Dosage Preferences

Bianca Datta and I worked on the current debate over ‘cognitive enhancers.’ This topic offered a wide range of possible actions for those interested because the debate is both so broad and becoming more visible.

To navigate the space of options, we decided to think about how we could explore our capacities as humans with limited time and resources.

We decided to create an interactive bar chart that allowed a person to signal their general level of interest in pursuing any one type of contribution to the issue. This would (or is supposed to) in turn reshuffle the display of options available to be more in line with the reader’s interest and capacity.

The act of exploring the options not only means a little more thought into how one can ‘help’ but also serves as a survey tool for media outlets.

In the ideal situation, this would be a fully responsive page where information did actually shuffle according to specified preferences but as it stands, it currently lacks that last capability. It is easy enough, however, to gut the information and put in new information so it could possibly serve as a tool in future after some extra work and polish.

With regards to the type of options we left available for the reader, Bianca and I felt that three broad actions governed most abilities to contribute for any given reader. A reader could either spread the information to raise awareness and attention, participate in discussions which could lead to greater action, or donate to an organization that would act in their interests.

Here are some screenshots:

Screen Shot 2015-04-22 at 3.14.16 AM

(Drag and release to increase or decrease dosage of action)

Screen Shot 2015-04-22 at 3.14.27 AM

(Read about actionables from what was selected as most important to least important – in progress functionality)

 

Check is out at: http://um-viz.media.mit.edu/fonAction/index.html (slide bars for fun, then click ‘okay’ to see information)

 

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Patrick – Patty – Paddy

For our assignment, Bianca Datta and I created a simple web page that demonstrates a possible tool for journalists in understanding a lot of social media data. We took the idea of creating a story via social media and attempted to automate it a bit more.

On the left is a feed of tweets based on the topic ‘St. Patrick’s Day’. The search terms we used were ‘StPatty’, ‘StPaddy’, and ‘StPatrick’ . Once we had these tweets in our database, we processed them to get topics using LDA via a Node.js package. The top ranking topics were then displaying on the right side of the webpage using a very simple web graphic. Based on the term’s use, the topic would be allotted a larger height for more use and smaller height for less use. The left column shows common topic terms in tweets with the word ‘Patty’. The middle column shows topic terms for ‘Paddy’ and the right column shows topic terms for ‘Patrick’.

This is a screenshot of the page:

Screen Shot 2015-03-18 at 4.34.12 AM

 

We also gathered content from Instagram, Vine, and Flickr and processed each of those to get topic rankings.

link : http://web.media.mit.edu/~vdiep/vdiepbdatta/indexy.html

 

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Vivian’s Media Diary

To keep track of my media content, I used two methods. The first, Rescue Time, to provide a complete look at my online activity and desktop work. That way, I can compare the time I spend reading the news versus getting work done. The second method was much more on the experimental side. I manually recorded what I classified to be ‘news’ as I read it. Although this week isn’t representative of other, more normal weeks, the data probably fits and overall trend in my media habits.

Vivian's RT ShotVivian's Categories

Above is a short summary of my ‘productivity’. Reading the news seems to occupy a fair share of my time, even rivaling ‘entertainment’ which encompasses youtube and spotify (both of which are often used to provide better environments for concentration).

What’s the most interesting part of exploring Rescue Time’s tracking is that I saw very obvious patterns of activity for myself. I know that I tend to tab over to Facebook, Reddit, blogs, and news sites for brief periods throughout the day but seeing how much that adds up is interesting. I’m not sue I care enough to curb it yet – more data needed on how much it helps me de-stress or just plain distracts. It’s also quite consistent throughout the week although none of the ‘tabbing-over’ is planned in any fashion.

Screen Shot 2015-02-18 at 12.01.26 AM

I’ve also learned that I although I feel like I spend the most time on Reddit, I’m actually on Facebook nearly twice as much. Perhaps because the content feels less interesting on Facebook? Reddit’s use of imgur as the image hosting site of choice must also be taken into account as I’m on imgur about 40 minutes per day, rounding my total true Redditing time to 3 H 10 M. Shame on me. Hmmm, however, I know for a fact that I’m on my phone Redditing often while waiting for the bus/train/unicorn so maybe not so much shame?

Given that I’m apparently on email about 8 hours a day (I do actually nearly compulsively check email), it’s not a surprise that a number of my news sources are actually through email. (See Media Diary link at bottom).

With the information I’ve collected manually, I’ve created a visualization to better understand how my media affects me and where I’m actually getting it (email, Facebook, etc.). Although the visualization needs a few more hours of work, it did help me realize that I’ve been getting my news a lot more through Facebook than I expected. I can further explore my visualization and see that I’m often on BostInno.com. This is a pretty decent highlight of Facebook’s role in news in general. Because Facebook is so good at the localized, personalized stuff, hyper-local news service might be a large part of their news pool. At least, it seems like that for me.

Within my visualization, I also included sentiment (using the ‘sentiment’ node package to compute sentiment scores), comment counts, tweet counts, and facebook share counts as a way to see what type of material tends to be shared (negative sentiment versus positive sentiment). Although these visualizations need some more work to get them to that stage of descriptiveness, they do provide some insight into what I prefer to read and how often I am made to feel ‘bad’ by what news I read online.

Obviously, I need to look at more world news  – Boston-based news occupies much of my news reading. I need to reclassify some activities on Rescue Time. I also need to limit my Facebook tab-overs – maybe switch it to BostInno since I clearly go to Facebook for their news most of the time anyway. (Link to website with visualizations coming very soon) EDIT: link is here!: https://media-diary.azurewebsites.net/ Click on buttons (preferably going from left to right because there are bugs . . .) to navigate to different views. A short description exists next to the buttons to somewhat explain the graph. Click on bars in the graph to see additional info.

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