EXECUTIVE SUMMARY — HOW SOCIAL MEDIA, AI & IOT AFFECT THE TRAJECTORY OF POLITICAL SYSTEMS AND IDEOLOGIES
“If you want to change politics, you first have to change culture, because politics flows from culture.”
-Christopher Wilke, former data scientist at Cambridge Analytica
Like all things, political systems and ideologies are marked by a constant state of change. The nature of these changes depend upon the variety of factors that come to bear on a specific historical situation: historical events, technological change, philosophical ideas, religious ideas, human migration patterns, diseases, exchanges between different cultures… etc. Each of these factors has a role in shaping peoples’ ideology, which can be defined as “a set of beliefs about the proper order of society and how it can be achieved.” The collective ideology of a given population is known as an ideological landscape.
This executive summary argues that the leap in communication technologies marked by the advent of social media platforms (Facebook, Twitter, Youtube, Instagram) represent: a game-changing shift in how political systems and populations interact with each other, even if the content of that interaction remains based on historical and social factors. This summary argues that the complexity of this shift can be seen in the variety of ways that social media has been used by different political actors from the time period 2011-2017 to directly influence the trajectories of political systems, the ideological landscape they rest upon, as well as the units of that landscape — individual people.
As such, the first two case studies cited by my research present two opposing scenarios: one in which social media is used by a democratic movement to influence an authoritarian political system (2011 Egyptian Revolution); and another where social media is used by an authoritarian political system to influence the population of a democratic country (Russian interference in US politics, 2014 – present). Further improvement in machine learning has aided efforts by political actors to use social media to influence populations. This is seen in our third case study (Cambridge Analytica).
As we look toward the future, based on evidence from the 2011-2017 time period, it is reasonable to that data collection and analysis capabilities from machine learning will continue to be utilized by political actors. As such, the potential for the Internet of Things (IOT) in the context of the power of mass collection of data, will have further disruptive effects on politics.
Case Study 1: 2011 Egyptian Revolution
Egypt was among the first prominent examples of social media being central to a large-scale political upheaval. Indeed, the 2011 Egyptian Revolution was dubbed the “Facebook Revolution”. The Pew Research Center argues that social media platforms facilitated not just the Egyptian revolution, but the entire Arab Spring. It did so in three main ways:
Networks formed online were crucial in organizing a core group of activists, specifically in Egypt.
Civil society leaders in Arab countries emphasized the role of “the internet, mobile phones, and social media” in the protests.
Additionally, digital media has been used by Arabs to exercise freedom of speech and as a space for civic engagement.”
Indeed, social media shaped the nature of the protests themselves to make them more powerful – yet more unpredictable. Scholar Essam Mansour writes, “What makes social media such a powerful and unpredictable force in global politics is that they replace the need for a charismatic leader. Certainly, there were a number of outstanding and courageous protesters, but there was no single face attached to this revolution; They are impossible to control or shut down.” In a way, social media enabled the protests to behave like a swarm or flock, where all the units behave individually, yet somehow end up acting collectively.
However, Mansour also describes social media as “politically agnostic”, meaning that “there is no overseer that watches over, or brand that is stamped on a grass-roots social network. The best anyone can hope for is a small say in what goes on within it.”
Case Study 2: Russian Interference in US Politics
In May 2017, special counsel Robert Mueller was appointed to investigate possible collusion between Russia and the election campaign of US President Donald Trump. The investigation concluded that there was in a strategic effort made by Russian operatives to sow discord in the U.S. political system through using social media to disseminate fake news from as far back as 2014. Russian agents posed as US citizens, made social media groups, organized protests and created web sites with misinformation designed to exploit and exacerbate polarization amongst US citizens. Over time, these social media accounts reached significant numbers of Americans and helped the Trump Campaign defeat Hillary Clinton.
That said, it is unknown how much the effort helped the Trump campaign when compared to other factors. Some argue that the Russian troll farm wasn’t actually that effective or insidious, and they were doing very standard practices of audience development that any media company would be doing.
Cast Study 3: Steve Bannon, Cambridge Analytica & Culture War
Cambridge Analytica (CA) is the intersection of three things: big data, behavioural psychology and micro-targeting. “Big Data” is “an aggregation of all data points that you can get your hands on.” Micro-targeting refers to individualized messaging. So, instead of classifying a person as belonging to a particular voter class (i.e. latino voters or women voters), as has been done in traditional political analysis, micro-targeting allows for a specific message to be tailored to a specific personality. CA is credited with having assisted the Ted Cruz campaign, and then the Trump campaign in 2016.
In an interview with The Guardian, former CA data scientist Christopher Wilke discussed the core issue on how social media, data, and AI can affect not just political system but the ideological landscape itself. He described how Steve Bannon helped to start CA to be a “psychmetric weapon” to fight a culture war in the United States. Bannon was the former editor of Breitbart News, an organization that holds views associated with the Alt-Right that is highly critical of mainstream media for being unfairly liberally biased.
Wilke stated that Steve Bannon “the reason he was interested in this [psychometric weapon], is he had this idea of the Brietbart doctrine, which is that if you want to change politics, you first have to change culture, because politics flows from culture. And so what i said is that if you want to change culture, you first have to understand the units of culture. People are the units of culture. So if you want to change politics, you first have to change people, to change the culture.”
Trump’s success in the 2016 election as well as the success of the Breitbart new organization in general, can be seen as evidence of the success of this effort at changing the changing the individual “units of culture” using data, AI and social media.
Thus, the ideological landscape of the US has been, and is currently being, shifted by the interaction of social media (interface to the people), data (gathering of information on the people) and AI (analysis of information about the people).
In reviewing the case studies from above, it appears the description of social media as “politically agnostic” applies to big data and AI as well. The above evidence supports the interpretation of social media, big data and AI as representing game-changing increases in capabilities of political actors. As we have seen, for the political actors who have effectively utilized these technologies — they are given a significant advantage over their competition.
Changing people to change culture is not a new concept. In the 20 century, communist governments sough schemes to re-educate their populace to fit their political system. (China’s Cultural Revolution is one prime example of such efforts). As such, based on the examples above it is difficult to say how these technologies impact “ideology” in any one-sided way. This is contrary to earlier arguments of how new technologies were inherently “democratizing”.
The only thing that can be concluded is that new technology seems to be the primary means of political struggle in the foreseeable future, as in the example cites above, those who are not ahead are left behind.