Matching Items (3)

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Dangers of Information Control and Misinformation in the Modern Era

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The basis of social power which has expanded in the most dangerous way over the last few decades has been that of information control, and how that control is used.

The basis of social power which has expanded in the most dangerous way over the last few decades has been that of information control, and how that control is used. Misinformation and the intentional spread of misinformation referred to as disinformation, has become commonplace among various bodies of power to either expand their own influence or diminish opposing influence. The methods of disinformation utilized in the various spheres of politics, the commercial marketplace, and the media today are explored in depth to better contextualize and describe the problems that disinformation and its use pose in the world today.

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  • 2021-05

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Russia's War on Democracy: Analyzing Disinformation in the United States, Latvia, and Colombia

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Disinformation has long been a tactic used by the Russian government to achieve its goals. Today, Vladimir Putin aims to achieve several things: weaken the United States’ strength on the

Disinformation has long been a tactic used by the Russian government to achieve its goals. Today, Vladimir Putin aims to achieve several things: weaken the United States’ strength on the world stage, relieve Western sanctions on himself and his inner circle, and reassert dominant influence over Russia’s near abroad (the Baltics, Ukraine, etc.). This research analyzed disinformation in English, Spanish, and Russian; noting the dominant narratives and geopolitical goals Russia hoped to achieve by destabilizing democracy in each country/region.

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  • 2021-05

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Understanding Disinformation: Learning with Weak Social Supervision

Description

Social media has become an important means of user-centered information sharing and communications in a gamut of domains, including news consumption, entertainment, marketing, public relations, and many more. The low

Social media has become an important means of user-centered information sharing and communications in a gamut of domains, including news consumption, entertainment, marketing, public relations, and many more. The low cost, easy access, and rapid dissemination of information on social media draws a large audience but also exacerbate the wide propagation of disinformation including fake news, i.e., news with intentionally false information. Disinformation on social media is growing fast in volume and can have detrimental societal effects. Despite the importance of this problem, our understanding of disinformation in social media is still limited. Recent advancements of computational approaches on detecting disinformation and fake news have shown some early promising results. Novel challenges are still abundant due to its complexity, diversity, dynamics, multi-modality, and costs of fact-checking or annotation.

Social media data opens the door to interdisciplinary research and allows one to collectively study large-scale human behaviors otherwise impossible. For example, user engagements over information such as news articles, including posting about, commenting on, or recommending the news on social media, contain abundant rich information. Since social media data is big, incomplete, noisy, unstructured, with abundant social relations, solely relying on user engagements can be sensitive to noisy user feedback. To alleviate the problem of limited labeled data, it is important to combine contents and this new (but weak) type of information as supervision signals, i.e., weak social supervision, to advance fake news detection.

The goal of this dissertation is to understand disinformation by proposing and exploiting weak social supervision for learning with little labeled data and effectively detect disinformation via innovative research and novel computational methods. In particular, I investigate learning with weak social supervision for understanding disinformation with the following computational tasks: bringing the heterogeneous social context as auxiliary information for effective fake news detection; discovering explanations of fake news from social media for explainable fake news detection; modeling multi-source of weak social supervision for early fake news detection; and transferring knowledge across domains with adversarial machine learning for cross-domain fake news detection. The findings of the dissertation significantly expand the boundaries of disinformation research and establish a novel paradigm of learning with weak social supervision that has important implications in broad applications in social media.

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  • 2020