Automatic Detection of Influential Actors in Disinformation Networks
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Arxiv:1805.10105V1 [Cs.SI] 25 May 2018
Effects of Social Bots in the Iran-Debate on Twitter Andree Thieltges, Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich Bavarian School of Public Policy Technical University of Munich Abstract Ahmadinejad caused nationwide unrests and protests. As these protests grew, the Iranian government shut down for- 2018 started with massive protests in Iran, bringing back the eign news coverage and restricted the use of cell phones, impressions of the so called “Arab Spring” and it’s revolution- text-messaging and internet access. Nevertheless, Twitter ary impact for the Maghreb states, Syria and Egypt. Many reports and scientific examinations considered online social and Facebook “became vital tools to relay news and infor- mation on anti-government protest to the people inside and networks (OSN’s) such as Twitter or Facebook to play a criti- 1 cal role in the opinion making of people behind those protests. outside Iran” . While Ayatollah Khameini characterized the Beside that, there is also evidence for directed manipulation influence of Twitter as “deviant” and inappropriate on Ira- of opinion with the help of social bots and fake accounts. nian domestic affairs2, most of the foreign news coverage So, it is obvious to ask, if there is an attempt to manipulate hailed Twitter to be “a new and influential medium for social the opinion-making process related to the Iranian protest in movements and international politics” (Burns and Eltham OSN by employing social bots, and how such manipulations 2009). Beside that, there was already a critical view upon will affect the discourse as a whole. Based on a sample of the influence of OSN’s as “tools” to express political opin- ca. -
Political Astroturfing Across the World
Political astroturfing across the world Franziska B. Keller∗ David Schochy Sebastian Stierz JungHwan Yang§ Paper prepared for the Harvard Disinformation Workshop Update 1 Introduction At the very latest since the Russian Internet Research Agency’s (IRA) intervention in the U.S. presidential election, scholars and the broader public have become wary of coordi- nated disinformation campaigns. These hidden activities aim to deteriorate the public’s trust in electoral institutions or the government’s legitimacy, and can exacerbate political polarization. But unfortunately, academic and public debates on the topic are haunted by conceptual ambiguities, and rely on few memorable examples, epitomized by the often cited “social bots” who are accused of having tried to influence public opinion in various contemporary political events. In this paper, we examine “political astroturfing,” a particular form of centrally co- ordinated disinformation campaigns in which participants pretend to be ordinary citizens acting independently (Kovic, Rauchfleisch, Sele, & Caspar, 2018). While the accounts as- sociated with a disinformation campaign may not necessarily spread incorrect information, they deceive the audience about the identity and motivation of the person manning the ac- count. And even though social bots have been in the focus of most academic research (Fer- rara, Varol, Davis, Menczer, & Flammini, 2016; Stella, Ferrara, & De Domenico, 2018), seemingly automated accounts make up only a small part – if any – of most astroturf- ing campaigns. For instigators of astroturfing, relying exclusively on social bots is not a promising strategy, as humans are good at detecting low-quality information (Pennycook & Rand, 2019). On the other hand, many bots detected by state-of-the-art social bot de- tection methods are not part of an astroturfing campaign, but unrelated non-political spam ∗Hong Kong University of Science and Technology yThe University of Manchester zGESIS – Leibniz Institute for the Social Sciences §University of Illinois at Urbana-Champaign 1 bots. -
Understanding Users' Perspectives of News Bots in the Age of Social Media
sustainability Article Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media Hyehyun Hong 1 and Hyun Jee Oh 2,* 1 Department of Advertising and Public Relations, Chung-Ang University, Seoul 06974, Korea; [email protected] 2 Department of Communication Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong SAR, China * Correspondence: [email protected] Received: 15 July 2020; Accepted: 10 August 2020; Published: 12 August 2020 Abstract: The move of news audiences to social media has presented a major challenge for news organizations. How to adapt and adjust to this social media environment is an important issue for sustainable news business. News bots are one of the key technologies offered in the current media environment and are widely applied in news production, dissemination, and interaction with audiences. While