Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e

We present a framework for (automatically) classifying the relative interests of Twitter users using information from Wikipedia. Our proposed framework first uses Wikipedia to automatically classify a user’s celebrity followings into various interest categories, followed by determining the relative

lightly supervised approach based on label regularization to in-fer the age, ethnicity, and political orientation of Twitter users. Our approach learns from a heterogeneous collection of soft constraints derived from Census demographics, trends in baby names, and Twitter accounts that are em

Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological

to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.

Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present

its founding, and it remains unclear whether prior results still hold, and whether the (often implicit) as-sumptions of proposed systems are still valid. In this paper, we take a first step towards answering these question by focusing on the evolution of Twit-ter’s users and their behavior. Using a

While much work has considered the problem of latent attribute inference for users of social media such as Twitter, little has been done on non-English-based content and users. Here, we conduct the first assessment of latent at-tribute inference in languages beyond English, focusing on gender

Twitter is an immense snapshot of online human interaction. With over 40 million users, billions of tweets, hundreds of directed social links per user, and a liberal API for extracting data from it, it has the potential to provide more sociological data on human interaction than most sociologists

Microblogging websites, especially Twitter have become an important means of communication, in todays time. Often these services have been found to be faster than conventional news services. With millions of users, a need was felt to classify users based on ambient metadata associated