Perception Is (as) Reality

It is the goal of this chapter is to explore the challenges inherent to a “post-fact” society through the lens of the school public information specialist and the library media specialist. The role of the school public information officer (PIO) has changed with the proliferation of opinion as “fact” on the internet and social media. Educating the public on all school-related matters, PIOs must be media-literate, effective content consumers and content generators, with the skills to gauge and predict the opinions of their voting public. Similarly, library media specialists tasked with educating students as consumers of information in the fast-paced, “on demand” digital age requires an understanding of their evolving role as content generators. Effective media literacy instruction encompasses more than simply using technology and electronic media in the educational setting. Rather, it begins with the understanding that students are ill equipped to critically evaluate the electronic mediums they so closely identify with.

Fake News Detection Using Deep Learning

The engendering of uncertain data in ordinary access news sources, for example, news sites, web-based life channels, and online papers, have made it trying to recognize capable news sources, along these lines expanding the requirement for computational instruments ready to give into the unwavering quality of online substance. For instance, counterfeit news outlets were observed to be bound to utilize language that is abstract and enthusiastic. At the point when specialists are chipping away at building up an AI-based apparatus for identifying counterfeit news, there wasn't sufficient information to prepare their calculations; they did the main balanced thing. In this chapter, two novel datasets for the undertaking of phony news locations, covering distinctive news areas, distinguishing proof of phony substance in online news has been considered. N-gram model will distinguish phony substance consequently with an emphasis on phony audits and phony news. This was pursued by a lot of learning analyses to fabricate precise phony news identifiers and showed correctness of up to 80%.

Saving Democracy

The recent rise of populism around the world, often accompanied by nationalism and isolationism, is a trend that presents a serious threat not only to liberal democracies but also to global peace and security. Populist forces have already shown their influence through the British referendum on membership withdrawal from the European Union and the election of Donald J. Trump as the U.S. President in 2016. These two events alone had ripple effects and were felt by the international community. The causes of populism are being currently revisited. It appears that socioeconomic and cultural aspects are key contributors. Even though the persistent existence of populist elements within societies comes from the very core of the democratic experiment, the current trend in social media technologies allowed demagogues to utilize viral deception on a considerable scale. The authors argue that social media technologies could be employed through e-participation to inhibit populism by bolstering civic empowerment, transparency, progressive inclusiveness, fact-based analysis, and informed decision making.

Fake News and Information Warfare

Fake news—false information passed off as factual—is an effective weapon in the information age. For instance, the Russian government perfected techniques used in its 2007 Estonian and 2008 Georgian cyber campaigns to support Donald Trump's successful candidacy in the 2016 United States presidential election. In this chapter, the authors examine fake news and Russia's cyberwarfare efforts across time as case studies of information warfare. The chapter identifies key terms and reviews extant political science and psychological research related to obtaining an understanding of psychological cyber warfare (“psywar”) through the proliferation of fake news. Specifically, the authors suggest that there are social, contextual, and individual factors that contribute to the spread and influence of fake news and review these factors in this chapter.

Reconstructing Diffusion Model for Virality Detection in News Spread Networks

In today's competitive world, organizations take advantage of widely-available data to promote their products and increase their revenue. This is achieved by identifying the reader's preference for news genre and patterns in news spread network. Spreading news over the internet seems to be a continuous process which eventually triggers the evolution of temporal networks. This temporal network comprises of nodes and edges, where node corresponds to published articles and similar articles are connected via edges. The main focus of this article is to reconstruct a susceptible-infected (SI) diffusion model to discover the spreading pattern of news articles for virality detection. For experimental analysis, a dataset of news articles from four domains (business, technology, entertainment, and health) is considered and the articles' rate of diffusion is inferred and compared. This will help to build a recommendation system, i.e. recommending a particular domain for advertisement and marketing. Hence, it will assist to build strategies for effective product endorsement for sustainable profitability.

An Empirically Supported Taxonomy of Misinformation

Fake news, which includes both disinformation and misinformation, has been a challenge for many countries in the last few years. Disinformation has been present in modern history as part of the tool kit of PSYOPS for the military. Likewise, misinformation has been part of human history for a long time. Hoaxes, rumors, and urban legends—all of which can be classified as differing types of misinformation, although they are not commonly addressed as such—have been exploited by adversarial organizations for their own benefit. This study will propose a comprehensive taxonomy to tackle fake news, disinformation, and misinformation and assess the level of threat they pose to society. A comprehensive comparison with existing typologies will also be included.

Using Social Media to Enhance Information Literacy

Academic librarians have long been committed to developing their students' abilities to assess the quality and credibility of various types of information. A combination of increasing public discourse about evaluating every day information and librarians' commitment to empowering students to be responsible consumers of information led Virginia Commonwealth University (VCU) librarians to develop the #VetYourSources campaign, focused on enhancing undergraduate students' skills for evaluating information in academic and day-to-day contexts through social media. This chapter details the design, planning, and execution of the campaign, as well as future directions.

Using Argumentation to Develop Critical Thinking About Social Issues in the Classroom

Thanks to the polarized nature of politics in the world today, students need to learn how to think critically about social issues. Argumentation can be both a type of critical thinking and a tool with which to teach students to think critically about social issues. This chapter lays out a framework for teaching students how to develop critical thinking about real world issues through the use of dialogic argumentation. The impact of dialogic argumentative activities in the classroom are discussed, particularly as they relate to the development of metacognition and theory of mind, as well as how they help students develop an “inner-locutor” that allows them to evaluate both their position and opposing positions. Finally, a model for how these elements contribute to students' value-loaded critical thinking about social issues is outlined.

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.

An Overview (and Criticism) of Methods to Detect Fake Content Online

The recent controversy over ‘fake news' reminds us of one of the main problems on the web today: the utilization of social media and other outlets to distribute false and misleading content. The impact of this problem is very significant. This article discusses the issue of fake content on the web. First, it defines the problem and shows that, in many cases, it is surprisingly hard to establish when a piece of news is untrue. It distinguishes the issue of fake content from issues of hate/offensive speech (while there is a relation, the issues involved are a bit different). It then overviews proposed solutions to the problem of fake content detection, both algorithmic and human. On the algorithmic side, it focuses on work on classifiers. The chapter shows that most algorithmic approaches have significant issues, which has led to reliance on the human approach in spite of its known limitations (subjectivity, difficulty to scale). Finally, it closes with a discussion of potential future work.