benefits and concerns coexist about the application of bots in news organizations, the current study aimed to examine how social media users perceive news bots, the factors that affect their acceptance of bots in news organizations, and how this is related to their evaluation of social media news in general. An analysis of the US national survey dataset showed that self-efficacy (confidence in identifying content from a bot) was a successful predictor of news bot acceptance, which in turn resulted in a positive evaluation of social media news in general. In addition, an individual’s perceived prevalence of social media news from bots had an indirect effect on acceptance by increasing self-efficacy. The results are discussed with the aim of providing a better understanding of news audiences in the social media environment, and practical implications for the sustainable news business are suggested. -
Wikileaks Vault 7 Has Created an Epic Liability Crisis for Corporate Directors
4/14/2017 Americans for Innovation: WIKILEAKS VAULT 7 HAS CREATED AN EPIC LIABILITY CRISIS FOR CORPORATE DIRECTORS 1 More Next Blog» Create Blog Sign In SEARCH by topic, keyword or phrase. Type in Custom Search box e.g. "IBM Eclipse Foundation" or "racketeering" Custom Search T u e s d a y , A p r i l 4 , 2 0 1 7 DEEP STATE SHADOW GOVERNMENT POSTER WIKILEAKS VAULT 7 HAS CREATED AN EPIC Harvard | Yale | Stanford Sycophants LIABILITY CRISIS FOR CORPORATE DIRECTORS Updated Mar. 14, 2017. CLICK HERE TO SEE TIMELINE VAULT 7 PROVES THAT CORPORATE DIRECTORS CANNOT OF THE HIJACKING OF THE PROTECT THEIR ASSETS FROM THE ROGUE C.I.A. & NAME INTERNET AND DATABASE BRAND TECHNOLOGY COLLUDERS AS REQUIRED BY THE PAY-t o-PLAY NEW WORLD ORDER BUSINESS JUDGMENT RULE This timeline shows how insiders sell access & manipulate politicians, police, intelligence, CONTRIBUTING WRITERS | OPINION | AMERICANS FOR INNOVATION | APR. 04, 2017, UPDATED APR. 14, 2017, judges and media to keep their secrets MAY YOUR GREAT AND HOLY FRIDAY BE A BLESSED ONE | PDF Clintons, Obamas, Summers were paid in cash for outlandish speaking fees and Foundation donations. Contributor: Michael T. McKibben, Chairman & Founder, Leader Technologies, Inc. the real Sycophant judges, poli icians, academics, bureaucrats inventor of social networking and media were fed tips to mutual funds tied to insider stocks like Facebook. Risk of public exposure, blackmail, pedophilia, “snuff par ies” (ritual child sexual abuse and murder) and Satanism have ensured silence among pay-to-play beneficiaries. The U.S. Patent Office is heir toy box. -
USA -V- Julian Assange Judgment
JUDICIARY OF ENGLAND AND WALES District Judge (Magistrates’ Court) Vanessa Baraitser In the Westminster Magistrates’ Court Between: THE GOVERNMENT OF THE UNITED STATES OF AMERICA Requesting State -v- JULIAN PAUL ASSANGE Requested Person INDEX Page A. Introduction 2 a. The Request 2 b. Procedural History (US) 3 c. Procedural History (UK) 4 B. The Conduct 5 a. Second Superseding Indictment 5 b. Alleged Conduct 9 c. The Evidence 15 C. Issues Raised 15 D. The US-UK Treaty 16 E. Initial Stages of the Extradition Hearing 25 a. Section 78(2) 25 b. Section 78(4) 26 I. Section 78(4)(a) 26 II. Section 78(4)(b) 26 i. Section 137(3)(a): The Conduct 27 ii. Section 137(3)(b): Dual Criminality 27 1 The first strand (count 2) 33 The second strand (counts 3-14,1,18) and Article 10 34 The third strand (counts 15-17, 1) and Article 10 43 The right to truth/ Necessity 50 iii. Section 137(3)(c): maximum sentence requirement 53 F. Bars to Extradition 53 a. Section 81 (Extraneous Considerations) 53 I. Section 81(a) 55 II. Section 81(b) 69 b. Section 82 (Passage of Time) 71 G. Human Rights 76 a. Article 6 84 b. Article 7 82 c. Article 10 88 H. Health – Section 91 92 a. Prison Conditions 93 I. Pre-Trial 93 II. Post-Trial 98 b. Psychiatric Evidence 101 I. The defence medical evidence 101 II. The US medical evidence 105 III. Findings on the medical evidence 108 c. The Turner Criteria 111 I. -
Supplementary Materials For
www.sciencemag.org/content/359/6380/1094/suppl/DC1 Supplementary Materials for The science of fake news David M. J. Lazer,*† Matthew A. Baum,* Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, Jonathan L. Zittrain *These authors contributed equally to this work. †Corresponding author. Email: [email protected] Published 9 March 2018, Science 359, 1094 (2018) DOI: 10.1126/science.aao2998 This PDF file includes: List of Author Affiliations Supporting Materials References List of Author Affiliations David M. J. Lazer,1,2*† Matthew A. Baum,3* Yochai Benkler,4,5 Adam J. Berinsky,6 Kelly M. Greenhill,7,3 Filippo Menczer,8 Miriam J. Metzger,9 Brendan Nyhan,10 Gordon Pennycook,11 David Rothschild,12 Michael Schudson,13 Steven A. Sloman,14 Cass R. Sunstein,4 Emily A. Thorson,15 Duncan J. Watts,12 Jonathan L. Zittrain4,5 1Network Science Institute, Northeastern University, Boston, MA 02115, USA. 2Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA 3John F. Kennedy School of Government, Harvard University, Cambridge, MA 02138, USA. 4Harvard Law School, Harvard University, Cambridge, MA 02138, USA. 5 Berkman Klein Center for Internet and Society, Cambridge, MA 02138, USA. 6Department of Political Science, Massachussets Institute of Technology, Cambridge, MA 02139, USA. 7Department of Political Science, Tufts University, Medford, MA 02155, USA. 8School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, USA. 9Department of Communication, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. -
