and Patrick Dunleavy won the 'Political Scientists Making a Difference' award from the UK Political Studies Association, in part for a series of policy reports on Government on the Internet for the UK National Audit Office (1999, 2002 and 2007), and she continues working to maximise the policy impact of her research. She sits on the Digital Advisory Board of the UK Government Digital Service and the World Economic Forum Global Agenda Council on the Future of Government. She is editor-in-chief of the journal Policy and Internet. She is a fellow of the Academy of Social Sciences. From 2011- 2014 she held the ESRC professorial fellowship 'The Internet, Political Science and Public policy: Re-examining Collective Action, Governance and Citizen-Governance Interactions in the Digital Era'. Professor Margetts joined the OII in 2004 from University College London where she was a Professor in Political Science and Director of the School of Public Policy. -
Annual Report 2018–19
Annual report 2018–19 2018 –19 Annual Report 2018–19 Part 1 Our year 3 Part 2 Trustees’ and strategic report 74 Part 3 Financial statements 84 Our year 1.1 Chair’s foreword 1.2 Institute Director and Chief Executive’s foreword 1 1.3 Outputs, impact and equalities 1.4 Turing trends and statistics 1.5 Research mapping: Linking research, people and activities 3 Creating real world impact We have had yet another year of rapid growth Since 2016, researchers at the Institute have produced and remarkable progress. over 200 publications in leading journals and demonstrated early impacts with great potential. Some of the impacts The Alan Turing Institute is a multidisciplinary institute are captured in this annual report and we are excited to formed through partnerships with some of the UK’s see the translation of our data science and AI research leading universities. This innovative approach gives us into a real-world context. access to expertise and skills in data science and AI that is unparalleled outside of a handful of large tech platforms. We are witnessing a massive growth of data science Our substantial convening power enables us to work and AI. We are also seeing important challenges and across the economy: with large corporations, the public breakthroughs in many areas including safe and ethical sector including government departments, charitable AI, quantum computing, urban transport, defence, foundations and small businesses. These collaborations manufacturing, health and financial services. Across cut across disciplines and break through institutional the Institute we are leading the public conversation in boundaries. -
Can You Spot COVID-19 Misinformation?
Can You Spot COVID-19 Misinformation? We’re all seeking the latest information on the COVID-19 pandemic and what might be coming next. But how can we spot and avoid the false information that is also circulating, especially on social media? Here are some things to look out for. Have you received a message from your friend Did you see a statement being shared on social that says her aunt/teacher/colleague knows networks that looks like it has come from the someone who works in the ER and has the government or a health authority? following information? If so: Be cautious. If so: Be a detective. Lots of copy and paste It may be ‘imposter rumors are spread content’, which is this way. when it’s not. Action: try pasting the Action: social networks. If you website of the organization see lots of examples, it quoted and check whether has likely travelled a long the information on their way before it got to you. site is a match. Maybe someone sent you a list of top tips to Did you see that very dramatic video on social media avoid the virus, like eating certain foods or using showing the latest updates relating to COVID-19? home remedies? If so: Be skeptical. If so: Look closer. There’s no food or Sometimes videos and supplement that can stop pictures being shared on you getting this virus, social media aren’t quite and there’s currently no what they seem. treatment - you can only try to manage the symptoms. Action: try reverse image searching pictures ? Action: consult the latest to see if they have been guidelines from the World used before. -
STEM Education Centre E-Bulletin: March 2014
