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Heberlein, Thomas A. 2017. Sweden
Milwaukee Journal Sentinel, March 5, 2017 Sweden is safe Since President Donald Trump called attention to violence in Sweden, we have had American journalists show up here. One, Tim Pool — funded in part by right-wing editor Paul Joseph Watson who tweeted “Any journalist claiming Sweden is safe; I will pay for travel costs & accommodation for you to stay in crime ridden migrant suburbs of Malmo” — visited Rosengård, Malmö’s toughest immigrant area. He was followed by Swedish reporters and found what was no surprise to me, an American living in Sweden: “If this is the worst Malmö can offer never come to Chicago.” Pool claimed Chicago had about 750 murders last year. All of Sweden, which has three times the population of Chicago, had 112 murders in 2015 (the last year for which statistics are available). Malmö, with a population of 330,000, had 12 murders in 2016. Madison, with a population of 243,000 had nine. So per capita, you are about as safe in Malmö as in Madison. Pool also said after a few hours in Rosengård: “For anyone worried about violence in Malmö and worried that he will be robbed or attacked in Rosengård is ‘löliga’ (ridiculous).” On TV news, he was shown walking in the community and reported it was boring and the only danger he felt was from the ice on the sidewalks. Sweden, I admit, is not a perfect society: It does have problems keeping ice off the sidewalks, and thousands are injured in falls every year. Tom Heberlein emeritus professor University of Wisconsin-Madison Stockholm, Sweden http://www.jsonline.com/story/opinion/readers/2017/03/04/letters-march-walkers-prison-priorities- wrong/98723360/ . -
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THE PORTRAYAL OF MARIJUANA ON VICE.COM DOCUMENTARIES The Graduate School of Economics and Social Sciences of İhsan Doğramacı Bilkent University by GÖKÇE ÖZSU In Partial Fulfillment of Requirements for the Degree of MASTER OF ARTS THE DEPARTMENT OF COMMUNICATION AND DESIGN İHSAN DOĞRAMACI BİLKENT UNIVERSITY ANKARA August 2017 Scanned by CamScanner ABSTRACT THE PORTRAYAL OF MARIJUANA ON VICE.COM DOCUMENTARIES Özsu, Gökçe MA, The Department of Communication and Design Supervisor: Prof. Dr. Bülent Çaplı August 2017 This thesis aims to examine the representational attitude of vice.com (or VICE) documentaries covering marijuana, in the context of normalization. In this respect, this thesis mainly descriptively analyses three VICE documentaries covering marijuana in the setting of recreation, medicine and industry. VICE is a United States based media outlet which uses hybrid form of journalism combining conventional form of media operations and new media techniques. Normalization is a sociological concept for describing the the scale of social acceptance as a norm which was disseminated from the iii margins of the society towards mainstream scale. To implement descriptive analysis on vice.com documentaries, normalization and drug representation in the United States media has been evaluated in the socio-historical setting, and examined. As a major finding, even though vice.com documentaries represent marijuana as normal, the normative references of normalization of marijuana is not clear. In this respect, in the conclusion, the determiners and normative background of normalization of marijuana are tried to be discussed. Keywords: Documentary, Marijuana, Normalization, Stigmatization, VICE iv ÖZET VICE.COM BELGESELLERİNDE MARİJUANANIN TASVİRİ Özsu, Gökçe Yüksek Lisans, İletişim ve Tasarım Bölümü Danışman: Prof. -
Setting the Record Straighter on Shadow Banning
