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by Michael D. Intriligator (jan 2003)
4. The Benefits of Globalization Stemming from Competition It has already been noted that globalization has both positive and negative effects. This section focuses on the positive effects of globalization, stemming from competition, while the next will focus on its negative effects that could lead to potential conflicts. Finally, the last section considers the potential for international cooperation to diminish or to offset the negative effects of globalization. Globalization has led to growing competition on a global basis. While some fear competition, there are many beneficial effects of competition that can increase production or efficiency. Competition and the widening of markets can lead to specialization and the division of labor, as discussed by Adam Smith and other classical economists writing on the benefits of a market system. Specialization and the division of labor, with their implications for increases in production, now exist not just in a nation but also on a worldwide basis. Other beneficial effects include the economies of scale and scope that can potentially lead to reductions in costs and prices and are conducive to continuing economic growth. Other benefits of globalization include the gains from trade in which both parties gain in a mutually beneficial exchange, where the "parties" can be individuals, firms and other organizations, nations, trading blocs, continents or other entities. Globalization can also result in increased productivity as a result of the rationalization of production on a global scale Globalization of the World Economy January 2003 www.milkeninstitute.org 9 and the spread of technology and competitive pressures for continual innovation on a worldwide basis. Overall, these beneficial effects of competition stemming from globalization show its potential value in improving the position of all parties, with the potential for increased output and higher real wage levels and living standards. The result is a potential for greater human well being throughout the world. Of course, there is the distributional or equity issue of who does, in fact, gain from these potential benefits of globalization.

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Fake News Detection on Social Media: A Data Mining Perspective Kai Shu† , Amy Sliva‡ , Suhang Wang† , Jiliang Tang \ , and Huan Liu† †Computer Science & Engineering, Arizona State University, Tempe, AZ, USA ‡Charles River Analytics, Cambridge, MA, USA \Computer Science & Engineering, Michigan State University, East Lansing, MI, USA † {kai.shu,suhang.wang,huan.liu}@asu.edu, ‡asliva@cra.com, \ tangjili@msu.edu ABSTRACT Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of “fake news”, i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users’ social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

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https://library.oapen.org/bitstream/handle/20.500.12657/30772/642730.pdf?sequence=1