STEM Education Centre E-bulletin: March 2014 Welcome to the March e-bulletin from the University of Birmingham’s STEM Education Centre. It is designed to provide you with information about STEM-related events, resources, news and updates that may be of interest to you and your colleagues. STEM Policy and Practice News Budget 2014: Alan Turing Institute to lead big data research A £42m Alan Turing Institute is to be founded to ensure that Britain leads the way in big data and algorithm research, George Osborne has announced. Drawing on the name of the British mathematician who led code-breaking work at Bletchley Park during the World War II, the institute is intended to help British companies by bringing together expertise and experience in tackling problems requiring huge computational power. Turing’s work led to the cracking of the German "Enigma" codes, which used highly compl ex encryption, is believed to have saved hundreds or even thousands of lives. He later formed a number of theories that underpin modern computing, and formalised the idea of algorithms – sequences of instructions – for a computer. This announcement marks further official rehabilitation of a scientist who many see as having been treated badly by the British government after his work during the war. Turing, who was gay, was convicted of indecency in March 1952, and lost his security clearance with GCHQ, the successor to Bletchley Park. He killed himself in June 1954. Only after a series of public campaigns was he given an official pardon by the UK government in December 2013. Public Attitudes to Science (PAS) 2014 Public Attitudes to Science (PAS) 2014 is the fifth in a series of studies looking at attitudes to science, scientists and science policy among the UK public. -
Download Book
Challenges to Democracy Political Studies Association Yearbook Series Titles include: Keith Dowding, James Hughes and Helen Margetts (editors) CHALLENGES TO DEMOCRACY Ideas, Involvement and Institutions Chris Pierson and Simon Tormey (editors) POLITICS AT THE EDGE The PSA Yearbook 1999 Political Studies Association Yearbook Series Standing Order ISBN 978-0-333-91373-4 (outside North America only) You can receive future titles in this series as they are published by placing a standing order. Please contact your bookseller or, in case of difficulty, write to us at the address below with your name and address, the title of the series and the ISBN quoted above. Customer Services Department, Macmillan Distribution Ltd, Houndmills, Basingstoke, Hampshire RG21 6XS, England Challenges to Democracy Ideas, Involvement and Institutions The PSA Yearbook 2000 Edited by Keith Dowding Professor of Political Science London School of Economics James Hughes Senior Lecturer in Comparative Economics London School of Economics and Helen Margetts Professor of Political Science Director, School of Public Policy University College London in association with Political Studies Association © Political Studies Association 2001 Softcover reprint of the hardcover 1st edition 2001 978-0-333-78982-7 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1P 0LP. Any person who does any unauthorised act in relation to this publication may be liable to criminal prosecution and civil claims for damages. -
Automating Data Science: Prospects and Challenges
Final m/s version of paper accepted (April 2021) for publication in Communications of the ACM. Please cite the journal version when it is published; this will contain the final version of the figures. Automating Data Science: Prospects and Challenges Tijl De Bie Luc De Raedt IDLab – Dept. of Electronics and Information Systems Dept. of Computer Science AASS Ghent University KU Leuven Örebro University Belgium Belgium Sweden José Hernández-Orallo Holger H. Hoos vrAIn LIACS Universitat Politècnica de València Universiteit Leiden Spain The Netherlands Padhraic Smyth Christopher K. I. Williams Department of Computer Science School of Informatics Alan Turing Institute University of California, Irvine University of Edinburgh London USA United Kingdom United Kingdom May 13, 2021 Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights arXiv:2105.05699v1 [cs.DB] 12 May 2021 • Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. • Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (Au- toML) are gaining traction. • Other aspects are harder to automate, not only because of technological chal- lenges, but because open-ended and context-dependent tasks require human in- teraction. 1 Introduction Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization [10]. -
1 Validation of a Novel Climate Change Denial Measure Using Item
1 Validation of a Novel Climate Change Denial Measure Using Item Response Theory Mr. George Lorama*,, Dr. Mathew Linga, Mr. Andrew Heada, Dr. Edward J.R. Clarkeb a Deakin University, Geelong, Australia, Misinformation Lab, School of Psychology b Federation University, Mount Helen, Australia, School of Health and Life Sciences * Corresponding Author, Misinformation Lab, Deakin University, Locked Bag 20001, Geelong VIC 3220, Australia. Email address: [email protected] Declarations of Interest: All authors declare that no conflict of interest exists. Acknowledgements: This work was supported by Deakin University (4th Year Student Research Funding; 4th Year Publication Award). 