Self-Generating Text & Bots

          

          The topic of self-generating text and bots can be controversial to say the least. Do these programs possess or produce emotions for their audiences? Who is responsible for the work these programs create? Is it the human being who wrote the code, or the computer itself? This discussion is solely based on one’s own personal opinion and beliefs, but Digital Dangers came up with a consensus as to what or who gets the credit for the productions made. To start with, yes, the individual who writes the code for self-generating programs has ownership of what will be produced. However, if the coder inputs materials that were previously written, such as lyrics, the author of the lyrics should also have some rights in the product because without their construction of the lyrics, the coder would not have had any material to write code for.
           A great example of this is a simplified program that Sonny Rae Tempest took from Nick Montfort, called “Camel Tail”. This piece is very fascinating because it takes iconic lyrics from the very well-known band, Metallica, and generates a random four line poem. We believe that Metallica has some right to this program because it uses the lyrics in which they wrote. However, we think Tempest would have the right to the text that is generated through the program itself. This is because the lyrics different from the original songs themselves because of the fact that only short snippets of lyrics are used in the order they are meant to be in. Some of what the text generated creates makes for the viewer, however, on some occasions the text that is generated is complete nonsense. This program would have never been created without Tempest or Metallica. That being said, we believe that both parties should have some sort of rights to the finished product when it gets down to the fine print.  

 Next, we switch gears to talk about bots. Unlike self-generating text, bots are more associated with social media. Twitter is a huge platform for bots to call home and reach many viewers. Some examples of Twitter bots are @NSA_PRISMbot, @congress-edits, and @TwoHeadlines. The NRA bot posts headlines that are fiction but believable responses from the NRA. While most of them are similar to the type of press releases the NRA puts out, they are still fictional. The information that this bot puts out are real numbers, locations, victim type and firearm usage. However, the bot jumbles all of the data up to get fictional posts. In contract, The Congress Edits Twitter page consists of strait facts. This bot tweets whenever anonymous edits are made to any Wikipedia page from an IP address associated with the U.S. Congress. This bot calls attention to possible fabrications that these articles can contain and is able to show the specific changes that are made. Two Headlines is more of a playful page. This bot takes two separate headlines from Google News, and pieces them together to reflect the news but not specific headlines. 
Another interesting example of a Twitter bot is @pentametron created by Ranjit Bhatnagar. Unlike the other examples of Twitter bots, @pentametron does not generate its own text. Instead, this bot filters though 10% of the Twitter stream while simultaneously also using an online dictionary service to find tweets to retweet that not only rhyme, but also follow the iambic pentameter, the preferred meter for most of Shakespeare's work. Iambic pentameter can also be described as being similar to a hear beat.
         The Twitter bot, @pentametron, is not meant to form coherent thoughts when filtering through its criteria for retweets. Instead, it aims to create a humorous juxtaposition of unrelated thoughts of real Twitter users paired together to form a never ending poem. This specific Twitter bot is different from the rest. This is because it questions whether the person who created the code for the bot, or the individuals who's tweets were selected are the author(s) for what is put on the page.

Whether or not any of the self-generating texts or bots produce emotion in their audiences depends on each individual viewer or group of people. Some bots that are considered protest bots, like the @NRA_Tally, can really create an uproar amongst certain audiences depending on what is exposes or entices people to dig into. The same can be said with the @congress-edits by showing what changes are being made to Wikipedia pages in real time. This means that people who are dedicated to this bot page can greater expose the edits and share the information with others. On the other hand, some would say that if a human is not creating the content of the piece, than there can be no emotion received by the reader. This is because they believe that if a computer is randomly selecting things, than there is no real meaning to any of it.