We all know that the first rule of hipsterdom is to not identify as a "hipster." And, if you really are one, you probably wouldn't be on something as mainstream as social media anyways. That creates a problem for geosocial data scientists like us. What do we call the folks who exhibit similar interests as these "mainstream-fearing, cypress-loving" people, and freely talk about themselves as hipsters on social media? Hence the name, "Self-Identifying Hipsters."
There is no shortage of hipster stereotypes out there, but we are more interested in what the data can show us than the perceptions of "hipsterness." In light of this, we present our "Self-Identifying Hipsters" segment for Boston, MA.
How Do You Find "Self-Identifying Hipsters" On Social Media?
We categorize location-based social media content (geosocial data) based on text similarity to reveal how areas compare with one another for a given social topic. Our analysis is more sophisticated than just looking at hashtags and keywords. Rather, these categories are generated through machine learning techniques. In other words, we didn’t set out to find "Self-Identifying Hipsters," it just happens to be a common type of conversation people naturally engage in on social media.
What is "Self-Identifying Hipsters?"
Some of the 200+ topics our text analysis organized for this specific category are:
hipster, nomad, hacked, inventor, restored, lonesome, #poser, cypress wood, aesthetics
These are the hipsters that aren’t afraid to tell you they are. They consider themselves outside the mainstream, even when holding popular opinions.
Over-Indexing Areas for "Self-Identifying Hipsters" in Boston
These are the areas that over-indexed for "Self-Identifying Hipsters" compared to the national average (national average = 0, 99th percentile = 10). It looks like our data confirms what the Boston Globe already knew regarding the "hipsterness" of Cambridge.
- Newton Corner
What would knowing this information help us with?
Coffee Shops: Our data has shown that there is a strong correlation between high-performing coffee shops and locations where "Self-Identifying Hipster" is over-indexing. By incorporating this type of analysis in their site planning strategy, property owners can focus on areas where "Self-Identifying Hipsters" scores highly.
Marketing: Businesses could optimize their marketing and advertising by analyzing the geosocial data. If a business sells succulents, for example, they could determine whether there is a strong correlation between "Self-Identifying Hipsters" and product sales. If there is a positive correlation, they could build a marketing strategy to best reach this segment.
The level of "Self-Identifying Hipsters" behavior can tell us a lot about an area, especially when observed alongside other social segments. What companies or use cases could you see this map being valuable for?
Curious about Spatial?