2 Abstract Climate change denial persists despite overwhelming scientific consensus on the issue. However, the rates of denial reported in the literature are inconsistent, potentially as a function of ad hoc measurement of denial. This further impacts on interpretability and integration of research. This study aims to create a standardised measure of climate change denial using Item Response Theory (IRT). The measure was created by pooling items from existing denial measures, and was administered to a U.S. sample recruited using Amazon MTurk (N = 206). Participants responded to the prototype measure as well as being measured on a number of constructs that have been shown to correlate with climate change denial (authoritarianism, social dominance orientation, mistrust in scientists, and conspiracist beliefs). Item characteristics were calculated using a 2-parameter IRT model. After screening out poorly discriminating and redundant items, the scale contained eight items. Discrimination indices were high, ranging from 2.254 to 30.839, but item difficulties ranged from 0.437 to 1.167, capturing a relatively narrow band of climate change denial. -
Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 1 Lucie C
Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 1 Lucie C. Burgess, David Crotty, David de Roure, Jeremy Gibbons, Carole Goble, Paolo Missier, Richard Mortier, 21 July 2016 Thomas E. Nichols, Richard O’Beirne Dickson Poon China Centre, St. Hugh’s College, University of Oxford, 6th and 7th April 2016 ALAN TURING INSTITUTE SYMPOSIUM ON REPRODUCIBILITY FOR DATA-INTENSIVE RESEARCH – FINAL REPORT AUTHORS Lucie C. Burgess1, David Crotty2, David de Roure1, Jeremy Gibbons1, Carole Goble3, Paolo Missier4, Richard Mortier5, Thomas E. Nichols6, Richard O’Beirne2 1. University of Oxford 2. Oxford University Press 3. University of Manchester 4. Newcastle University 5. University of Cambridge 6. University of Warwick TABLE OF CONTENTS 1. Symposium overview – p.2 1.1 Objectives 1.2 Citing this report 1.3 Organising committee 1.4 Format 1.5 Delegates 1.6 Funding 2. Executive summary – p.4 2.1 What is reproducibility and why is it important? 2.2 Summary of recommendations 3. Data provenance to support reproducibility – p.7 4. Computational models and simulations – p.10 5. Reproducibility for real-time big data – p.13 6. Publication of data-intensive research – p.16 7. Novel architectures and infrastructure to support reproducibility – p.19 8. References - p.22 Annex A – Symposium programme and biographies for the organising committee and speakers Annex B – Delegate list ACKNOWLEDGEMENTS The Organising Committee would like to thanks the speakers, session chairs and delegates to the symposium, and in particular all those who wrote notes during the sessions and provided feedback on the report. This report is available online at: https://osf.io/bcef5/ (report and supplementary materials) https://dx.doi.org/10.6084/m9.figshare.3487382 (report only) Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research – FINAL REPORT Page | 2 Lucie C. -
Handbook of Research on Deception, Fake News, and Misinformation Online
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Strathclyde Institutional Repository Handbook of Research on Deception, Fake News, and Misinformation Online Innocent E. Chiluwa Covenant University, Nigeria Sergei A. Samoilenko George Mason University, USA A volume in the Advances in Media, Entertainment, and the Arts (AMEA) Book Series Published in the United States of America by IGI Global Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2019 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Chiluwa, Innocent, editor. | Samoilenko, Sergei A., 1976- editor. Title: Handbook of research on deception, fake news, and misinformation online / Innocent E. Chiluwa and Sergei A. Samoilenko, editors. Description: Hershey, PA : Information Science Reference, 2019. | Includes bibliographical references and index. Identifiers: LCCN 2018055436| ISBN 9781522585350 (hardcover) | ISBN 9781522585374 (ebook) | ISBN 9781522585367 (softcover) Subjects: LCSH: Fake news. | Deception. | Misinformation. | Social media. Classification: LCC PN4784.F27 H365 2019 | DDC 302.23/1--dc23 LC record available at https://lccn.loc.gov/2018055436 This book is published in the IGI Global book series Advances in Media, Entertainment, and the Arts (AMEA) (ISSN: 2475-6814; eISSN: 2475-6830) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. -
CV Adilet Otemissov
