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Graham, Milligan, and Weingart argue that computational historians were too hubristic in their truth claims concerning the use of statistics to understand history? Can the same be said for digital historians and “big data”?
The most important line to me in the discussion of understanding ‘big data’ in the “Accessing the Third Wave Today” section where the author states “This big data, however, is only as useful as the tools that we have to interpret it”. “Big data” is great, you can compile thousands of articles on a certain topic and look for trends in word usage or word that have simply been omitted. Referencing the “The United states are” vs. “The United States is” example, tools are only are only useful if you know HOW to use them. These findings were only found because a digital historian decided to utilize this tool to look for the trends in when the United States was described as a single entity. Relating to our lab last week, Voyant Tools was able to read our document and produce work frequencies, but it was up to us to change the ‘stop words’ to create a bigger story and find meaning in the article without actually reading the entire thing.
I think that the same can be said about digital historians and “big data”. On the page “The Limits of Big Data, orBig Data and the Practice if History” it is explained that big data has many implications. This was not an issue that was brought up at the forefront of this book chapter, but are still prominent for digital historians. Big Data contains extensive computational databases that contributes to computational assumptions. Just as our discussion of H-Net last week which derives answers to posed questions based on consensus rather than facts, there are embedded assumptions in Big Data as well. Big Data is helpful for digital historians because it can help derive meaning from multiple sources, but unless these assumptions are accounted for, a true sense of the history embedded in these sources are misunderstood by hubristic beliefs in the use of these new technologies.
Big data has become a popular analytical tool that just started to evolve years ago. It has launched a new era of technology due to it’s new found perspective. For historians, it is especially significant that newer approaches like big data are continuously improving its archival methods. However, big data is a tool that is not controlled by humans manually, therefore it has little to no empathetic meaning. Thus, it is essential for historians to not be hubristic, implementing risk preventions to take in considerations of the advantages and disadvantages for digital approaches like the big data. When people first discovered the big data and other digital approaches, we often attain the benefits of the method first before noticing the possible negative side-effects. In addition, as we have learnt in many chapters from the course readings, technologies can not completely guarantee data preservation. For historians, big data does save time and effort in some way; for instance, when collecting data. Nonetheless, big data does need more in-depth detailed analysis from historians to interpret it.
After reading this work it is evident that big data is more prominent within society today. Since it is constantly evolving, historians use these tools because they are constantly developing and updating. Big data allows historians to take data from multiple sources in order to derive meaning for a specific topic. However, since Big Data is out of human control, there is more room for errors. As seen with sourcing, this approach allows historians to take multiple points of view, however if they are not properly sourced, this may lead to audiences taking on a hubristic belief and misunderstanding. This may also lead to improper data preservation as discussed in previous class discussions.
Great responses this week and goo discussion about these issues surrounding the use of “big data” in history. I think we highlighted some of the exciting possibilities of distant reading, but (I hope) we also noted the potential risks of assuming too much from this methodology and losing sight of the continued importance of traditional close reading methods.
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