Hashtags have emerged as a way to mark keywords, topics, commentary or even add snark to content posted on Twitter. Adding the hash character (#) to the beginning of any word attaches the posted text to a wider conversation. For example, using the #immigration hashtag in the midst of a post on the topic of immigration exposes it to a wider audience, especially as it becomes hotter during the GOP primaries. Hashtags are being embraced by the political class as a way to both connect with their audiences but also battle with their opponents. Especially this year, we’re seeing a surge in hashtag usage leading to the 2012 US elections.

Looking at hashtag usage over time sheds light on how people’s perceptions of candidates are reflected through the language that they use. That’s exactly what we set out to track, analyzing over 8500 hashtags used over the past 3 weeks associated with each Republican candidate.

At SocialFlow we analyze the resonance of language within audiences by looking at historical and realtime language used in posts. We develop an understanding for people’s perception on any given topic, mapping out relationships between terms and topics amongst different audiences. We learn which topics are central, who the conversational outliers are and what topics act as bridges between different sides of the spectrum.

Network graph of all hashtags used in reference with GOP candidates over the past 3 weeks.

From the data one learns that people’s perception of GOP candidates are certainly reflected through not only the language they use on Twitter, but also the frequency and combination of terms. In the graph above, each color represents connections between the candidate and associated hashtags. Every time a hashtag is published with a reference to a candidate in a tweet we added an edge to the graph. Every time hashtags appear with other hashtags, we also added an edge between them. The larger each node, the more times it appeared in tweets. Finally, we run a force-directed algorithm to organize the graph such that related hashtags appear closer. Continue reading →