A study of tweets sent around the United Nations 2014 climate summit gives a model for analysing social media in political activism, and suggests directions for more in-depth analysis.
Tools and methods for analysing social media use are increasingly helpful for governments monitoring social, environmental and quality of life indicators, and for democracy by informing strategies to engage citizens in political campaigns.
A short-term case study by the Joint Research Centre (JRC) models a ‘big data’ approach to studying online activism that aims to influence policies. Its method identifies when and where Twitter users are most engaged in a specific topic, and could be developed to dive deeper into civic engagement.
The JRC team based its study on activity around the September 2014 climate summit in New York. Over a five-day period before and during the summit, the team collected real-time original Twitter posts containing hashtags related to the event. This period included potential dates for high tweet numbers – i.e. Sunday 21 September 2014, the day of a protest march, and Tuesday 23 September, the day of the summit.
The frequency of the retrieved tweets was then visualised on maps and charts. This allowed the JRC researchers to look for patterns in the times and locations of posts containing these words during the five-day period, identifying how tweeters were trying to influence or promote the summit.
They found that one peak in temporal frequency was around 13:00 UTC on the day of the march, and another throughout the day of the summit. The hashtag climatemarch was used most during the march, while climate24, the summit’s official hashtag, was used most during the summit. The spatial distribution of tweeters during the march was concentrated around its course.
In themselves, the results are unsurprising. However, they confirm that the JRC team’s method can identify trends in engagement with an event and even show crowd movements during it.
The researchers point out that their method gives insights rather than statistics, and cannot provide complex information such tweeter profiles. But more precise analysis could be possible, according to the study. For example, retweets could be processed to identify the most influential tweeters, while keyword filters could adapt dynamically as campaign vocabularies change. The result could be a clearer view on political activism – for policy-makers and activists alike.