Aviral and Ali’s Presentation and Abstract


Triggerhappy Commentary: The Reactionary Nature of Social Media and the Transformation of Societal Response

While fake news, trolls, and millennial culture are the most often blamed side effects of social media, the reactionary nature of the medium has gone overlooked as the main culprit for a larger societal problem.  The immediate and instantaneous qualities of social media, for better and for worse, allow for information (regardless of accuracy) to travel around the world like no other time in history.  But the reaction to said information is also travelling at the speed of your bandwidth, with real time responses fueling social movements, collective judgment, and increasing polarization in all aspects of life.  What is missing from the equation is simple: thoughtfulness.  Data needs to be processed before it can be useful, and current events must do the same in our collective conscience.  Instantaneous reactions spell doom for actual thought pieces, and instead social media news feeds are flooded with half baked ideas written by anyone and everyone with access to the internet.  The oversaturation of commentary has created a culture of fatigue.  For this project we will be exploring how the reactionary nature of social media is causing a shift in human behavior, or in fact creating new behaviors in the growing number of participants in online discourse.  We will examine a specific event on social media through its instant reaction, the impact of the reaction on those involved in the conversation, and the sentiment several weeks later.  For example, the recent Executive Order banning immigration from seven Muslim majority nations has inspired a large and polarized reaction from various sects of society.  While the sentiment around the order itself may not change much from the immediate aftermath to the present, false information (whether intentional or not) spread about the details of the order and the number of people affected at airports quickly disseminated across social media and amplified the intensity of people’s reactions.  The consequences of social media in this situation are thereby more difficult to understand.  Did the spread of this information cause protestors and those sympathetic to give money or time to the cause?  Did it mask Islamophobic currents within the far right to those moderates unable to see the ban as targeting Muslims?  What is the responsibility of the platform (Twitter, Facebook, etc.) in managing these unintended consequences?  We will mine the data around several hashtags during different times, allowing the chain of reaction to guide our broader research question about social media’s impact on behavior.  Through a visualization of the social media data we collect, we hope to show the class an alternative and more nuanced approach to the study of social media while avoiding the traps of becoming a millennial-bashing luddite or tech industry sycophant whose bubble never burst.


Notes for INT200: Algorithms and Culture on 2/16/2017

Presentation by Ryan Leach

Article “Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue” by Anindya Ghose, Panagiotis Ipeirotis and Beibei Li

-Critical approach to impact of consumer behavior on the search engine
-Looking at hotel search websites
-Study illustrates how the internet can be structured to encourage certain user pathways— particularly consumer pathways
-Parallel with the structure of cities— Psychogeography which maps the relations between late capitalism and structure of urban areas
-Derive— “high theory parkour”— a digital derive? Applying this idea to internet structures, pathways a user can take through hyperlinks,
-Alternative models:
-Neutral Vessel model
-Actor-Network model
-Technodeterminist model— Kittler— the category of human is produced by technologies
-Technologies aren’t all powerful
-In the article humans aren’t really mentioned, the subject is reduced to a series of clicks and purchases
-Fragmentation of human subjectivity
-How might the algorithms themselves play a role in their application to increase consumption and profit?

Week 2: Nuance vs. Pragmatism

This week’s readings give us a great jumping off point for why interdisciplinary classes like this are necessary and should be embraced by academia. In my mind, the essays we read boil down to cross disciplinary communication and issues of close and distant approaches, or micro vs. macro, or nuanced vs. pragmatic if you prefer.

Dr. El Abbadi’s piece was great for a humanist like me to get insight into how a computer scientist views data or a problem, particularly in terms of wanting the most efficient (and helpful to the user) solution, and not just merely a solution. This is a problem that I think occurs in much of the humanities, where we get lost in high-minded concepts that alienate our work from others, be they in a different discipline or completely outside of academia, or even from the very classroom in which we are trying to transmit the information over to the undergraduate student population. We’re good talkers, and can certainly elucidate a topic or bring up important issues, but sometimes the talk leads to very little impact for the “user” or student for that matter.

But both Manovich and the Striphas provide a glimpse into a more complicated approach that marries both the pragmatic and efficient computational methods with the critical humanist approach. Rather than battle with each other like it’s the good ol’ days of academics, building our reputation off of esoteric assaults at other over-educated intellectuals in our privileged bubble, underlying both pieces is that collaboration is the key. Tracing the history of data like Striphas and Roberston/Travaglia provides vital insight into the consequences of data analytics, and how we might learn from past mistakes in order to build a more complicated portrait from any given dataset.

I’ve read the Robertson/Travaglia and Manovich pieces previously, and there is certainly more to say about both (if I’m going to get all critical humanist), but again the most important takeaways from the readings this week were collaboration and complexity. It’s why Manovich proposes the “wide” approach to data, and why he stresses the overlap of so many different projects.

Whether you call them Digital Humanities, Cultural Analytics, or Social Computing, there will be people dissecting your methods to shreds. There will be attacks and superficial dismissals and even blind support of these new paths forward, sometimes labeling them as trends or money-grabs. In my own program, whenever I mention the Digital Humanities I get one of two reactions: (1) utter contempt and indignant comments about distant reading, without really understanding the idea, or (2) “oh that’s really big right now, it’ll help you get a job.” Maybe it will (crossing my fingers), maybe it will help me a get a big fat grant like Manovich, but more importantly, as it relates to my research interests as well, I hope this interdisciplinary approach brings positive gains for everyone— people in a variety of disciplines in academia and (especially) those without the privilege to be involved in these discussions.