Inferring Online Relationships from User Characteristics

Inferring Relationships from Trajectory Data

Devices like smart phones and GPS navigators are very popular nowadays. These equipments can save the location of an object with an associated time, generating a new kind of data, called trajectories of moving objects. With these data it is possible to discover several interesting patterns, among which is the interaction between individuals, allowing to infer their relationship. This work addre...

Inferring User Interests From Microblogs

We address the problem of inferring users’ interests from microblogging sites such as Twitter, based on their utterances and interactions in the social network. Inferring user interests is important for systems such as search and recommendation engines to provide information that is more attuned to the likes of its users. In this paper, we propose a probabilistic generative model of user uttera...

Inferring the Location of Twitter Messages Based on User Relationships

Inferring Real-World Relationships from Spatiotemporal Data

The pervasiveness of GPS-enabled mobile devices and the popularity of location-based services have generated, for the first time, massive data that represents the movements of people in the real world at a high resolution, aka spatiotemporal data. Such collections of spatiotemporal data constitute a rich source of information for studying various social behaviors, and in particular, give a boos...

Inferring User Political Preferences from Streaming Communications

Existing models for social media personal analytics assume access to thousands of messages per user, even though most users author content only sporadically over time. Given this sparsity, we: (i) leverage content from the local neighborhood of a user; (ii) evaluate batch models as a function of size and the amount of messages in various types of neighborhoods; and (iii) estimate the amount of ...