There’s no immunity. Computational propaganda is infecting every significant online socio-political conversation. Algorithms are directly influencing the content populating social feeds, and people with ill-intentions are using software automation tools to spread digital pathogens. There’s no escaping that the number of likes, re-tweets, shares, and views are the foundations of our “filter bubbles.”
These key social indicators are easily manipulated. They’re like micro-events and discerning human from non-human engagement is nearly impossible to detect. Detecting SocialBots at work and seeing concentrated efforts to influence public opinion and perceptions leaves us wondering how civil discourse will survive this spreading digital black death.
The hashtags #vaccines and #antivax recently have received our attention. We were curious to see how involved SocialBots are in conversations connected with science and public health. It turned out to be overwhelming.
Our goal is to research and document SocialBots and their behaviors. In this case, it’s difficult to look at this issue without getting pulled into the debate, unpacking the rhetoric, being aghast at the volume of misinformation, and the very evident politicization of conversation. That’s an essay for another day.
For this case-study we observed 23 different daily Twitter maps. Each map captures the last 200 tweets and the profiles that tweeted using a the hashtag #Vaccines. Before separating real profiles from the fakes, the first map we reviewed (above) seemingly highlights the divisiveness of this issue. We also noted the volume of tweets from those profiles staking an anti-vaccine position subsequently flow to high profile and politically partisan secondary profiles.
After reviewing the 23 separate maps of the hashtag #Vaccines, we documented 284 profile as SocialBots with one dominant participant emerging above the rest. Day in and day out @LotusOak is at the center of this conversation. This short video of snapshots from 14 days in July highlights herever present voice.
While there’s a person (or possibly people) behind the profile, given @LotusOak’s anonymity (we found no evidence of the real Vera Burnayev), the high volume of tweets, the consistency of message and tweet design, and the pattern of re-tweeting all of her own tweets, and we have noted the consistent re-purposing of content. All of this points to a computational and programmatic endeavor.
While @LotusOak’s voluminous efforts were straightforward to track and document, we also note some equally active amplifiers. In the case below @eTweeetz in a 3 minute window retweeted @LotusOak 11 times.
@draintheswamp pushes out these 11 retweets during a 5 minute window.
Out of the 23 maps we also noted the presence these four profiles re-tweeting @LotusOak on multiple days —
We classify them as SocialBots. As well as noting 284 SocialBot profiles tweeting the hashtag #Vaccine, we also documented every hashtag used in conjunction with it. A total of 609 secondary hashtags were used. Here are the top 30 hashtags.
There’s patterns, behaviors, and volumes of activity pointing to a concerted programmatic effort to dominate and manipulate the conversation connected with #Vaccines. At any given moment we can see the profiles displaying Bot-like behaviors, as this re-tweet map from @LotusOak highlights.
In retrospect, none of this is shocking. In our course of researching the subject, this work by Renee DiResta and Gilad Lotan in Wired (06. 08 .2015) — Anti-Vaxxers Are Using Twitter to Manipulate a Vaccine Bill, was brought to our attention. This image below illustrates how they visualized the issue at scale in 2015.
Unlike a pathogen, the spreading of this misinformation will not be an unseen killer. It will be there in every re-tweet and like for anyone to see (if they choose). Seeing a distinct political agenda being advanced ahead of public health is disconcerting. It also leaves us wondering where Ovid would land on the issue.
FIND OUT WHY SOCIAL BOTS ARE SO DANGEROUS.
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