Category: Social Networking

The Cult of Sharing

Mike Bulajewski has written an excellent critique of the The Cult of Sharing. He describes the way ideas of community and sharing are being exploited by a new type of cult-like company like Airbnb and Uber. Under the guise of sharing and building community these companies are bypassing employment and labor legislation. What’s worse is that they are painting basic labor rights as the outdated way of doing things.

That’s because they’ve adopted a kind of cultural critique of capitalism. For them, the problem with capitalism is not the system itself, but rather depraved contemporary Western culture, which is greedy, individualistic, selfish and acquisitive, and rewards greedy, corrupt, ill-intentioned individuals. The opponents of the so-called culture of greed see the behavior of Black Friday shoppers and Wall Street bankers as equal manifestations of the same general phenomenon, and perhaps believing that we get the leaders we deserve, conclude that the public’s moral flaws makes them in some way responsible for the greed of Wall Street.

The sharing economy is clearly not the kind of economy where wealth and prosperity is shared between rich and poor. On the contrary, it worsens income inequality and concentrates wealth in the hands of those who need it the least. Progressive advocates are well aware of this, but they also see an upside: these startups teach their workers moral lessons about sharing, community, giving and service with a smile.

I’m not sure this is going to be the problem Bulajewski thinks it will be, but he has me worried. I hope that that shine of sharing will wear off and consumers/sharers will begin to treat this as any other industry. I also think the media will soon start reporting the downside of staying on someone’s couch or getting a ride with someone who isn’t licensed. It’s like the internet, which we all thought was a nice sharing community, until it wasn’t.

Blended Learning Award for GWrit

The Game of Writing (Gwrit) project that I am part of just got support through a University of Alberta Blended Learning Award. See the 2014 Selected Courses. This award is going towards creating a flipped version of Writing 101, a service course that is being scaled up to support large sections by Roger Graves and Heather Graves. With the Blended Learning Award support from the Centre for Teaching and Learning and with Faculty of Arts funding we are redeveloping GWrit to be used in large sections of Writing 101. Here is part of the abstract of the proposal,

Research suggests that by creating a rich online environment for students to connect and interact with instructors and peers they can improve as writers. We are currently building a gamified online writing environment, The Game of Writing (GWrit), for Writing Studies 101 (WRS 101) that can support student writers and alumni. WRS 101 is a high demand service course required for many degree programs across the University. We are creating a large class version that blends face-to-face with gamification strategies. In GWrit students will choose and work on assignments or quests that are part of the course. Their progress on these assignments or quests will be shared with peers and instructional staff; in this way all students can see who is working on the same quests, and they can ask for help or advice from them. Informal assessment will be available online from peers in the class; from paid peer tutors; from GTAs; and from alumni. This represents a significant expansion of the informal assessment available in traditional face-to-face courses, where peers and sometimes the instructor give informal feedback. We also intend to invite alumni to post assignments/quests that come from a workplace writing context. Students who complete WRS 101 will continue to have access to GWrit throughout their undergraduate careers and as alumni.

GWrit started as a prototype developed with support from GRAND. The original idea was an open writing environment where folks could challenge each other to compete at writing and where you could get analytics on your writing (number of words written, tasks completed, and visualizations like word clouds.) This research prototype is now being completely redeveloped by the Arts Resource Centre as a learning tool that can be used by students of our courses. We are adding commenting features so that students (and later alumni) can provide writing guidance in a structured fashion.

Early Selfie (1865)

There was a discussion on Humanist about selfies and Emma Clarke on behalf of the Letters 1916 Project team posted a link to this holding of the National Library of Ireland, Augusta Caroline Dillon and Luke Gerald Dillon with camera on tripod reflected in a large mirror.

The two were apparently skilled amateur photographers who experimented with photographs like this. If one goes beyond photographs to paintings, I wonder if Las Meninas would qualify as a selfie.

How Netflix Reverse Engineered Hollywood

Alexis C. Madrigal has a fine article in The Atlantic on How Netflix Reverse Engineered Hollywood (Jan. 2, 2014). The article moves from an interesting problem about Netflix’s micro-genres, to text analysis of results of a scrape, to reverse engineering the Netflix algorithm, to creating a genre generator (at the top of the article) and then to an interview with the Netflix VP of Product who was responsible for the tagging system. It is a lovely example of thinking through something and using technology when needed. The text analysis isn’t the point, it is a tool to use in understanding the 76,897 micro-genres uncovered. (Think about it … Netflix has over 70,000 genres of movies and TV shows, some with no actual movies or shows as examples of the micro-genre.)

Madrigal goes on to talk about the procedure Netflix uses to create genres and use them in recommending shows. It turns out to be a combination of content analysis (actual humans watching a movie/show and ranking it in various ways) and automatic methods that combine tags. This combination of human and machine methods is also the process Madrigal describes for his own pursuit of Netflix genres. It is another sense of humanities computing – those procedures that involve both human and algorithmic interventions.

