Twitter Research Supported by Info Visualisation

Everyday users of Twitter users provide the world with us with of tweets on topics differing from what they ate, how they feel to what music they listen to. This is a great source for researchers who want to look deeper into certain topics of interest. The question is only how to organize and present all this information and only filter out the important stuff. There seem to be a case of too much information within this medium that causes difficulty in understanding issues and how individuals view upon them. The term for this phenomenon is the much discussed ‘Information overload’. Back in the early sixties Bertram Gross already mentioned this term in his book ‘Managing of Organizations’. It was the sociologist and futurist Alvin Toffler who popularized the term later on in his book ‘Future Shock’ (1979). The challenge seems to be, to organize this big pool of data into understandable smaller pieces that are easier to grasp.

A solution to improve the interpretability of numerous tweets and therefore add great value to Twitter research is through information visualization. Since Twitter really is a people’s medium and researchers are trying to get an insight on these people, a clear method of organizing data in a way that everyone understands seems like a good solution. Chaomei Chen, explains in her paper the use and implications of information visualization as a research method:

“The holy grail of information visualization is for users to gain insights. In general, the notion of insight is broadly defined, including unexpected discoveries, a deepened understanding, a new way of thinking, eureka-like experiences, and other intellectual breakthroughs … A tough and ultimate question for information visualization advocates to the general public and other scientific and technological fields is what information visualization can achieve that other ways can’t or at least without paying a much higher price. In light of MySpace, FaceBook, Twitter, and more, social networking in cyberspace offers a new perspective to the issue”

Chen also points out that the social dimension of these networks may offer a new route to increase the chance of getting insight. Martin Watterberg’s is a pioneer in his field and with his ‘Map of the Market’ he visualized the stock market in 1998. He brought one of the first visualizations on the web. Inspired by his great work Jeff Clark, who is part of the community of data visualization enthusiasts did a good job by visualizing the big pile of data known as Twitter. He built four engines that basically search words used in tweets, then look for relationships to other words or other Tweeters, they function almost in real time. His tools are in the early stages but you can definitely imagine where they could be taken.

*Twitter Spectrum, a tool that compares two search terms and shows which words are most commonly associated with each term and which words are most commonly used in tweets with both terms

According to Clark there is a lot of work done called scientific visualization or business intelligence graphics but their all based on pragmatic problem solving, usually presented as a bar chart or pie. Those standard ways are not adequate if you try to mine a richer data space. The world is full of complex data and we’re just starting to get the tools to make sense of it. We’re looking for new ways of presenting data.

A great thing about Twitter is that is also a great place to track emerging trends. There are a wide variety of web applications, Twitter accounts, and even iPhone apps that can help people do everything, from track popular hashtags to graph out recent Twitter trends. As Twitter grows, this information will only become more useful for understanding what is popular at any given moment, or even what was popular in the past. A few of them are, Twist, Monitter, Tweetmeme and Twitturly, for more applications click here. I guess you can conclude that info visualization is an attractive and interesting way to support Twitter research. The outcomes will be useful for the scientists and understandable for the person in the street but at the same time challenging for programmers to build the best working engines.

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Berinato, S. (2010) Four ways of looking at Twitter. From http://blogs.hbr.org/research/2010/02/visualizing-twitter.html on 7 Oktober 2020.

Bertram, M. G. (1968) Organizations and their managing, New York: Free Press

Chen, C. (2010) Information visualization. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4) pp. 387-403

Tofffler, A. (1972) Future Shock, London: Pan Books.