Lethal, Viral, Global The Role of Mobile Media and the Growing International Scourge of Fake News
Gordon Kuo Siong Tan, Sun Sun Lim, and Roy Kheng
The rising ubiquity of mobile phones and other mobile-enabled devices, even among bottom-of-pyramid users, has greatly accelerated the spread of online disinformation. Whereas news was previously shared via a one-to-many broadcast model in which incumbent news providers played a gatekeeping role and tried to adhere to journalistic standards, media production and dissemination capabilities are now within easy reach of everyday consumers. We have thus transitioned to an era of many-to-many communication, in which news is no longer the preserve of established media companies but can be produced and widely shared by media consumers themselves.This democratization of media production and dissemination has also contributed to the surge in online disinformation because consumers may become key nodes in sharing fabricated information that is calculated to be eye-catching, sensationalist, or titillating. This chapter will analyze the spread of fake news and the role that mobile media has played in exacerbating this pernicious trend in a globalized media landscape.
We examine the architecture of social media platforms built upon user engagement and sharing of content where advertisement-driven revenue models have encouraged the proliferation of viral and inflammatory content, with journalistic best practices like fact-checking and source verification being de-emphasized. We also explain the role of human factors and cognitive biases in spreading fake news, even as content production is within easier reach of everyday media consumers. Finally, we explore the role of emerging technologies such as social media bots and “deep fake” videos that facilitate the spread of fake news at much faster speeds to an ever-growing global audience.
Surveillance Capitalism and the Rise of Social Media
Contemporary capitalism has spawned a new breed of high-technology companies with entirely different business models. Technology titans like Facebook, Google, and Twitter serve users globally by offering complimentary services that are supported by advertising, through which they capture user data that can be monetized in multiple ways. Zuboft (2019) calls this “surveillance capitalism,” where technology is used to surveil users and to commodify them as profit-making objects by monitoring and influencing their online behavior.
Social media firms in particular generate lucrative advertising revenue by “monetizing” attention through motivating users to stay longer and interact more intensively with their platforms. Social media platforms increase user engagement by using proprietary algorithms to personalize news (and advertising) content. These algorithms sort and organize content based on relevancy rather than chronological order and employ complex statistical models to track and aggregate voluminous amounts of user data to infer and predict user preferences and behavior. Information is then algorithmically curated to deliver customized content that captures user attention. The emergence of the always-on, always-connected multi-function smartphone gave social media a real shot in the arm (Westlund, 2014). With people increasingly tethered to their smartphones, their usage of social media increased both in quantity and in form, further invigorated by the rise of location-based services (Katz & Lai, 2014) that enable an even more precise delivery of personalized content.
One frequently cited example is Facebook’s news feed function. It is powered by sophisticated algorithms in which “nearly every interaction with content on Facebook informs the algorithm to accommodate accordingly” (Wiggins, 2017: 19). Algorithms actively predict the information that users want to see by harvesting and analyzing data from their digital trail: users’ profiles, the profiles of their friends, browsing and search histories, their locations, and virtually every trackable online activity within legally permissible limits. Trending stories are tailored to each user’s preferences as determined by the algorithms. Content is refreshed and displayed in real time according to popularity, as measured by user engagement metrics such as “likes,” “clicks,” and “shares.”
It is in precisely such a user-driven and commercially oriented architecture that falsehoods have thrived. Notably,Vosoughi, Roy, and Aral (2018) show that false stories are more captivating and spread faster than real news. Individuals react more to content that elicits stronger emotional responses like shock, amazement, fear, and disgust. Therefore, fake news is intentionally crafted to trigger emotional responses by being sensational, to ensure that it is liberally shared and widely circulated. Social media algorithms that heavily prioritize user engagement thus actively promote inflammatory content to users and those in their network. The act of sharing by users gives further traction to fake news because it confers implicit endorsement that makes the message appear compelling and plausible. As more people turn to social media as their main source of news (Shearer, 2018), these platforms have become the prime conduits for ill-intentioned actors seeking to disseminate falsehoods.