Hashtags and Information Retrieval on Twitter
While an average tweet by itself might not contain a wealth of content, an average day of tweeting does, by an impressive degree. Twitter handles an average of 500 million tweets per day, and around 200 billion tweets per year, (as of 2013), and the number of Twitter users is only growing larger.[1] With such a massive corpus of messages, the Twitter community needed a way of cataloging this data if anyone was going to have any hope of understanding it.
Their solution? #Hashtags. A hashtag is a label that users place on their own tweets that associates their messages with a certain group of similar messages. For instance, one may associate a tweet with #networks, which would allow their message to be seen next to other posts with the same hashtag.
Since their introduction in 2009, hashtags have become one of Twitter’s most iconic features. Twitter aggregates the most popular hashtags in real-time as the basis for their “Trending” system, which allows users to see what’s currently being discussed on the social network. This real-time collection of tweets allows Twitter to be one of the most up to date social networks, with news breaking on twitter sometimes faster than it does in the mainstream media![2]
Although hashtags aren’t perfect (as they depend on the user to annotate their own content), and are rarely predictable, they serve as an effective way to get a big picture view of current discussions on Twitter. Twitter’s approach to information retrieval and cataloguing is a perfect fit for the spontaneous social network, and come on, how many other metadata tags have their own entry in the Oxford English Dictionary?[3]
[1] http://www.internetlivestats.com/twitter-statistics/
[2] https://www.fastcoexist.com/1682521/twitter-vs-mainstream-media-science-proves-which-breaks-news-faster
[3] http://www.theregister.co.uk/2014/06/13/hashtag_added_to_the_oed/