New software to analyse your tweets better

Researchers have developed a more accurate Twitter analysis tool - a software programme that helps decipher 'trends' on the microblogging website.
"Trending" topics on the social media platform Twitter show the quantity of tweets associated with a specific event.
However, trends only show the highest volume keywords and hashtags, and may not give qualitative information about the tweets themselves.
Now, using data associated with the Super Bowl, the annual championship game of the US National Football League (NFL), and World Series - the annual championship series of North American-based Major League Baseball (MLB), researchers at the University of Missouri have developed and validated a software programme that analyses event-based tweets and measures the context of tweets rather than just the quantity.

The programme will help Twitter analysts gain better insight into human behaviour associated with trends and events.
"Trends on Twitter are almost always associated with hashtags, which only gives you part of the story," said Sean Goggins, assistant professor in the School of Information Science and Learning Technologies at MU.

"When analysing tweets that are connected to an action or event, looking for specific words at the beginning of the tweets gives us a better indication of what is occurring, rather than only looking at hashtags," said Goggins.
Goggins partnered with Ian Graves, a doctoral student in the Computer Science and IT Department at the College of Engineering at MU, to develop the software that analyses tweets based on the words found within the tweets.

By programming a "bag of words," or tags they felt would be associated with the Super Bowl and World Series, the software analysed the words and their placement within the 140 character tweets.
"The software is able to detect more nuanced occurrences within the tweet, like action happening on the baseball field in between batters at the plate or plays in the game," Graves said.
"The programme uses a computational approach to seek out not only a spike in hashtags or words, but also what's really happening on a micro level. By looking for low-volume, localised tweets, we gleaned intelligence that stood apart from the clutter and noise associated with tweets related to the World Series," Graves said.

Goggins feels using this method to analyse tweets on a local level can help officials involved with community safety or disaster relief to investigate the causes of major events or to help predict future events.
"Most of the things that happen on Twitter are not related to specific events in the world," Goggins said.
"If analysts are just looking at the volume of tweets, they're not getting the insight they need about what's truly happening or the whole picture. By focusing on the words within the tweet, we have the potential to find a truer signal inside of a very noisy environment," Goggins said.

The study was published in the journal New Media and Strategy.