Parasitic Platforms and the Crisis in News

The boarded-up building of the McKeesport Daily News near Philadelphia

The viability of news has been in rapid decline since the mid-2000s. This post presents a critical analysis of how news publishers themselves helped precipitate the crisis by enthusiastically adopting Big Tech platform technologies and audience-building strategies. I show how search and social media platforms disrupted publishers’ relationships with audiences and advertisers by appropriating control over news distribution and revenue. I use Anthony Giddens’ structuration theory and Bruno Latour’s actor-network theory to explore the restructuring of the news industry by the sociotechnical practices and surveillance economics of today’s dominating platforms. Leveraging Michel Serres’ discourse on social parasitism, I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems using historical, quantitative, and qualitative methods, and to identify where news publishers still have agency to begin resolving the crisis. And I suggest the urgency of this research framework as publishers rapidly adopt new AI technologies.

Annotation – A Bundle of Open Source Resources for Social Media Data Mining and Analysis

We've reached the final annotation in our series on "Social Media Data Collection, Processing, and Use in Research, Marketing, and Political Communication." Toward the end of the project my research drifted from traditional academic sources to investigative journalism. We now veer further off-track into blog posts and GitHub repos. Some videos and a course syllabus on Data Science for Social Systems. Tools, documentation, and related sources that don't fit neatly into any particular box. This isn't so much an annotation as a grab bag of annotated links. I apologize in advance.

Annotation – Technology Firms Shape Political Communication: The Work of Microsoft, Facebook, Twitter, and Google With Campaigns During the 2016 U.S. Presidential Cycle

If you ran digital strategy for a presidential campaign, and Facebook came knocking on your door and said "We want to help you win this election," would you turn them down? There's nothing like a little help from the mothership.

Annotation: A 61-Million-Person Experiment in Social Influence and Political Mobilization

Finally, a break from annotating technical stuff. Don't get me wrong I like it but...here's where it hits the road: Changes in voting behavior. Here we have a study published in 2012 in Nature on a randomized controlled study of changes in users voting behavior after seeing different versions of messages on Facebook. You want to read this one.

Annotation – Content Marketing Through Data Mining on Facebook Social Network

So far I've been looking at how researchers analyze social media networks, and perform tasks like opinion or sentiment analysis to understand how people feel and think about various subjects and entities. With this annotation we're looking at a thing called content marketing, where influential users of a social network are first identified, then used to spread messages to their network of influence. Even in a huge network, a little bit of leverage in the right places can move products, and perhaps elections.

Annotation: Mining the Social Web

Chart of Connections among the Twitter users

While mining the Information Science Virtual Library for academic papers on "social media" and "data mining," I came across Matthew Russell's O'Reilly book Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More. The 2nd edition was published in October 2013, with a 3rd edition scheduled for publication next month. Because the book covers the specific techniques I'm after concerning data mining and analysis of social media, I decided to pull the trigger and buy the book right now.

The book is basically a tutorial on data mining social media sites using Python. Alas all the source code it references is in Python 2.7 and I've been working with version 3.6, but that's fine. It also covers using IPython Notebooks and even begins with a guide to setting things up on a virtual server. I'll probably wait to actually do that until I see what's new in the 3rd edition. But the book definitely makes the final cut for my annotated bibliography. With that as a given, I thought it would be useful to get started with the first annotation.