Twitter as a data source: An overview of tools for journalists

Wasim Ahmed 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.

There are other methods such as social network analysis, which examines online communities and the relationships between them. A number of qualitative methodologies also exist, such as content analysis and thematic analysis, which can be used to manually label social media posts. From a journalistic perspective, network analysis may be of importance initially via tools such as NodeXL. This is because it can quickly provide an overview of influential Twitter users alongside a topic overview.

From an industry standpoint, there has been much focus on gaining insight into users’ personalities, through services such as IBM Watson’s Personality Insights service. This uses linguistic analytics to derive intrinsic personality insights, such as emotions like anxiety, self-consciousness, and depression. This information can then be used by marketers to target certain products; for example, anti-anxiety medication to users who are more anxious…(An overview of tools for 2017).”