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CHAPTER XXX. of Fallacies. Section 827. After Examining the Conditions on Which Correct Thoughts Depend, It Is Expedient to Clas
CHAPTER XXX. Of Fallacies. Section 827. After examining the conditions on which correct thoughts depend, it is expedient to classify some of the most familiar forms of error. It is by the treatment of the Fallacies that logic chiefly vindicates its claim to be considered a practical rather than a speculative science. To explain and give a name to fallacies is like setting up so many sign-posts on the various turns which it is possible to take off the road of truth. Section 828. By a fallacy is meant a piece of reasoning which appears to establish a conclusion without really doing so. The term applies both to the legitimate deduction of a conclusion from false premisses and to the illegitimate deduction of a conclusion from any premisses. There are errors incidental to conception and judgement, which might well be brought under the name; but the fallacies with which we shall concern ourselves are confined to errors connected with inference. Section 829. When any inference leads to a false conclusion, the error may have arisen either in the thought itself or in the signs by which the thought is conveyed. The main sources of fallacy then are confined to two-- (1) thought, (2) language. Section 830. This is the basis of Aristotle's division of fallacies, which has not yet been superseded. Fallacies, according to him, are either in the language or outside of it. Outside of language there is no source of error but thought. For things themselves do not deceive us, but error arises owing to a misinterpretation of things by the mind. -
BPGC at Semeval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning
BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning Rajaswa Patil1 and Somesh Singh2 and Swati Agarwal2 1Department of Electrical & Electronics Engineering 2Department of Computer Science & Information Systems BITS Pilani K. K. Birla Goa Campus, India ff20170334, f20180175, [email protected] Abstract Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification - given any news article, the system tags those specific fragments which contain at least one propaganda technique and Technique Classification - correctly classify a given propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach. For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features. Our results reveal that the linguistic features are the reliable indicators for covering minority classes in a highly imbalanced dataset. 1 Introduction Propaganda is biased information that deliberately propagates a particular ideology or political orientation (Aggarwal and Sadana, 2019). Propaganda aims to influence the public’s mentality and emotions, targeting their reciprocation due to their personal beliefs (Jowett and O’donnell, 2018). News propaganda is a sub-type of propaganda that manipulates lies, semi-truths, and rumors in the disguise of credible news (Bakir et al., 2019). -
35. Logic: Common Fallacies Steve Miller Kennesaw State University, [email protected]
Kennesaw State University DigitalCommons@Kennesaw State University Sexy Technical Communications Open Educational Resources 3-1-2016 35. Logic: Common Fallacies Steve Miller Kennesaw State University, [email protected] Cherie Miller Kennesaw State University, [email protected] Follow this and additional works at: http://digitalcommons.kennesaw.edu/oertechcomm Part of the Technical and Professional Writing Commons Recommended Citation Miller, Steve and Miller, Cherie, "35. Logic: Common Fallacies" (2016). Sexy Technical Communications. 35. http://digitalcommons.kennesaw.edu/oertechcomm/35 This Article is brought to you for free and open access by the Open Educational Resources at DigitalCommons@Kennesaw State University. It has been accepted for inclusion in Sexy Technical Communications by an authorized administrator of DigitalCommons@Kennesaw State University. For more information, please contact [email protected]. Logic: Common Fallacies Steve and Cherie Miller Sexy Technical Communication Home Logic and Logical Fallacies Taken with kind permission from the book Why Brilliant People Believe Nonsense by J. Steve Miller and Cherie K. Miller Brilliant People Believe Nonsense [because]... They Fall for Common Fallacies The dull mind, once arriving at an inference that flatters the desire, is rarely able to retain the impression that the notion from which the inference started was purely problematic. ― George Eliot, in Silas Marner In the last chapter we discussed passages where bright individuals with PhDs violated common fallacies. Even the brightest among us fall for them. As a result, we should be ever vigilant to keep our critical guard up, looking for fallacious reasoning in lectures, reading, viewing, and especially in our own writing. None of us are immune to falling for fallacies. -
Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People’S Republic of China
Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People’s Republic of China Rong-Ching Chang Chun-Ming Lai [email protected] [email protected] Tunghai University Tunghai University Taiwan Taiwan Kai-Lai Chang Chu-Hsing Lin [email protected] [email protected] Tunghai University Tunghai University Taiwan Taiwan ABSTRACT ACM Reference Format: The digital media, identified as computational propaganda provides Rong-Ching Chang, Chun-Ming Lai, Kai-Lai Chang, and Chu-Hsing Lin. a pathway for propaganda to expand its reach without limit. State- 2021. Dataset of Propaganda Techniques of the State-Sponsored Informa- tion Operation of the People’s Republic of China. In KDD ’21: The Sec- backed propaganda aims to shape the audiences’ cognition toward ond International MIS2 Workshop: Misinformation and Misbehavior Min- entities in favor of a certain political party or authority. Further- ing on the Web, Aug 15, 2021, Virtual. ACM, New York, NY, USA, 5 pages. more, it has become part of modern information warfare used in https://doi.org/10.1145/nnnnnnn.nnnnnnn order to gain an advantage over opponents. Most of the current studies focus on using machine learning, quantitative, and qualitative methods to distinguish if a certain piece 1 INTRODUCTION of information on social media is propaganda. Mainly conducted Propaganda has the purpose of framing and influencing opinions. on English content, but very little research addresses Chinese Man- With the rise of the internet and social media, propaganda has darin content. From propaganda detection, we want to go one step adopted a powerful tool for its unlimited reach, as well as multiple further to providing more fine-grained information on propaganda forms of content that can further drive engagement online and techniques that are applied. -
Better Thinking and Reasoning Chapter 11 Test Answer
Name ________________________________ Better Thinking and Reasoning Chapter 11 Test Page 1 Answer each question. 1. Define formal fallacy. an error in the form of reasoning (caught by using a truth table) 2. Define informal fallacy. an error in the relationship of the premises (caught by examining context) 3. When is a valid argument unsound? when it has a false premise 4. What should you always do after you have completed your essay? rewrite it and check it for logical fallacies Classify the informal fallacy in each deductive argument as accident, begging the question, composition, division, or equivocation. 5. Anyone who thrusts a knife into another person should be arrested on the grounds of attempted murder. Since surgeons regularly put knives into their victims, they should be arrested for attempted murder. accident 6. Every atom in this chair is invisible. Therefore, the chair is invisible. composition 7. Please be quiet. You are making far too much noise. begging the question 8. A watermelon is a fruit. Therefore, a small watermelon is a small fruit. equivocation 9. The average American family has 2.4 children. The Smiths are an average American family. Therefore the Smiths have 2.4 children. division Classify each deductive argument as displaying a false dichotomy or having one of these three formal fallacies: affirming the consequent, denying the antecedent, or improper negation. 10. If your pet is a whale or a dolphin, then your pet is a mammal. Judy is not a mammal. Therefore, Judy is not a whale or not a dolphin. improper negation 11. If Probst won the election, then we would have a Democrat for mayor. -
Skypemorph: Protocol Obfuscation for Tor Bridges
SkypeMorph: Protocol Obfuscation for Tor Bridges Hooman Mohajeri Moghaddam Baiyu Li Mohammad Derakhshani Ian Goldberg Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada {hmohajer,b5li,mderakhs,iang}@cs.uwaterloo.ca ABSTRACT The Tor network is designed to provide users with low- latency anonymous communications. Tor clients build cir- cuits with publicly listed relays to anonymously reach their destinations. However, since the relays are publicly listed, they can be easily blocked by censoring adversaries. Con- sequently, the Tor project envisioned the possibility of un- listed entry points to the Tor network, commonly known as bridges. We address the issue of preventing censors from detecting the bridges by observing the communications be- tween them and nodes in their network. We propose a model in which the client obfuscates its mes- sages to the bridge in a widely used protocol over the Inter- net. We investigate using Skype video calls as our target protocol and our goal is to make it difficult for the censor- Figure 1: This graph from metrics.torproject.org shows the ing adversary to distinguish between the obfuscated bridge number of users directly connecting to the Tor network from connections and actual Skype calls using statistical compar- China, from mid-2009 to the present. It shows that, after isons. 2010, the Tor network has been almost completely blocked We have implemented our model as a proof-of-concept from clients in China who do not use bridges. [41] pluggable transport for Tor, which is available under an open-source licence. Using this implementation we observed the obfuscated bridge communications and compared it with attempts to block access to Tor by regional and state-level those of Skype calls and presented the results. -