Setting the Record Straighter on Shadow Banning Erwan Le Merrer, Benoˆıt Morgan, Gilles Tredan,´ Univ Rennes, Inria, CNRS, Irisa IRIT/ENSHEEIT LAAS/CNRS [email protected] [email protected] [email protected] Abstract—Shadow banning consists for an online social net- happens on the OSN side, collecting information about po- work in limiting the visibility of some of its users, without them tential issues is difficult. In this paper, we explore scientific being aware of it. Twitter declares that it does not use such approaches to shed light on Twitter’s alleged shadow banning a practice, sometimes arguing about the occurrence of “bugs” to justify restrictions on some users. This paper is the first to practice. Focusing on this OSN is crucial because of its central address the plausibility of shadow banning on a major online use as a public communication medium, and because potential platform, by adopting both a statistical and a graph topological shadow banning practices were recently commented. approach. Shadow banning and moderation techniques. shadow banning We first conduct an extensive data collection and analysis (SB or banning for short, also known as stealth banning [6]) campaign, gathering occurrences of visibility limitations on user profiles (we crawl more than 2:5 millions of them). In such is an online moderation technique used to ostracise undesired a black-box observation setup, we highlight the salient user user behaviors. In modern OSNs, shadow banning would refer profile features that may explain a banning practice (using to a wide range of techniques that artificially limit the visibility machine learning predictors). -
Big Data for Improved Health Outcomes — Second Edition — Arjun Panesar Machine Learning and AI for Healthcare Big Data for Improved Health Outcomes Second Edition
Machine Learning and AI for Healthcare Big Data for Improved Health Outcomes — Second Edition — Arjun Panesar Machine Learning and AI for Healthcare Big Data for Improved Health Outcomes Second Edition Arjun Panesar Machine Learning and AI for Healthcare Arjun Panesar Coventry, UK ISBN-13 (pbk): 978-1-4842-6536-9 ISBN-13 (electronic): 978-1-4842-6537-6 https://doi.org/10.1007/978-1-4842-6537-6 Copyright © 2021 by Arjun Panesar This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. -
Write-Ins Race/Name Totals - General Election 11/03/20 11/10/2020
Write-Ins Race/Name Totals - General Election 11/03/20 11/10/2020 President/Vice President Phillip M Chesion / Cobie J Chesion 1 1 U/S. Gubbard 1 Adebude Eastman 1 Al Gore 1 Alexandria Cortez 2 Allan Roger Mulally former CEO Ford 1 Allen Bouska 1 Andrew Cuomo 2 Andrew Cuomo / Andrew Cuomo 1 Andrew Cuomo, NY / Dr. Anthony Fauci, Washington D.C. 1 Andrew Yang 14 Andrew Yang Morgan Freeman 1 Andrew Yang / Joe Biden 1 Andrew Yang/Amy Klobuchar 1 Andrew Yang/Jeremy Cohen 1 Anthony Fauci 3 Anyone/Else 1 AOC/Princess Nokia 1 Ashlie Kashl Adam Mathey 1 Barack Obama/Michelle Obama 1 Ben Carson Mitt Romney 1 Ben Carson Sr. 1 Ben Sass 1 Ben Sasse 6 Ben Sasse senator-Nebraska Laurel Cruse 1 Ben Sasse/Blank 1 Ben Shapiro 1 Bernard Sanders 1 Bernie Sanders 22 Bernie Sanders / Alexandria Ocasio Cortez 1 Bernie Sanders / Elizabeth Warren 2 Bernie Sanders / Kamala Harris 1 Bernie Sanders Joe Biden 1 Bernie Sanders Kamala D. Harris 1 Bernie Sanders/ Kamala Harris 1 Bernie Sanders/Andrew Yang 1 Bernie Sanders/Kamala D. Harris 2 Bernie Sanders/Kamala Harris 2 Blain Botsford Nick Honken 1 Blank 7 Blank/Blank 1 Bobby Estelle Bones 1 Bran Carroll 1 Brandon A Laetare 1 Brian Carroll Amar Patel 1 Page 1 of 142 President/Vice President Brian Bockenstedt 1 Brian Carol/Amar Patel 1 Brian Carrol Amar Patel 1 Brian Carroll 2 Brian carroll Ammor Patel 1 Brian Carroll Amor Patel 2 Brian Carroll / Amar Patel 3 Brian Carroll/Ama Patel 1 Brian Carroll/Amar Patel 25 Brian Carroll/Joshua Perkins 1 Brian T Carroll 1 Brian T. -
Analyzing Twitter Users' Behavior Before and After Contact By
