Researchers have developed a new software that analyzes event-based tweets and measures the context of tweets rather than just the quantity.
"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.
According to the researchers at the University of Missouri, the program will help Twitter analysts gain better insight into human behavior associated with trends and events.
Sean Goggins, assistant professor in the School of Information Science and Learning Technologies at MU, said that trends on Twitter are almost always associated with hashtags, which only gives you part of the story. When analyzing 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.
Goggins partnered with Ian Graves, a doctoral student in the Computer Science and IT Department at the College of Engineering at MU. Graves developed software that analyzes 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 analyzed the words and their placement within the 140 character tweets.
The researchers added that 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 and 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, localized tweets, they gleaned intelligence that stood apart from the clutter and noise associated with tweets related to the World Series.
The study was published in the journal, New Media and Strategy.