Inductive Arguments: Fallacies ID1050– Quantitative & Qualitative Reasoning Analyzing an Inductive Argument
Inductive Arguments: Fallacies ID1050– Quantitative & Qualitative Reasoning Analyzing an Inductive Argument • In an inductive argument, the conclusion follows from its premises with some likelihood. • Inductive arguments can be strong, weak, or somewhere between. • Ways to attack an inductive argument: • Introduce additional (contradictory) premises that weaken the argument. • Question the accuracy of the supporting premises. • Identify one (or more) logical fallacies in the argument. What is a Fallacy? • A logical fallacy is an error in reasoning in an argument. • Formal fallacy • A ‘formal fallacy’ is an error in the structure of an argument. • Formal fallacies are used to analyze deductive arguments for validity by means of symbolic logic. • Informal fallacy • An ‘informal fallacy’ is an error in the content of an argument. • This is the type of fallacy that will be discussed in this presentation. • An argument with a fallacy is said to be ‘fallacious’. Formal and Informal Fallacies • Formal fallacy example: • All humans are mammals. All dogs are mammals. So, all humans are dogs. • This argument has a structural flaw. The premises are true, but they do not logically lead to the conclusion. This would be uncovered by the use of symbolic logic. • Informal fallacy example: • All feathers are light. Light is not dark. So, all feathers are not dark. • The structure of this argument is actually correct. The error is in the content (different meanings of the word ‘light’.) It uses a fallacy called ‘Equivocation’. Lists of Fallacies • There are a great number of identified fallacies of the informal type. Following are some good websites that list them and provide definitions and examples. -
Global Manipulation by Local Obfuscation ∗
Global Manipulation by Local Obfuscation ∗ Fei Liy Yangbo Songz Mofei Zhao§ August 26, 2020 Abstract We study information design in a regime change context. A continuum of agents simultaneously choose whether to attack the current regime and will suc- ceed if and only if the mass of attackers outweighs the regime’s strength. A de- signer manipulates information about the regime’s strength to maintain the status quo. The optimal information structure exhibits local obfuscation, some agents receive a signal matching the true strength of the status quo, and others receive an elevated signal professing slightly higher strength. Public signals are strictly sub- optimal, and in some cases where public signals become futile, local obfuscation guarantees the collapse of agents’ coordination. Keywords: Bayesian persuasion, coordination, information design, obfuscation, regime change JEL Classification: C7, D7, D8. ∗We thank Yu Awaya, Arjada Bardhi, Gary Biglaiser, Daniel Bernhardt, James Best, Jimmy Chan, Yi-Chun Chen, Liang Dai, Toomas Hinnosaar, Tetsuya Hoshino, Ju Hu, Yunzhi Hu, Chong Huang, Nicolas Inostroza, Kyungmin (Teddy) Kim, Qingmin Liu, George Mailath, Laurent Mathevet, Stephen Morris, Xiaosheng Mu, Peter Norman, Mallesh Pai, Alessandro Pavan, Jacopo Perego, Xianwen Shi, Joel Sobel, Satoru Takahashi, Ina Taneva, Can Tian, Kyle Woodward, Xi Weng, Ming Yang, Jidong Zhou, and Zhen Zhou for comments. yDepartment of Economics, University of North Carolina, Chapel Hill. Email: [email protected]. zSchool of Management and Economics, The Chinese University of Hong Kong, Shenzhen. Email: [email protected]. §School of Economics and Management, Beihang University. Email: [email protected]. 1 1 Introduction The revolution of information and communication technology raises growing con- cerns about digital authoritarianism.1 Despite their tremendous effort to establish in- formation censorship and to spread disinformation, full manipulation of information remains outside autocrats’ grasp in the modern age. -
Fallacy: a Defect in an Argument That Arises from a Mistake in Reasoning Or the Creation of an Illusion That Makes a Bad Argument Appear Good
Fallacy: A defect in an argument that arises from a mistake in reasoning or the creation of an illusion that makes a bad argument appear good. There are two kinds of fallacy: • Formal fallacy: Detectable by analyzing the form of an argument • Informal fallacy: Detectable only by analyzing the content of an argument Fallacies of Relevance: The premises are not relevant to the conclusion: • Appeal to force: Arguer threatens the reader/ listener. • Appeal to pity: Arguer elicits pity from the reader/ listener. • Appeal to the people (Ad Populum): Arguer incites a mob mentality ( direct form) or appeals to our desire for security, love, or respect ( indirect form). This fallacy includes appeal to fear, the bandwagon argument, appeal to vanity, appeal to snobbery, and appeal to tradition. • Argument against the person (Ad hominems): -Arguer personally attacks an opposing arguer by verbally abusing the opponent ( ad hominem abusive) – Presenting the opponent as predisposed to argue as he or she does ( ad hominen circumstantial), or by – Presenting the opponent as a hypocrite ( tu quoque). For ad hominem to occur, there must be two arguers. • Accident: A general rule is applied to a specific case it was not intended to cover. • Straw man: Arguer distorts an opponent’s argument and then attacks the distorted argument. N ote: For this fallacy to occur, there must be two arguers. • Missing the point: Arguer draws a conclusion different from the one supported by the premises. N ote: Do not cite this fallacy if another fallacy fits. • Red herring: Arguer leads the reader/ listener off the track. Fallacies of Weak Induction: The premises may be relevant to the conclusion, but they supply insufficient support for the conclusion: • Appeal to unqualified authority: Arguer cites an untrustworthy authority. -
