Read the full post at Data Driven Journalism.
Journalists may wish to use data from social media platforms in order to provide greater insight and context to a news story. For example, journalists may wish to examine the contagion of hashtags and whether they are capable of achieving political or social change. Moreover, newsrooms may also wish to tap into social media posts during unfolding crisis events. For example, to find out who tweeted about a crisis event first, and to empirically examine the impact of social media.
Furthermore, Twitter users and accounts such as WikiLeaks may operate outside the constraints of traditional journalism, and therefore it becomes important to have tools and mechanisms in place in order to examine these kinds of influential users. For example, it was found that those who were backing Marine Le Pen on Twitter could have been users who had an affinity to Donald Trump.
There remains a number of different methods for analysing social media data. Take text analytics, for example, which can include using sentiment analysis to place bulk social media posts into categories of a particular feeling, such as positive, negative, or neutral. Or machine learning, which can automatically assign social media posts to a number of different topics.