Online Social Networks (OSNs) represent an online community of Internet users. Depending on the website in question, many of these online community members share common interests in, for example, hobbies, religion, politics, etc. Online social networks such as Twitter, Foursquare, and Facebook have emerged only during the last decade and have since become popular tools for public discourse as well as political and social commentary. Other social networks, such as LinkedIn, also focus on business and professional networking.
As the popularity of social networking is on the rise, new uses for the technology are constantly being unearthed. At the forefront of emerging trends on social networking websites is the concept of “real time” and “location based” searches. However, mining attributes such as the location of a user is not an easy task in itself. For instance, traditional text-based location extraction techniques often do not perform well in the domain of social networks. A reason for the abridged performance is the presence of multiple locations within the text, which makes it difficult to identify a single location for the user. Additionally, there is often a lack of relationship between the location of the user and the location mentioned in the text. Thus, there is a need for a solution to these limitations, as well as others, in existing techniques.