Frame Viral Tweets.Pdf
BIG IDEAS! Challenging Public Relations Research and Practice Viral Tweets, Fake News and Social Bots in Post-Factual Politics: The Case of the French Presidential Elections 2017 Alex Frame, Gilles Brachotte, Eric Leclercq, Marinette Savonnet [email protected] 20th Euprera Congress, Aarhus, 27-29th September 2018 Post-Factual Politics in the Age of Social Media 1. Algorithms based on popularity rather than veracity, linked to a business model based on views and likes, where novelty and sensationalism are of the essence; 2. Social trends of “whistle-blowing” linked to conspiracy theories and a pejorative image of corporate or institutional communication, which cast doubt on the neutrality of traditionally so-called expert figures, such as independent bodies or academics; 3. Algorithm-fuelled social dynamics on social networking sites which structure publics by similarity, leading to a fragmented digital public sphere where like-minded individuals congregate digitally, providing an “echo-chamber” effect susceptible to encourage even the most extreme political views and the “truths” upon which they are based. Fake News at the Presidential Elections Fake News at the Presidential Elections Fake News at the Presidential Elections Political Social Bots on Twitter Increasingly widespread around the world, over at least the last decade. Kremlin bot army (Lawrence Alexander on GlobalVoices.org; Stukal et al., 2017). DFRLab (Atlantic Council) Social Bots A “social bot” is: “a computer algorithm that automatically produces content and -
Empirical Comparative Analysis of Bot Evidence in Social Networks
Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks Ross Schuchard1(&) , Andrew Crooks1,2 , Anthony Stefanidis2,3 , and Arie Croitoru2 1 Computational Social Science Program, Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA [email protected] 2 Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA 3 Criminal Investigations and Network Analysis Center, George Mason University, Fairfax, VA 22030, USA Abstract. The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the con- versational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes. -
Disinformation, and Influence Campaigns on Twitter 'Fake News'
Disinformation, ‘Fake News’ and Influence Campaigns on Twitter OCTOBER 2018 Matthew Hindman Vlad Barash George Washington University Graphika Contents Executive Summary . 3 Introduction . 7 A Problem Both Old and New . 9 Defining Fake News Outlets . 13 Bots, Trolls and ‘Cyborgs’ on Twitter . 16 Map Methodology . 19 Election Data and Maps . 22 Election Core Map Election Periphery Map Postelection Map Fake Accounts From Russia’s Most Prominent Troll Farm . 33 Disinformation Campaigns on Twitter: Chronotopes . 34 #NoDAPL #WikiLeaks #SpiritCooking #SyriaHoax #SethRich Conclusion . 43 Bibliography . 45 Notes . 55 2 EXECUTIVE SUMMARY This study is one of the largest analyses to date on how fake news spread on Twitter both during and after the 2016 election campaign. Using tools and mapping methods from Graphika, a social media intelligence firm, we study more than 10 million tweets from 700,000 Twitter accounts that linked to more than 600 fake and conspiracy news outlets. Crucially, we study fake and con- spiracy news both before and after the election, allowing us to measure how the fake news ecosystem has evolved since November 2016. Much fake news and disinformation is still being spread on Twitter. Consistent with other research, we find more than 6.6 million tweets linking to fake and conspiracy news publishers in the month before the 2016 election. Yet disinformation continues to be a substantial problem postelection, with 4.0 million tweets linking to fake and conspiracy news publishers found in a 30-day period from mid-March to mid-April 2017. Contrary to claims that fake news is a game of “whack-a-mole,” more than 80 percent of the disinformation accounts in our election maps are still active as this report goes to press. -
Macron Leaks” Operation: a Post-Mortem