Improving Meek with Adversarial Techniques
Improving Meek With Adversarial Techniques Steven R. Sheffey Ferrol Aderholdt Middle Tennessee State University Middle Tennessee State University Abstract a permitted host [10]. This is achieved by manipulating the Host header of the underlying encrypted HTTP payload in As the internet becomes increasingly crucial to distributing in- order to take advantage of cloud hosting services that forward formation, internet censorship has become more pervasive and HTTP traffic based on this header. Domain fronting exploits advanced. Tor aims to circumvent censorship, but adversaries censors’ unwillingness to cause collateral damage, as block- are capable of identifying and blocking access to Tor. Meek, ing domain fronting would require also blocking the typically a traffic obfuscation method, protects Tor users from censor- more reputable, permitted host. From the point of view of ship by hiding traffic to the Tor network inside an HTTPS an adversary using DPI and metadata-based filtering, there is connection to a permitted host. However, machine learning no difference between Meek and normal HTTPS traffic. All attacks using side-channel information against Meek pose unencrypted fields in Meek traffic that could indicate its true a significant threat to its ability to obfuscate traffic. In this destination such as DNS requests, IP addresses, and the Server work, we develop a method to efficiently gather reproducible Name Indication (SNI) inside HTTPS connection headers do packet captures from both normal HTTPS and Meek traffic. not reveal its true destination. We then aggregate statistical signatures from these packet However, recent work has shown that Meek is vulnerable captures. Finally, we train a generative adversarial network to machine learning attacks that use side-channel informa- (GAN) to minimally modify statistical signatures in a way tion such as packet size and timing distributions to differenti- that hinders classification. -
The Production and Circulation of Propaganda
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by RMIT Research Repository The Only Language They Understand: The Production and Circulation of Propaganda A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Christian Tatman BA (Journalism) Hons RMIT University School of Media and Communication College of Design and Social Context RMIT University February 2013 Declaration I Christian Tatman certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed. Christian Tatman February 2013 i Acknowledgements I would particularly like to thank my supervisors, Dr Peter Williams and Associate Professor Cathy Greenfield, who along with Dr Linda Daley, have provided invaluable feedback, support and advice during this research project. Dr Judy Maxwell and members of RMIT’s Research Writing Group helped sharpen my writing skills enormously. Dr Maxwell’s advice and the supportive nature of the group gave me the confidence to push on with the project. Professor Matthew Ricketson (University of Canberra), Dr Michael Kennedy (Mornington Peninsula Shire) and Dr Harriet Speed (Victoria University) deserve thanks for their encouragement. My wife, Karen, and children Bethany-Kate and Hugh, have been remarkably patient, understanding and supportive during the time it has taken me to complete the project and deserve my heartfelt thanks. -
Machine Learning Model That Successfully Detects Russian Trolls 24 3
Human–machine detection of online-based malign information William Marcellino, Kate Cox, Katerina Galai, Linda Slapakova, Amber Jaycocks, Ruth Harris For more information on this publication, visit www.rand.org/t/RRA519-1 Published by the RAND Corporation, Santa Monica, Calif., and Cambridge, UK © Copyright 2020 RAND Corporation R® is a registered trademark. RAND Europe is a not-for-profit research organisation that helps to improve policy and decision making through research and analysis. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org www.randeurope.org III Preface This report is the final output of a study proof-of-concept machine detection in a known commissioned by the UK Ministry of Defence’s troll database (Task B) and tradecraft analysis (MOD) Defence Science and Technology of Russian malign information operations Laboratory (Dstl) via its Defence and Security against left- and right-wing publics (Task C). Accelerator (DASA).