Description
We chose to focus on social media because we wanted to analyse current active debate. We picked Twitter because it is an online hive of activity and real-time communication by individuals, news outlets and corporations. The nature of the website encourages people to be vocal, opinionated and discursive and even utilises features such as top hashtags to allow users to follow trending topics.
Our initial finding was that we had many more hashtags on Twitter discussing “#genderequality” as opposed to “#genderinequality” and so we chose to focus on the one which generated the most results.
The alluvial graph displayed in this section starts by showing a ranked list of the 50 most active users who use hashtags related to the query “gender equality” inside of the dataset, generated by the tool we used. In the central nodes of the alluvial graph the users are divided into whether they are public pages, personal pages or unidentified. We manually divided the users into categories by male, female & transgender by visiting their pages. We discovered that public information pages are very active in discussing our query. They focus on different topics, from women’s empowerment to men’s rights. Among personal pages, we found a very interesting pattern where men were more likely than women to tweet about gender equality.
Protocol
Starting from the Twitter capture analysis tool (TCAT) we used a specific function called “hashtag user activity”. Inside the “gender” dataset we inserted the query “gender equality” and set the time range from 10/1/16 to 10/2/2016, which was the last month available for our dataset. From there we obtained an information rich data set with the list of hashtags, the number of users (with their username) tweeting hashtags related to our query and the number of tweets containing that hashtag.
Data
Timestamp: 01/12/2016 - 05/12/2016
Data source: Scopus, Google Scholar
Download data (4MB)
The tool we used generated a table with various information. Resulting from the dataset, we firstly obtained a list of most relevant hashtags related to “gender”. We then have the number of tweets containing these hashtags. Lastly, we identified a list of users who use these hashtags actively and their user frequency.