Presentation: Twitterbots

 

twitterbot presentation pdf

Project Twitterbot Abstract: Leavell/Mackey

When Twitter released its IPO several years ago, much information about the company became public as well. Most relevant to this discussion was the surprising revelation that much of Twitter is run by automated accounts, aka “twitterbots”. In 2014, Twitter admitted that as many as 23 million, or 8.5%, of its users were fake.  For our project, we are curious about the prevalence and influence of automated accounts, those exhibiting behavior outside of human-like patterns of content generation, in political events and narratives with a large presence on Twitter.

We propose using R-Shief to sample Twitter activity during a popular political event, such as the Women’s March last month in Washington DC and around the country, or possibly an upcoming event, such as Donald Trump’s Supreme Court nominee process. With the help of pre-existing machine learning-based classification software, BotOrNot , we can make reasonably confident guesses as to whether a Tweet sent a specific Twitter handle is an automated account, one of the so called “twitter bots”; while the identification of twitterbots can be done almost as efficiently through sampling, we are also interested in more nuanced aspects of their behavior, creating a need to ‘fish’ for large number of twitterbots to perform further analysis on. With these automated techniques for twitterbot gathering, we hope to characterize twitterbot activity during political events and present our findings visually, using tools provided by the R-Shief software and others.

While the exact list of characterizations is still being developed, we have targeted a few questions which are both theoretically interesting and realistically observable based on the methods of data collection and analysis we have at our disposal.

Demographics: Do communities on different positions on the political spectrum have significantly different proportions of automated accounts? While it has been shown that the number of automated followers differs for certain political candidates, observing the ecosystems of automated accounts which exist for different political camps would yield insight into the social media activity of these ideological groups.

Thresholds: Is there a threshold of trending activity on a hashtag that triggers twitterbot activity? If so, what is that threshold? R-Shief conveniently segregates collected tweets by the time interval in which they were collected, leading us to wonder if there existed an identifiable threshold of authentic activity at which twitterbots begin to participate heavily? This threshold would be dependent on both the structure of the Twitter social network and specific twitterbot attributes. Additionally, can different thresholds be found for different events?

Impact: Is there any discernable goal of twitterbots on the social media conversation around the given topic (ie, dissemination of fake news, targeting of any specific people on twitter, promoting alternative analyses or politics to the majority of hashtag users, etc.). Although these accounts are widely assumed to be harmful to the user experience, little is known about the intentions of those who develop and deploy these accounts. While some accounts simply bolster the activity of those they follow (magnifying the perception of a public figure or idea), others hijack popular hashtags or intentionally spread misinformation.

Short thoughts week 3

There’s a dual comfort and dismay at the fact of incomputability. Philosophically, it’s humbling to admit that there are simply things that we cannot and may  not ever be able to solve. Bringing in Stephen Hawking and his 10 billion year time frame gives perspective to our humanity and our abilities. I recently bought a telescope and having spent a few weeks looking up at the sky at night between the rain lately, I have to admit it’s given my research some helpful perspective.

It contrasts some with the discussion of Turing’s “On Computable Numbers” paper in this same piece, the Church-Turing thesis. It’s more than a computable question, I think, on whether human brain capacity can be equivalent to a computer and deep neural network computing. I listened to an interesting debate recently between Jaron Lanier and a singularity advocate, whose name I now forget. The idea that the human mind could and will eventually be replicated by a computer to me seems like a bad ending to what had been an otherwise enjoyable sci fi novel. I don’t know that that need be our end point, or that it is even possible. Jacques Ellul wrote about Technique, and the ever growing and ever more integral obsession with results, efficiency, and function, and I think there is a healthy space for critique in this area- what’s possible, what’s not, what is lost, what is outside of our horizon and paradigm.

Then there’s the example of Google and the flu. Here we see that big data can make mistakes, and reach impossible predictions. But, when it comes to other areas, like self driving cars, or areas where artificial general intelligence or artificial super intelligence may step in as part of the algorithmic fabric of the imminent future, big data fails are inevitable and more deadly. All new technologies fail. A failure at a certain level, however, might be difficult to come back from. Interesting discussion of this in Our Final Invention.

 

 

Week 2 :Big Data to a Mediated Culture

We can start with some basic questions inspired by my reading of Robertson and Travaglia. Who wants to know? Who owns the data? Who is being cataloged? To what end? Looking back at the practice of social ordering in Robertson and Travaglia’s piece, we realize that the power and control endemic to this earlier “first information age” is familiar to today. Data is a raw material collected by corporate and government entities at alarming rates. If you follow the main stream news, it seems like intelligence agencies are collecting data in such bulk that it remains unexamined and unorganized.  If this is even true, it can’t be true for long.

The  authors write, “…much of the data collected about human beings by bureaucratic systems has a history not simply of description or even understanding but one of control”. This applies to the mass data collection exposed in recent years by US whistle blowers, but I would add also a means of profit to the discussion. Data is a primary raw material collected and traded by top corporations, funneled as fuel into the refining mechanisms of a mature advertising industry. The old adage about the free lunch is true- our social media activity isn’t actually free, in that our habits, purchases, love lives, and friendships are being mined in order to more expertly sell them back to us. Political and technology theorist Jodi Dean calls this “communicative capitalism”, and understands it to be a qualitatively new phase of capitalism. In this phase, capitalism has adapted in new ways to control workers and the surplus army of nonworkers through communications technology. Furthermore, in a Lacanian twist, our interactions on social media satisfy drive while always thwarting a deeper desire for equality and justice, keeping us in front of the screens instead of in streets.

This leads into further questions about culture, in Manovich and Striphas’s texts. Is it time to just admit that 21st century culture is all online and cataloged? Probably. What does culture even mean outside of technology today? Given that, what does it mean when culture is mediated through corporations and listed according to algorithms to which we have zero access? Is culture a hood that has been welded shut? Both authors do their part to define the word “culture” and its content. Twitter and Instagram are our cultural mediators, but they are also owned by seemingly unstoppable and unknowable corporations.

A part of me still feels resistant to calling this “culture”. I watched an interview recently with filmmaker Abel Ferrara, and, when asked why he’d abandoned his home country to live and work in Italy, he said “There’s no culture where I come from. A bunch of fuck’n lunatics show up 300 years ago, shoot everybody that’s there. Kill every motherfucker that’s there…I’ve never met an Indian in my life… that’s my country. So where’s the culture?” On some days, I agree with him.