The New Normal: Tulsi2020
Have you ever fallen into a spiraling lake of data? That is how it feels trying to understand the debate between Hillary Rodham Clinton and Tulsi Gabbard over the “Russia” question. In the last post, we alerted readers to the heavy presence of #MAGA movement identities (user_descriptions) in the “Tulsi OR Gabbard” Twitter data which is accumulating at a remarkable rate.
Today, while we are pouring over the translated Russian and other non-English user bios, I wanted to share something new emerging from our inductive research methods.
Trump Trolls Love to Post about Tulsi Gabbard
There are a lot of things happening in this data. We are not finding one trend, a single pattern, or a clear playbook. It’s more like walking around an Iowa hog lot; no matter where you go, everything stinks. For example, we are seeing a lot of interesting activity in the accounts that are 1-day old.
The Top 20 Most Viral Tweets about Tulsi in Our Collection
Based on more than 750,000 Tweets collected from the Twitter Search API over the last three or so days, these are the most viral retweets are shown here.
That is a bunch of hot topics for a 2% candidate. So who are the most active screen_names? What are the top 10 user descriptions?
As in many of these posts, we invite the reader to reflect on these data. You can visually inspect samples from the users and hashtags cited here using the Advanced Search feature provided by Twitter. In a complex and ambiguous space, the value of manual inspection cannot be over-stated.
The accumulation of strategies and accidents, humans and machines, truths and distortions, all in one semi-regulated information system is a fact we live with. In every open democratic debate, with or without Twitter, there will be authentic and staged performances. All the world is a stage. It is worth noting, in this context, the special platform affordances of Twitter, including automated retweeting, post scheduling, private networks, and so on.
Sometimes it is precisely the lack of affordance that is notable as well. For example, the landscape of very limited authentication of identity. I am a PhD-holding recovering academic, a former 7-year customer of Gnip & Twitter, with many public presentations posted on legitimate third party web sites, yet I cannot get a blue Twitter verified check mark next to my name. Why am I unverifiable? I was a founding board member and Treasurer of a 501(c)(6) dedicated to the long term preservation of the social data ecosystem.
By enabling extreme forms of inauthentic communication, networks like Twitter seek commoditize the transmission of misleading information. There are ad tech connections creating incentives to allow this behavior.