Analyzing Twitter Users’ Behavior Before and After Contact by Russia’s Internet Research Agency UPASANA DUTTA, RHETT HANSCOM, JASON SHUO ZHANG, RICHARD HAN, TAMARA 90 LEHMAN, QIN LV, SHIVAKANT MISHRA, University of Colorado Boulder, USA Social media platforms have been exploited to conduct election interference in recent years. In particular, the Russian-backed Internet Research Agency (IRA) has been identified as a key source of misinformation spread on Twitter prior to the 2016 U.S. presidential election. The goal of this research is to understand whether general Twitter users changed their behavior in the year following first contact from an IRA account. We compare the before and after behavior of contacted users to determine whether there were differences in their mean tweet count, the sentiment of their tweets, and the frequency and sentiment of tweets mentioning @realDonaldTrump or @HillaryClinton. Our results indicate that users overall exhibited statistically significant changes in behavior across most of these metrics, and that those users that engaged with the IRA generally showed greater changes in behavior. CCS Concepts: • Applied computing ! Law, social and behavioral sciences; • Human-centered com- puting ! Collaborative and social computing. Additional Key Words and Phrases: Internet Research Agency; IRA; Twitter; Trump; democracy ACM Reference Format: Upasana Dutta, Rhett Hanscom, Jason Shuo Zhang, Richard Han, Tamara Lehman, Qin Lv, Shivakant Mishra. 2021. Analyzing Twitter Users’ Behavior Before and After Contact by Russia’s Internet Research Agency. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 90 (April 2021), 24 pages. https://doi.org/10.1145/3449164 INTRODUCTION For many years, the growth of online social media has been seen as a tool used to bring people together across borders and time-zones. -
Generating Counter Narratives Against Online Hate Speech: Data and Strategies
Generating Counter Narratives against Online Hate Speech: Data and Strategies Serra Sinem Tekiroglu˘ 1, Yi-Ling Chung1,2, and Marco Guerini1 1Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy [email protected],[email protected],[email protected] 2University of Trento, Italy Abstract the conversations (Bielefeldt et al., 2011; Jurgens et al., 2019). In this line of action, some Non- Recently research has started focusing on avoiding undesired effects that come with Govermental Organizations (NGOs) train operators content moderation, such as censorship and to intervene in online hateful conversations by writ- overblocking, when dealing with hatred on- ing counter-narratives. A Counter-Narrative (CN) line. The core idea is to directly intervene in is a non-aggressive response that offers feedback the discussion with textual responses that are through fact-bound arguments and is considered as meant to counter the hate content and prevent the most effective approach to withstand hate mes- it from further spreading. Accordingly, au- sages (Benesch, 2014; Schieb and Preuss, 2016). tomation strategies, such as natural language generation, are beginning to be investigated. To be effective, a CN should follow guidelines simi- 1 Still, they suffer from the lack of sufficient lar to those in ‘Get the Trolls Out’ project , in order amount of quality data and tend to produce to avoid escalating the hatred in the discussion. generic/repetitive responses. Being aware of Still, manual intervention against hate speech the aforementioned limitations, we present a is not scalable. Therefore, data-driven NLG ap- study on how to collect responses to hate ef- proaches are beginning to be investigated to assist fectively, employing large scale unsupervised language models such as GPT-2 for the gen- NGO operators in writing CNs. -
Countering Terrorism Online with Artificial Intelligence an Overview for Law Enforcement and Counter-Terrorism Agencies in South Asia and South-East Asia