The post ends with an anomaly that illustrates the hybridity of procedure. It turns out the most named actor is Raymond Burr of Perry Mason. Netflix has a larger number of altgenres with Raymond Burr than anyone else. Why would he rank so high in micro-genres? Madrigal tries a theory as to why this is that is refuted by the VP Yellin, but Yellin can’t explain the anomaly either. As Madrigal points out, in Perry Mason shows the mystery is always resolved by the end, but in the case of the mystery of Raymond Burr in genre, there is no revealing bit of evidence that helps us understand how he rose in the ranks.

On the other hand, no one — not even Yellin — is quite sure why there are so many altgenres that feature Raymond Burr and Barbara Hale. It’s inexplicable with human logic. It’s just something that happened.

I tried on a bunch of different names for the Perry Mason thing: ghost, gremlin, not-quite-a-bug. What do you call the something-in-the-code-and-data which led to the existence of these microgenres?

The vexing, remarkable conclusion is that when companies combine human intelligence and machine intelligence, some things happen that we cannot understand.

“Let me get philosophical for a minute. In a human world, life is made interesting by serendipity,” Yellin told me. “The more complexity you add to a machine world, you’re adding serendipity that you couldn’t imagine. Perry Mason is going to happen. These ghosts in the machine are always going to be a by-product of the complexity. And sometimes we call it a bug and sometimes we call it a feature.”

Perhaps this serendipity is what is original in the hybrid procedures involving human practices and algorithms? For some these anomalies are the false positives that disrupt big data’s certainty, for others they are the other insight that emerges from the mixing of human and computer processes. As Madrigal concludes:

Perry Mason episodes were famous for the reveal, the pivotal moment in a trial when Mason would reveal the crucial piece of evidence that makes it all makes sense and wins the day.

Now, reality gets coded into data for the machines, and then decoded back into descriptions for humans. Along the way, humans ability to understand what’s happening gets thinned out. When we go looking for answers and causes, we rarely find that aha! evidence or have the Perry Mason moment. Because it all doesn’t actually make sense.

Netflix may have solved the mystery of what to watch next, but that generated its own smaller mysteries.

And sometimes we call that a bug and sometimes we call it a feature.

Wikileaks – The Spy files

On December 1st, 2011 Wikileaks began releasing The Spy files, a collection of documents from the intelligence contractors. These documents include presentations, brochures, catalogs, manuals and so on. There are hundreds of companies selling tools to anyone (country/telecom) who wants to spy on email, messaging and phones. I find fascinating what they should about the types of tools available to monitor communications, especially the interfaces they have designed for operatives. Here are some slides from a presentation by Glimmerglass Networks (click to download entire PDF).

Continue reading Wikileaks – The Spy files

Rap Game Riff Raff Textual Analysis

Tyler Trkowski has written a Feature for NOISEY (Music by Vice) on Rap Game Riff Raff Textual Analysis. It is a neat example of text analysis outside the academy. He used Voyant and Many Eyes to analyze Riff Raff’s lyrical canon. (Riff Raff, or Horst Christian Simco, is an eccentric rapper.) What is neat is that they embedded a Voyant word cloud right into their essay along with Word Trees from Many Eyes. Riff Raff apparently “might” like “diamonds” and “versace”.

HedgeChatter – Social Media Stock Sentiment Analysis Dashboard

HedgeChatter – Social Media Stock Sentiment Analysis Dashboard is a site that analyzes social media chatter about stocks and then lets you see how a stock is doing. In the picture above you can see the dashboard for Apple (APPL). Rolling over it you can see what people are saying over time – what the “Social Sentiment” is for the stock. I’m assuming with an account one can keep a portfolio and perhaps get alerts when the sentiment drops.

To do this they must have some sort of text analysis running that gives them the sentiment.

Building Inspector by NYPL Labs

The New York Public Library has another cool digital project called the Building Inspector. They are crowdsourcing the training and correction of a building recognition tool that is combing through old maps. You see a portion of a map with red dots outlining a building and you click “Yes” (if the outline is correct), “No” (if it is wrong), and “Fix” (if it is close, but needs to be fixed.)

They also have a neat subtitle to the project, “Kill Time. Make History.”

CBC.ca alberta@noon Monday June 10, 2013

Last week I was interviewed by Judy Aldous on the CBC programme alberta@noon Monday June 10, 2013. We took calls about social media. I was intrigued by the range of reactions from “I don’t need anything other than messaging” to “I use it all the time for my company.” One point I was trying to make is that we all have to now manage our social media presence. There are too many venues to be present in all of them and, as my colleague Julie Rak points out, we are now all celebrities in the sense that we have to worry about how we appear in media. That means we need to educate ourselves to some degree and experiment with developing a voice.