Atlantic Council The “Macron Leaks” Operation: A Post-Mortem Jean-Baptiste Jeangène Vilmer The “Macron Leaks” Operation: A Post-Mortem Jean-Baptiste Jeangène Vilmer ISBN-13: 978-1-61977-588-6 This report is written and published in accordance with the Atlantic Council Policy on Intellectual Indepen- dence. The author is solely responsible for its analysis and recommendations. The Atlantic Council and its donors do not determine, nor do they necessarily endorse or advocate for, any of this report’s conclusions. June 2019 Contents Acknowledgments iv Abstract v Introduction 1 I- WHAT HAPPENED 4 1. The Disinformation Campaign 4 a) By the Kremlin media 4 b) By the American alt-right 6 2. The Aperitif: #MacronGate 9 3. The Hack 10 4. The Leak 11 5. In Summary, a Classic “Hack and Leak” Information Operation 14 6. Epilogue: One and Two Years Later 15 II- WHO DID IT? 17 1. The Disinformation Campaign 17 2. The Hack 18 3. The Leak 21 4. Conclusion: a combination of Russian intelligence and American alt-right 23 III- WHY DID IT FAIL AND WHAT LESSONS CAN BE LEARNED? 26 1. Structural Reasons 26 2. Luck 28 3. Anticipation 29 Lesson 1: Learn from others 29 Lesson 2: Use the right administrative tools 31 Lesson 3: Raise awareness 32 Lesson 4: Show resolve and determination 32 Lesson 5: Take (technical) precautions 33 Lesson 6: Put pressure on digital platforms 33 4. Reaction 34 Lesson 7: Make all hacking attempts public 34 Lesson 8: Gain control over the leaked information 34 Lesson 9: Stay focused and strike back 35 Lesson 10: Use humor 35 Lesson 11: Alert law enforcement 36 Lesson 12: Undermine propaganda outlets 36 Lesson 13: Trivialize the leaked content 37 Lesson 14: Compartmentalize communication 37 Lesson 15: Call on the media to behave responsibly 37 5. -
Julian Assange Judgment
JUDICIARY OF ENGLAND AND WALES District Judge (Magistrates’ Court) Vanessa Baraitser In the Westminster Magistrates’ Court Between: THE GOVERNMENT OF THE UNITED STATES OF AMERICA Requesting State -v- JULIAN PAUL ASSANGE Requested Person INDEX Page A. Introduction 2 a. The Request 2 b. Procedural History (US) 3 c. Procedural History (UK) 4 B. The Conduct 5 a. Second Superseding Indictment 5 b. Alleged Conduct 9 c. The Evidence 15 C. Issues Raised 15 D. The US-UK Treaty 16 E. Initial Stages of the Extradition Hearing 25 a. Section 78(2) 25 b. Section 78(4) 26 I. Section 78(4)(a) 26 II. Section 78(4)(b) 26 i. Section 137(3)(a): The Conduct 27 ii. Section 137(3)(b): Dual Criminality 27 1 The first strand (count 2) 33 The second strand (counts 3-14,1,18) and Article 10 34 The third strand (counts 15-17, 1) and Article 10 43 The right to truth/ Necessity 50 iii. Section 137(3)(c): maximum sentence requirement 53 F. Bars to Extradition 53 a. Section 81 (Extraneous Considerations) 53 I. Section 81(a) 55 II. Section 81(b) 69 b. Section 82 (Passage of Time) 71 G. Human Rights 76 a. Article 6 84 b. Article 7 82 c. Article 10 88 H. Health – Section 91 92 a. Prison Conditions 93 I. Pre-Trial 93 II. Post-Trial 98 b. Psychiatric Evidence 101 I. The defence medical evidence 101 II. The US medical evidence 105 III. Findings on the medical evidence 108 c. The Turner Criteria 111 I. -
Social Bots and Social Media Manipulation in 2020: the Year in Review
Social Bots and Social Media Manipulation in 2020: The Year in Review Ho-Chun Herbert Chang∗a, Emily Chen∗b,c, Meiqing Zhang∗a, Goran Muric∗b, and Emilio Ferraraa,b,c aUSC Annenberg School of Communication bUSC Information Sciences Institute cUSC Department of Computer Science *These authors contributed equally to this work Abstract The year 2020 will be remembered for two events of global significance: the COVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we summarize recent studies using large public Twitter data sets on these issues. We have three primary ob- jectives. First, we delineate epistemological and practical considerations when combin- ing the traditions of computational research and social science research. A sensible bal- ance should be struck when the stakes are high between advancing social theory and concrete, timely reporting of ongoing events. We additionally comment on the compu- tational challenges of gleaning insight from large amounts of social media data. Second, we characterize the role of social bots in social media manipulation around the discourse on the COVID-19 pandemic and 2020 U.S. Presidential Election. Third, we compare re- sults from 2020 to prior years to note that, although bot accounts still contribute to the emergence of echo-chambers, there is a transition from state-sponsored campaigns to do- mestically emergent sources of distortion. Furthermore, issues of public health can be confounded by political orientation, especially from localized communities of actors who spread misinformation. We conclude that automation and social media manipulation arXiv:2102.08436v1 [cs.SI] 16 Feb 2021 pose issues to a healthy and democratic discourse, precisely because they distort repre- sentation of pluralism within the public sphere.