COUNTERING TERRORISM ONLINE WITH ARTIFICIAL INTELLIGENCE AN OVERVIEW FOR LAW ENFORCEMENT AND COUNTER-TERRORISM AGENCIES IN SOUTH ASIA AND SOUTH-EAST ASIA COUNTERING TERRORISM ONLINE WITH ARTIFICIAL INTELLIGENCE An Overview for Law Enforcement and Counter-Terrorism Agencies in South Asia and South-East Asia A Joint Report by UNICRI and UNCCT 3 Disclaimer The opinions, findings, conclusions and recommendations expressed herein do not necessarily reflect the views of the Unit- ed Nations, the Government of Japan or any other national, regional or global entities involved. Moreover, reference to any specific tool or application in this report should not be considered an endorsement by UNOCT-UNCCT, UNICRI or by the United Nations itself. The designation employed and material presented in this publication does not imply the expression of any opinion whatsoev- er on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries. Contents of this publication may be quoted or reproduced, provided that the source of information is acknowledged. The au- thors would like to receive a copy of the document in which this publication is used or quoted. Acknowledgements This report is the product of a joint research initiative on counter-terrorism in the age of artificial intelligence of the Cyber Security and New Technologies Unit of the United Nations Counter-Terrorism Centre (UNCCT) in the United Nations Office of Counter-Terrorism (UNOCT) and the United Nations Interregional Crime and Justice Research Institute (UNICRI) through its Centre for Artificial Intelligence and Robotics. -
Congressional Record—Senate S4658
S4658 CONGRESSIONAL RECORD — SENATE August 3, 2020 The PRESIDING OFFICER. Without companies censor political viewpoints employ disturbing and familiar im- objection, it is so ordered. that they find objectionable. That was agery. COMMUNICATIONS DECENCY ACT 2 years ago. It has only worsened in the According to the ESPN investiga- Mr. WICKER. Madam President, for 2 years since then. tion, one former league employee com- almost 25 years, the internet has grown These concerns come at a time when pared the atmosphere at the Xinjiang and thrived under the light-touch regu- tech companies wield unprecedented camp to ‘‘World War II Germany.’’ latory framework established by the power within our economy and our cul- An American coach, who worked at a Communications Decency Act. I hope ture at large, and no one can deny that. similar facility, described it as a we can continue that. I think some A bipartisan chorus of committee ‘‘sweat camp for athletes.’’ changes need to be made. members from the other body pointed Now, according to the investigation, Passed in 1996, the law that the Com- this out just last week. More and more almost immediately after the NBA munications Decency Act is a part of of our daily business is taking place launched this program back in 2016, helped create the internet. Section 230 online, and that trend is only accel- multiple coaches who were staffing the of that law gives broad liability protec- erating during the current pandemic. camps reported to high-ranking organi- tions to interactive computer services, As we near the 2020 election, Ameri- zation officials that they had witnessed such as Facebook, Twitter, and other cans have serious concerns about Chinese coaches beating and berating social media platforms. -
BY Galen Stocking, Patrick Van Kessel, Michael Barthel, Katerina Eva Matsa and Maya Khuzam
FOR RELEASE SEPTEMBER 28, 2020 BY Galen Stocking, Patrick van Kessel, Michael Barthel, Katerina Eva Matsa and Maya Khuzam FOR MEDIA OR OTHER INQUIRIES: Katerina Eva Matsa, Associate Director, Journalism Research Galen Stocking, Senior Computational Social Scientist Hannah Klein, Communications Manager Andrew Grant, Communications Associate 202.419.4372 www.pewresearch.org RECOMMENDED CITATION Pew Research Center, September, 2020, “Many Americans Get News on YouTube, Where News Organizations and Independent Producers Thrive Side by Side” 1 PEW RESEARCH CENTER About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, content analysis and other data-driven social science research. It studies U.S. politics and policy; journalism and media; internet, science and technology; religion and public life; Hispanic trends; global attitudes and trends; and U.S. social and demographic trends. All of the Center’s reports are available at www.pewresearch.org. Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. © Pew Research Center 2020 www.pewresearch.org 2 PEW RESEARCH CENTER Terminology [FORMAT AS DROPDOWN BOX W/LINK] This study explores the landscape of news on YouTube through a survey of YouTube news consumers alongside an analysis of the most popular YouTube channels that produce news and the videos published by a subset of these channels. Here are some definitions of key terms used throughout this report: ▪ YouTube news consumers: Those who said they get news from YouTube in a Pew Research Center survey of U.S. -
