Tempo of Social Media: Twitter as a Case Study

One of our original project goals was to “vocalize an ephemeral concept and thus shed light on the suspected shallowness in our social culture”. We hoped to display this through the comparison of sets of hashtags–one associated with pop culture, and light-hearted expression (#yolo, #firstworldproblems), and one associated with heavier, more political topics (#syria, #worldhunger). At a base level, we assumed that if we vocalized each hashtag with a recognizable tone, the abundance of the “silly” hashtags would be more obvious. We thought simply the abundance of conversation around these hashtags could prove an inherit shallowness within the twitter medium.

This ultimately didn’t allow for any solid conclusions for many reasons: #yolo and #firstworldproblems have solidified themselves as common themes on twitter and are used more as hashtags rather than #syria and #worldhunger. Ideally, we would have made a much larger data set–allowing for these inconsistencies among topics.

Also, we suspected any tweet which references something like #firstworldproblems is likely bordering on narcassistic and void of any real content–but expression is unpredictable, and #yolo or #firstworldproblems could be used to highlight a cultural irony or dissonance in unexpected ways. So even if these seemingly irrelevant hashtags are used more, proving a shallowness in culture just based on tempo is problematic.

#worldhunger shallow tweet

Even tweets that utilize seemingly serious hashtags can stray into the shallow end of spectrum of expression.

Screen Shot 2012-12-10 at 1.36.35 PM

The same phenomena can easily be reversed– using #yolo in a politically charged tweet.

While the content aspect of our experiment proved weak, the issue of tempo rose to the forefront of discussion. Twitter’s 140 character limit and stream style display both encourage a higher frequency of sharing. In order to stay salient to followers, users need to tweet consistently to stay near the top of a stream. Every tweet gets the same base impressions–no matter the content. A tweet about breakfast goes out to the same followers as a tweet sharing more meaty content. However, on many other sharing websites, impressions are directly related to engagement–better content gets engaged with more, and audience impressions increase. Salience relies on relevant, quality content (in theory, of course).

The magnitude of this increased tempo is difficult to see on twitter’s automatically updating stream. The page never ends and design differences make it difficult to visualize this comparison with other, slower moving platforms. However, our sound experiment allows for a transfer of data into noise–a medium that allows us to distinguish tempo very easily.

The structure and user interface of a platform set the foundation for user interaction. Twitter’s case is made slightly more complicated due to it’s open API. With user generated content abstracting Twitter’s basic functions, new forms of expression are emerging within the twitter community. Hashtags, @replies are all results of user demands. While more complexities are allowed, the goal of the interface is still simplicity–most noticeable in its simple timeline stream.

As Stephensen points out in his analysis of GUI systems vs command line systems, the display of information is vital to user interaction.

The desire to have one’s interactions with complex technologies simplified through the interface, and to surround yourself with virtual tchotchkes and lawn ornaments, is natural and pervasive–presumably a reaction against the complexity and formidable abstraction of the computer world.- Stephensen

At a graphical level, Twitter’s interface doesn’t seem to vary too heavily from that of Tumblr, or even Facebook. But when you translate the data from graphical to aural, a new idea of tempo emerges. Twitter’s use of hashtags allow us to track “group think” and popular themes among its users. The tempo of interaction among different topics is difficult to distinguish graphically. Loading times are abstracted and grouped together, making it difficult to feel tempo in real time. However, when we translate this data into sound–the tempo becomes increasingly clear. Different expressions of the same data can allow for entirely different interpretations. While you lose any idea of content within each tweet, simply being aware of the tempo of discussion can be telling to the topic at hand.

The ECHO Theory

In the modern age, we are constantly inundated with a plethora of information. When accosted with this constant stream of consciousness, it is necessary to filter out and focus on only the topics that appeal to you. However, this human defense mechanism has its downsides, as often times, more “important” issues will be overshadowed by shallower, yet viscerally satisfying topics. For example, a quick look at the Yahoo front page will reveal a scroll bar of “top stories” that the editors feel are most likely to be read. Near the top of the list are banal tales of Celebrity weddings and shallow fashion advice. In contrast, more “important” stories, like political upheavals, are thrown to the wayside.

According to DailyBeatsMotion.com, these are the top trending topics from September 2012:

Here are the top 10 Twitter Trends for September 2012, ranked by the number of days on which they trended in at least one major metro area in the US.

  1. #nowplaying: (29 days) –
  2. Bonanza: (26 days)
  3. Cali: (26 days)
  4. #job: (26 days)
  5. #oomf:  (24 days)
  6. #blessed: (23 days)
  7. Facebook: (22 days)
  8. Calm: (22 days)
  9. Netflix: (21 days)
  10. #news: (21 days)

The goal of this project is to expose this unfortunate truth, which is accomplished by counting the number of Tweets under the hashtags: #firstworldproblems #humblebrag #yolo; allowing us a glimpse into the true concerns of the modern age. These concepts are going to be represented by a droning sound, which represents the numbing drone of the twitter feed. To draw attention to this, we will also monitor the hashtags of a few, more “important”, tweets: like human rights and the like. When our program detects these tweets, instead of a comfortable droning noise, a bit more dissonant, looping sound will play – hopefully jarring the listener.

These sounds will be playing all at once, which will cause a hellish cacophony – but this is the point.  We contribute to the noise of media with opinions and discussion every day through our tweets, but how many of them are making a difference?  By hacking the Twitter system and hashtags and making more of a clear dichotomy between “first world problems” and “real world problems,” and converting them into sound to make a little more sense of the scale of this problem, perhaps people will take pause and really consider how they perceive the world.

This is a social experiment and an exercise in observing the digital environment that has become an integral part of our lives. The Echo Theory is our attempt to drag the gems of decency from the shallow black lake of escapism. While, in and of itself, E.C.H.O cannot change the world, the resulting insight may bring our society to a higher state of digital and social awareness.

Finding More Elegant Solutions for the ECHO Python Script

Goal of Python Code:

  • To scan twitter for various hashtags, send a signal to an Arduino which then signals an assigned audio input once someone tweets with a hashtag

The twitter API utilizes JSON but we chose to write our code in Python, so we needed a “translator” so we could interface with Twitter easily. Tweepy allows this translation, so all you need to do is download, authenticate and import the library.

Problems and Solutions:

  • One of the earliest problems we ran into was integrating Tweepy into our code because we needed to renew our authentication and we were using an outdated instruction manual for our version of Tweepy.

The basic_auth.py on the Tweepy github page was different than the one that downloads with the library, so it tooks us a while to realize this and then update our methods.

  • Simplifying the code so that it reads all of twitter and only recognizes hashtags, rather than “printing” all of the data associated with each tweet.

When we were first experimenting, our code basically threw up all of the data associated with each hashtag: the user info, tweet itself, and time stamp. If this was something we would distribute to the public, this might be something that needs to be changed, but we decided it wasn’t exactly a “problem”, but more of a design suggestion for the future.

Python Twitter Script Throwup

Python Twitter Script Throw Up

  • Finding a solution to enable the code to scan for multiple hashtags at once and then send signals to a specific serial port on the Arduino, rather than any hashtag triggering any serial port.
    • Example: #yolo needs to trigger serial port 2, while #lybia needs to trigger serial port 4, etc

In order to run several processes at once, Belinda’s original idea was to incorporate “subprocesses”. There were a few options for subprocess libraries that allowed us to “spawn new processes, connect to their input/output/error pipes, and obtain their return codes. Even though this would have been a solution, Andy informed us that creating a list, or array, would be a more elegant solution.

This was our final solution for our python script:

Python Script for ECHO

Python Script for ECHO