Monday, 6 July 2015

Why is there so much research on Twitter? And what does this mean for our methods?

Wasim Ahmed is a PhD candidate at the University of Sheffield and the #NSMNSS Twitter Manager. This blog post was orignally posted on his research blog. You can find him on Twitter at @was3210
I was asked on Twitter by a fellow PhD student what tools and methods there were of capturing and analysing data from Facebook, and although I was able to find a few, there were far more Twitter data capture tools. I also noticed that there are very few tools that can be used to obtain data from other social media platforms such as, Pinterest, Goolge+, Tumblr, Instagram, Flickr, Vine, and Amazon among others. This led me to wonder whether it was tool availability, or some other reason for why there is more research on Twitter, compared to other social media platforms.
I then asked the following question on Twitter:
Why is there so much research on Twitter? Is it because it’s difficult to get data from other platforms? Or is Twitter a special platform?
I received a range of responses:
  1. Twitter is a popular platform in terms of the media attention it receives and therefore it attracts more research due to this cultural status
  2. Twitter makes it easier to find and follow conversations which consequently makes it easier to research
  3. Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data
  4. Twitter data is easy to retrieve as major incidents, news stories and events on Twitter are normally centered around a hashtag
  5. The Twitter API is more open and accessible compared to other social media platforms, which makes Twitter more favorable to developers creating tools to access data. This consequently increases the availability of tools to researchers.
It is probable that a combination of response 1 to 6 have led to more research on Twitter. However, this raises another distinct but closely related question: when research is focused so heavily on Twitter, what (if any) are the implications of this on our methods?
The methods that are currently used in analysing Twitter data i.e., sentiment analysis, time series analysis (examining peaks in tweets), network analysis etc., can these be applied to other platforms or are different tools, methods and techniques required?
I have used the following four methods in analysing Twitter data for the purposes of my PhD, below I consider whether these would work for other platforms:
  1. Sentiment analysis works well with Twitter data, as tweets are consistent in length (i.e., <= 140) would sentiment analysis work well with, for example Facebook data where posts may be longer?
  2. Time series analysis is normally used when examining tweets overtime to see when a peak of tweets may occur, would examining time stamps in Facebook posts, or Instagram posts, for example, produce the same results? Or is this only a viable method because of the real-time nature of Twitter data?
  3. Network analysis is used to visualize the connections between people and to better understand the structure of the conversation. Would this work as well on other platforms whereby users may not be connected to each other i.e., public Facebook pages, or images from Instagram?
  4. Machine learning methods may work well with Twitter data due to the length of tweets (i.e., <= 140) but would these work for longer posts and posts (i.e., Instagram) where images may be present?
It may well be that at least some of these methods can be applied to other platforms, however they may not be the best methods, and may require the formulation of new methods, techniques, and tools. On the tool front, I would like to see more software for those in the social sciences to obtain data for a range of platforms and including a range of data i.e., web links, images, and video. At the Masters and PhD level there should be more emphasis on training for social science students in effectively using existing software that can be used to capture data analyse data from social media platforms.


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