Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.
The advent of location-based social networking sites provides an open sharing space of crowd-sourced lifelogs that can be regarded as a novel source to monitor massive crowds' lifestyles in the real world. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing the crowd lifelogs in urban area. In order to collect crowd behavioral data, we utilize Twitter where enormous numbers of geo-tagged crowd's micro lifelogs can be easily acquired. We model the crowd behavior on the social network sites as a feature, which will be used to derive crowd-based urban characteristics. Based on this crowd behavior feature, we analyze significant crowd behavioral patterns for extracting urban characteristics. In the experiment, we actually conduct the urban characterization over the crowd behavioral patterns using a large number of geo-tagged tweets found in Japan from Twitter and report a comparison result with map-based observation of cities as an evaluation. The emergence of innovative web applications, often labelled as Web 2.0, has permitted an unprecedented increase of content created by non-specialist users. In particular, Location-based Social Networks (LBSN) are designed as platforms allowing the creation, storage and retrieval of vast amounts of georeferenced and user-generated contents. LBSN can thus be seen by Geographic Information specialists as a timely and cost-effective source of spatio-temporal information for many fields of application, provided that they can set up workflows to retrieve, validate and organise such information. This paper aims to improve the understanding on how LBSN can be used as a reliable source of spatio-temporal information, by analysing the temporal, spatial and social dynamics of Twitter activity during a major forest fire event in the South of France in July 2009.