CV Adilet Otemissov RESEARCH INTERESTS Global Optimization; Stochastic Methods for Optimisation; Random Matrices EDUCATION DPhil (PhD) in Mathematics 2016{2021 University of Oxford The Alan Turing Institute, the UK's national institute for data science and AI - Thesis title: Dimensionality reduction techniques for global optimization (supervised by Dr Coralia Cartis) MSc in Applied Mathematics with Industrial Modelling 2015{2016 University of Manchester - Thesis title: Matrix Completion and Euclidean Distance Matrices (supervised by Dr Martin Lotz) BSc in Mathematics 2011{2015 Nazarbayev University AWARDS & ACHIEVEMENTS - SEDA Professional Development Framework Supporting Learning award for successfully completing Developing Learning and Teaching course; more details can be found on https://www.seda.ac.uk/supporting-learning, 2020 - The Alan Turing Doctoral Studentship: highly competitive full PhD funding, providing also a vibrant interdisciplinary research environment at the Institute in London, as well as extensive training in the state-of-the-art data science subjects from world's leading researchers, 2016{2020 - NAG MSc student prize in Applied Mathematics with Industrial Modelling for achieving the highest overall mark in the numerical analysis course units in my cohort at the University of Manchester, 2016 PUBLICATIONS - C. Cartis, E. Massart, and A. Otemissov. Applications of conic integral geometry in global optimization. 2021. Research completed, in preparation - C. Cartis, E. Massart, and A. Otemissov. Constrained global optimization of functions with low effective dimensionality using multiple random embeddings. ArXiv e-prints, page arXiv:2009.10446, 2020. Manuscript (41 pages) submitted for publication in Mathematical Programming. Got back from the first round of reviewing - C. Cartis and A. Otemissov. A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality. -
Infodemic and Misinformation in the Fight Against COVID-19
Understanding the infodemic and misinformation in the fight against COVID-19 |DIGITAL TRANSFORMATION TOOLKIT KNOWLEDGE TOOLS 9 COVID-19 Factsheet Digital Health Understanding the Infodemic and Misinformation in the fight against COVID-19 IMPORTANT NOTE: Stay informed with timely information on the Coronavirus Disease (COVID-19), available on the PAHO and WHO websites and through your national and local public health authorities What is the Infodemic? As stated by the WHO, the COVID-19 outbreak and response has been accompanied by a massive infodemic: an overabundance of information – some accurate and some not – that makes it hard for people to find trustworthy sources and Access to the right reliable guidance when they need it. Infodemic refers to a large increase in the volume of information associated with a information, at the specific topic and whose growth can occur exponentially in a right time, in the right short period of time due to a specific incident, such as the format IS CRITICAL! current pandemic. In this situation, misinformation and rumors appear on the scene, along with manipulation of information with doubtful intent. In the information age, this phenomenon is amplified through social networks, spreading farther and faster like a virus.1 What is Misinformation? Misinformation is false or inaccurate information deliberately intended to deceive. In the context of the current pandemic, it can greatly affect all aspects of life, especifically people’s mental health, since searching for 361,000,000 videos were uploaded on COVID-19 updates on the Internet has YouTube in the last 30 days under the “COVID-19” jumped 50% – 70% across all and “COVID 19” classification, and about 19,200 generations. -
Enabling a World-Leading Regional Digital Economy Through Data Driven Innovation
©Jonathan Clark Fine Art, Representatives of the Artist's Estate Enabling a World-Leading Regional Digital Economy through Data Driven Innovation Edinburgh & South East Scotland City Region A Science and Innovation Audit Report sponsored by the Department for Business, Energy & Industrial Strategy MAIN REPORT Edinburgh and South East Scotland City Region Consortium Foreword In Autumn 2015 the UK Government announced regional Science and Innovation Audits (SIAs) to catalyse a new approach to regional economic development. SIAs enable local Consortia to focus on analysing regional strengths and identify mechanisms to realise their potential. In the Edinburgh and South East Scotland City Region, a Consortium was formed to focus on our rapidly growing strength in Data-Driven Innovation (DDI). This report presents the results which includes broad-ranging analysis of the City Region’s DDI capabilities, the challenges and the substantial opportunities for future economic growth. Data-led disruption will be at the heart of future growth in the Digital Economy, and will enable transformational change across the broader economy. However, the exact scope, scale and timing of these impacts remains unclear1. The questions we face are simple yet far-reaching – given this uncertainty, is now the right time to commit fully to the DDI opportunity? If so, what are the investments we should make to optimise regional economic growth? This SIA provides evidence that we must take concerted action now to best position the City Region to benefit from the disruptive effects of DDI. If investment is deferred, we run the risk of losing both competitiveness and output to other leading digital clusters that have the confidence to invest.