Twitter: what are you feeling?
What if our words had no hidden meaning? What if we didn’t play the mind games that we play in deconstructing and (over-)analyzing every sentence or word or gesture and we would have to look no further than the dictionary to know the exact meaning of a word? Can you imagine there not being any metaphors, euphemisms or sarcasm involved in our conversations or even our random utterances?
As Bram and Jidi have already reported in their blog posts, when it comes to Twitter research, there is an abundance of beautifully crafted charts and interactive graphs that can help us make sense of what is happening on Twitter, in terms of trends, emerging topics, user activity etc.We can get a fairly good idea of what we tweet about by using these tools, but can we get an idea of what we tweet about the things we tweet about?
J.W. Rettberg argues that the digital data presented in this form show digital portraits that are indicative of our collective coming of age where we as a culture are discovering that we have voices online and can express ourselves. But are these portraits accurate, as long as they are constricted by quantitative variables and templates? Discourse analysis of our tweets only goes so far as taking into account the use of emoticons or word associations, but sarcasm or the use of metaphors are even difficult to catch by human subjects involved in computer-mediated communication (CMC), so a semantic map of emotions and attitudes might be difficult to convey through use of algorithms.
To illustrate this point, I would like to make use of the methodology used by Walther and D’Addario in their study about the impact of emoticons in interpreting CMC, trying to prove that mixed messages—positive verbal messages with a negative emoticon or vice versa—may not be readily interpretable. They asked human subjects to choose the emotion behind a message , based on the use of a different emoticon at the end of a phrase:
That econ class you asked me about, it’s a joy. I wish all my classes were just like it. :-)
That econ class you asked me about, it’s a joy. I wish all my classes were just like it. :-(
That econ class you asked me about, it’s a joy. I wish all my classes were just like it. ;-)
That econ class you asked me about, it’s a joy. I wish all my classes were just like it.
That econ class you asked me about, it’s hell. I wish I never have another class like it. :-)
That econ class you asked me about, it’s hell. I wish I never have another class like it. :-(
That econ class you asked me about, it’s hell. I wish I never have another class like it. ;-)
That econ class you asked me about, it’s hell. I wish I never have another class like it.
Naturally, the wink emoticon was a bit more difficult to assess for the participants, as it is two sided; the smiling aspect suggests positivity, and the wink connotes an extra dimension of humor or irony. One of the more popular visualizations of Twitter today is the Pulse of the Nation: U.S. Mood Throughout the Day Inferred From Twitter , for which scientists have tracked the mood of Americans throughout the day, based on the words they use in their tweets. To quote John Haltiwanger, East Coast is really pissed. The tool analyzes tweets based on happy and sad words. While such words as laughter, friendly, romantic or rollercoaster are happy words, headache, sick, fearful or bomb are considered sad words. But what if I were to tweet „I’m on an emotional rollercoaster and don’t know how I will make it through the week” or „The concert last night was the bomb!” (I would never use this phrasing, but for the sake of proving my point, let’s pretend I would). What sense would the tool make of my emotions in this case? Maybe East Coast is not really pissed, just misinterpreted.
We are faced with a similar problem when using Twitrratr. Twitrratr could be the perfect tool for marketers to get an overview of how their product or their competition is viewed by Twitter users. The application is a search engine that finds all the tweets related to a certain topic or product and splits the content in to positive, negative or neutral categories. The problem is that when searching for Coca Cola, for example, it will display tweets such as I think the key to kicking Coca-Cola is to switch to Coke Zero. The stuff tastes like crippling depression in the neutral bin, while doesn’t really like coca-cola anymore is regarded as carrying positive meaning.
Twitter also provides inspiration for digital artists to develop art projects around it. Twistori was chosen by Mashable.com as one of six incredible Twitter powered art projects in 2009. Similar to Jonathan Harris’s project wefeelfine.org , it is an aggregate of human emotions displayed on Twitter, color-coded and arranged in an aesthetically pleasing manner. As an art project, it’s great, but once in a while the purple love cloud will turn gloomy with a sarcastic remark.
Although researching Twitter through info-visualization is a perfect way of incorporating research in a digital environment and we do, indeed, get to see our online-selves in a new light and get a deeper understanding of our role in creating content, I think we should not lose focus on appropriate methodology. Although Twitter infographics are perfect when it comes to temporal, spatial or social mapping, there is room for improvement in semantic mapping and qualitative research of emotions online.
References
Daantje Derks, Arjan E. R. Bos and Jasper von Grumbkow. Emoticons and Online Message Interpretation. Social Science Computer Review 2008 26: 379
Jill Walker Rettberg. ‘Freshly Generated for You, and Barack Obama’ : How Social Media Respresent Your Life. European Journal of Communication 2009 24: 451
Joseph B. Walther and Kyle P. D’Addario. The Impacts of Emoticons on Message Interpretation in Computer-Mediated Communication. Social Science Computer Review 2001 19: 324
Stephen Fineman, Sally Maitlis and Niki Panteli. Themed articles: Virtuality and emotion: Introduction. Human Relations 2007 60: 555