Inferring Activities from Social Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals

Social media and travel behaviour

Social media has emerged as a trend that greatly influences transportation and travel behaviour. This influence is identified both on the way that travellers make decisions concerning transportation related matters and the fact that social media allow tracing back the way that these decisions were made and allow for the collection of data, related to understanding these decisions. This chapter introduces these interdependences between Social Media (SM) and travel behaviour by presenting a collection of use cases and methods. Firstly, an introduction to the existing dominant SM is presented, including current availability of data and a discussion of helpful frameworks that could facilitate transportation related research on the subject. The pertinent literature on the User Generated Content (UGC) generation process and users’ personality characteristics is reviewed, in order to gain understanding on the characteristics of the users, who generate the content. Secondly, the relation of SM to travel behaviour is established by investigating the impact of UGC to the transportation system and vice versa. The capabilities of SM to allow for behavioural interventions is discussed towards the direction of inducing more socially responsible behaviour. Finally, related case studies are presented

Where and Why Users "Check In"

The emergence of location based social network (LBSN) services makes it possible to study individuals’ mobility patterns at a fine-grained level and to see how they are impacted by social factors. In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities. We explore how self-reinforcing behaviors, social factors, and exogenous effects contribute to this clustering and introduce a framework to distinguish these effects at the level of individual check-ins for both users and venues. Using check-in data from three major cities, we show not only that our model can improve prediction of future check-ins, but also that disentangling of different factors allows us to infer meaningful properties of different venues