Conservative Media's Coverage of Coronavirus On
University of South Carolina Scholar Commons Theses and Dissertations Fall 2020 Conservative Media’s Coverage of Coronavirus on YouTube: A Qualitative Analysis of Media Effects on Consumers Michael J. Layer Follow this and additional works at: https://scholarcommons.sc.edu/etd Part of the Mass Communication Commons Recommended Citation Layer, M. J.(2020). Conservative Media’s Coverage of Coronavirus on YouTube: A Qualitative Analysis of Media Effects on Consumers. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/ etd/6133 This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. CONSERVATIVE MEDIA’S COVERAGE OF CORONAVIRUS ON YOUTUBE: A QUALITATIVE ANALYSIS OF MEDIA EFFECTS ON CONSUMERS by Michael J. Layer Bachelor of Arts Goucher College, 2017 Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Arts in Mass Communications College of Information and Communications University of South Carolina 2020 Accepted by: Linwan Wu, Director of Thesis Leigh Moscowitz, Reader Anli Xiao, Reader Cheryl L. Addy, Vice Provost and Dean of the Graduate School © Copyright by Michael J. Layer, 2020 All Rights Reserved. ii DEDICATION To the wonderful creators on the internet whose parasocial relationships have taught me so much about the world and myself - namely Olly Thorn, Philosophy Tube; Carlos Maza; Natalie Wynn, Contrapoints; Lindsey Ellis; Bryan David Gilbert; Katy Stoll and Cody Johnson, Some More News; and Robert Evans, Behind the Bastards - thank you. To Dr. Linwan Wu - I am profoundly grateful for your understanding, guidance, and patience. -
Tro Og Love-Erklæring
Det Humanistiske Fakultet Speciale Forside til specialet Afleveringsfrist (sæt x) Januar: [Årstal] Juni: 2019 X Andet: [Dato] Vejleder: Casper Sylvest Studie: Kandidat Historie Titel, dansk: Ytringsfrihed I den moderne informationsalder: Hate Speech og Politisk Korrekthed Titel, engelsk: Freedom of Expression in the Modern Age of Information: Hate Speech and Political Correctness Min./max. antal typeenheder: 144.000 – 192.000 Din besvarelses antal typeenheder1: 190.201 (60 – 80 normalsider) (1 normalside = 2400 typeenheder) Du skal være opmærksom på, såfremt din besvarelse ikke lever op til det angivne (min./max) antal typeenheder (normalsider) vil din opgave blive afvist, og du har brugt et forsøg. Tro og love-erklæring Det erklæres herved på tro og love, at undertegnede egenhændigt og selvstændigt har udformet denne eksamensopgave. Alle citater i teksten er markeret som sådanne, og eksamensopgaven, eller væsentlige dele af den, har ikke tidligere været fremlagt i anden bedømmelsessammenhæng. Læs mere her: http://www.sdu.dk/Information_til/Studerende_ved_SDU/Eksamen.aspx Afleveret af: Henrik Herlev Sørensen Fornavn: Henrik Efternavn: Sørensen Fødselsdato: 02-02-1994 1 Tælles fra første tegn i indledningen til sidste tegn i konklusionen, inkl. fodnoter. Tabeller tælles med med deres antal typeenheder. Følgende tælles ikke med: resumé, indholdsfortegnelse, litteraturliste og bilag. Se i øvrigt eksamensbestemmelserne for disciplinen i studieordningen. Side 1 af 91 Freedom of Expression in the Modern Age of Information: - Hate Speech and Political Correctness “If liberty means anything at all, it means the right to tell people what they do not want to hear.” - George Orwell Written by: Henrik Herlev Sørensen Exam number: 408968 Master thesis June 2019 University of Southern Denmark Counselor: Casper Sylvest Side 2 af 91 Abstract I de foregående år er ytringsfrihed igen blevet en vigtig samfundsdebat, som nu ikke længere blot beror sig på friheden til hverdagslige politiske samtaler men også på internettet.