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Fake news has existed for a very long time, nearly the same amount of time as news began to circulate widely after the printing press was invented in 1439. A narrow definition of fake news is news articles that are intentionally and verifiably false and could mislead readers. There are two key features of this definition: authenticity and intent. First, fake news includes false information that can be verified as such. Second, fake news is created with dishonest intention to mislead consumers. This definition has been widely adopted in recent studies. Broader definitions of fake news focus on the either authenticity or intent of the news content. Some papers regard satire news as fake news since the contents are false even though satire is often entertainment-oriented and reveals its own deceptiveness to the consumers. Other literature directly treats deceptive news as fake news, which includes serious fabrications, hoaxes, and satires. As an increasing amount of our lives is spent interacting online through social media platforms, more and more people tend to seek out and consume news from social media rather than traditional news organizations. The reasons for this change in consumption behaviours are inherent in the nature of these social media platforms: (i) it is often more timely and less expensive to consume news on social media compared with traditional news media, such as newspapers or television; and (ii) it is easier to further share, comment on, and discuss the news with friends or other readers on social media. For example, 62 percent of U.S. adults get news on social media in 2016, while in 2012, only 49 percent reported seeing news on social media. It was also found that social media now outperforms television as the major news source. Despite the advantages provided by social media, the quality of news on social media is lower than traditional news organizations. However, because it is cheap to provide news online and much faster and easier to disseminate through social media, large volumes of fake news, i.e., those news articles with intentionally false information, are produced online for a variety of purposes, such as financial and political gain. It was estimated that over 1 million tweets are related to fake news “Pizzagate” by the end of the presidential election. Given the prevalence of this new phenomenon, “Fake news” was even named the word of the year by the Macquarie dictionary in 2016. The extensive spread of fake news can have a serious negative impact on individuals and society. First, fake news can break the authenticity balance of the news ecosystem. For example, it is evident that the most popular fake news was even more widely spread on Facebook than the most popular authentic mainstream news during the U.S. 2016 president election. Second, fake news intentionally persuades consumers to accept biased or false beliefs. Fake news is usually manipulated by propagandists to convey political messages or influence. For example, some report shows that Russia has created fake accounts and social bots to spread false stories. Third, fake news changes the way people interpret and respond to real news. For example, some fake news was just created to trigger people’s distrust and make them confused, impeding their abilities to differentiate what is true from what is not. To help mitigate the negative effects caused by fake news–both to benefit the public and the news ecosystem. It’s critical that we develop methods to automatically detect fake news on social media.
There are several characteristics of this problem that make it uniquely challenging for automated detection. First, fake news is intentionally written to mislead readers, which makes it nontrivial to detect simply based on news content. The content of fake news is rather diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. For example, fake news may cite true evidence within the incorrect context to support a non-factual claim. Thus, existing hand-crafted and data-specific textual features are generally not sufficient for fake news detection. Other auxiliary information must also be applied to improve detection, such as knowledge base and user social engagements. Second, exploiting this auxiliary information actually leads to another critical challenge: the quality of the data itself. Fake news is usually related to newly emerging, time-critical events, which may not have been properly verified by existing knowledge bases due to the lack of corroborating evidence or claims. In addition, users’ social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Effective methods to differentiate credible users, extract useful post features and exploit network interactions are an open area of research and need further investigations.

Post Author: admin