John Robb has a concept of Networked Tribalism, which is a result of the combination of filter bubbles, AI algorithms, and political polarization. Robb is a military analyst who looks at the impact of technology on culture, and he started his Global Guerrillas blog back in 2003 where he was tracking what he calls the “Open Source Insurgency” in Iraq where there over 70 different factions who were all collaborating against the U.S. occupation. Open Source Insurgency morphed into Open Source Protest with Occupy Wall Street and the protest movements around the world, and then eventually into Open Source Politics with the 2016 Trump campaign, and then eventually into what he calls “Networked Tribalism.” Robb now runs his Global Guerrillas Report through his Patreon.
Robb uses David Ronfeldt’s framework laid out in a 1996 paper titled “Tribes, Institutions, Markets, Networks: A Framework About Societal Evolution” that splits society into the following groups:
- “the kinship-based tribe, as denoted by the structure of extended families, clans, and other lineage systems;”
- “the hierarchical institution, as exemplified by the army, the (Catholic) church, and ultimately the bureaucratic state;”
- “the competitive-exchange market, as symbolized by merchants and traders responding to forces of supply and demand;”
- “and the collaborative network, as found today in the web-like ties among some NGOs devoted to social advocacy.”
Media theorist Marshall McLuhan talked about tribalism with Mike McManus in his last televised interview on September 19, 1977, where he predicted digitally-mediated tribalism.
In the years following McLuhan and Ronfeldt’s work using the terms of tribes and tribalism, there have been historians like Chris Lowe who have pointed some of the cultural baggage that comes with these terms that’s explored in his essay The Trouble with Tribe. But Robb is referencing McLuhan’s & Ronfeldt’s work when he talks about the “Networked Tribalism” dynamics with regards to the types of mob-mentality and phase alignment he’s seeing in online behaviors.
The type of networked tribalism that Robb is looking at is happening within a deeper cultural context of political polarization. The United States election was called for Joe Biden by the Associated Press on 11:25am EST on Saturday, November 7th after 3.5 days of counting ballots in a few key battleground states. The electoral college race was a lot closer than the overall popular vote where Biden got over 74 millions total votes while Donald Trump received over 70 million votes. For a lot of people inside and outside of the United States, then it may be a bit confusing as to why this race was even as close as it was. But the level of political polarization in the United States can’t be underestimated as there seems to be two completely different filter bubbles that have a different set of facts that form mutually exclusive narratives on the story of truth and reality.
There’s a deeper cultural context for this political polarization that Pew Research reported on back in October 5, 2017. Going back through nearly 20 years of surveys, their research found “widening differences between Republicans and Democrats on a range of measures the Center has been asking about since 1994.”
Michigan State Associate Professor Zachary Neal did a network analysis of legislation over the past 40 years, in order to document an increasing amount of political polarization. His 2020 paper in Social Networks titled A sign of the times? Weak and strong polarization in the U.S. Congress, 1973–2016 documents decreasing amounts of bi-partisan collaboration in favor of no-compromise, partisan alignment.
There are also elements within the media ecosystem that have been becoming more and more explicitly partisan in their coverage as documented by the Media Bias Chart 6.0 by Ad Fontes Media. They map out different news organizations on a spectrum of left vs right political bias as well as on a spectrum of reliability vs unreliability.
It’s within this larger cultural context where user behavior combined with technology algorithms at Facebook, Google, YouTube, and Twitter that have made the boundaries of these filter bubbles of reality more explicit. Eli Pariser’s 2011 TED talk popularized the “filter bubble” concept, and the technology firms may be merely reflecting and amplifying our patterns of behavior that are driven by a confirmation bias to consume information that reinforces rather than challenges our assumptions about the nature of reality. It’s a lot harder to train algorithms to provide users with aspirational content that’s both relevant, important, uncomfortable, challenging, and has a diversity of alternative points of view and perspectives.
The issue of filter bubbles has reached the level of technology policy with Senator John Thune introducing the Filter Bubble Transparency Act that would require technology companies to disclose how algorithms filter information on their services, but also the option to turn off the algorithmic-driven timelines and search results in order to escape these data-driven filter bubbles.
The combination of the political polarization, filter bubbles, and AI algorithms is cultivating a deeper context for networked tribalism to thrive within our culture. Robb was the Sensemaker in Residence for a four-part series of Zoom talks at Peter Limberg’s Stoa throughout August 2020 that were posted on their YouTube Channel on October 3, 2020: PART 1: August 10, PART 2: August 17, PART 3: August 24, & PART 4: August 31.
I wanted to invite Robb onto the Voices of VR podcast because I found his “Networked Tribalism” sensemaking framework to be helpful in making sense of some of the cultural and political dynamics in the United States, and how they’re interfacing with technology policy issues around filter bubbles & the impacts of algorithmic filtering, as well as the dynamics of censorship online weighed against the role of a code of conduct and community standards in order to create online spaces that are free from abuse and harassment.
The First Amendment protects the freedom of speech in relationship to the U.S. government, but it doesn’t extend out to private property or for big technology platforms where speech is regulated by their terms of service, codes of conduct, and community guidelines. But even the First Amendment has a number of different free speech exceptions such as the “Fighting Words” category of speech, incitement, false statements of fact, obscenity, child pornography, threatening the President of the United States, or speech owned by others. Each technology company has to decide how it weights the benefits of free speech with the potential harms that come from all of the unprotected classes of free speech, and how they will enforce it on their platform.
Whether or not the enforcement of these codes of conduct and community guidelines is seen as political censorship or the regulation of unprotected speech depends a lot on the larger cultural and political context that’s driven by the narratives that leaders and influencers within these political factions are creating. Then what happens when the boundaries of acceptable and unacceptable behavior and speech are determined by machine learning data sets that operate at scale and are imperfect in their implementation? And then what happens when your access to virtual and augmented reality will be determined by your actions and behaviors in a media ecosystem that’s being monitored by these same black box AI algorithms?
Robb expects that the intersection between Filter Bubbles + AI Algorithms + Political Polarization will continue to accelerate and drive collective behaviors through networked tribalism, and it’s an open question to what degree the technology policy and legal legislation will be able to reign this larger cultural and political dynamic.
You can look at this issue through a couple of lenses like Ronfeldt’s framework of “Tribes, Institutions, Markets, Networks” or Lawrence Lessig’s Pathetic Dot Theory of law, social norms/culture, the market, and technological architecture/code. Either way, there’s certainly a large cultural and political aspect where these affinity groups cultivate in-group dynamics through the combination of networked communication architectures that build alignment either through psychologically-driven confirmation bias, algorithmic-enforced filter bubbles, or a increasingly-biased media ecosystem that’s not accurately representing all sides of a story or countering misinformation and propaganda coming from political leaders. This is obviously a very complicated, but also deeply relevant topic for how the intersections of culture, politics, and technology policy will continue to unfold into the 21st Century.
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