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Twitter is one of the most prominent social media platforms nowadays. A primary reason that has brought the medium at the spotlight of academic attention is its real-time nature, with people constantly uploading information regarding their surroundings. This trait, coupled with the service’s data access policy for researchers and developers, has allowed the community to explore Twitter’s potential as a news reporting tool. Finding out promptly about newsworthy events can prove extremely useful in crisis management situations. In this paper, we explore the use of Twitter as a mechanism used in disaster relief, and consequently in public safety. In particular, we perform a case study on the floods that occurred in the United Kingdom during January 2014, and how these were reflected on Twitter, according to tweets (i.e., posts) submitted by the users. We present a systematic algorithmic analysis of tweets collected with respect to our use case scenario, supplemented by visual analytic tools. Our objective is to identify meaningful and effective ways to take advantage of the wealth of Twitter data in crisis management, and we report on the findings of our analysis.

Browsing the web is one of the most common activities that users engage in nowadays, and downloading web resources of interest, such as images, documents, music, etc., is part of this process. However, users would rather temporarily save that resource to a default path that they have easy access to (e.g. their “Desktop”) than select the actual directory where they would eventually place it. This clearly implies that existing user interfaces are not as effective for this particular task as the users would like them to be. Instead of proposing a different User Interface, in this paper, we try to address the problem at its core, and propose a methodology to suggest the most likely directory where the file would (eventually) be saved by the user. By doing so, future interfaces can also benefit from our technique. We provide a formal definition of the problem and propose a classification framework to tackle it. We present our overall solution to this problem, namely Directory Download PrediCtor, or DiDoCtor for short. We give experimental evidence of its effectiveness, by implementing our approach as part of a widely used browser and evaluate it with real user activity. We also discuss lessons learned from this process, regarding the efficiency perspective.

Microblogging platforms, such as Twitter, Tumblr etc., have been established as key components in the contemporary Web ecosystem. Users constantly post snippets of information regarding their actions, interests or perception of their surroundings, which is why they have been attributed the term Live Web. Nevertheless, research on such platforms has been quite limited when it comes to identifying events, but is rapidly gaining ground. Event identification is a key step to news reporting, proactive or reactive crisis management at multiple scales, efficient resource allocation, etc. In this paper, we focus on the problem of automatically identifying events as they occur, in such a user-driven, fast paced and voluminous setting. We propose a novel and natural way to address the issue using notions from emotional theories, combined with spatiotemporal information and employ online event detection mechanisms to solve it at large scale in a distributed fashion. We present a modular framework that incorporates all of our key ideas and experimentally validate its superiority, in terms of both efficiency and effectiveness, over the state-of-the-art using real life data from the Twitter stream. We also present empirical evidence on the importance of spatiotemporal information in event detection for this setting.

Location is prevalent in most applications nowadays, and is considered a first class citizen in social networks. Locational information is of great significance since it can be used to map information from the online back to the physical world, to contextualize information, or to provide localized recommendations through Location-Based Services (LBS), and can be extended to trajectories and itineraries by adding a time-dimension. Despite all that, location extraction in social networks usually relies on GPS-enabled devices, that provide accurate geodetic coordinates. Nevertheless, most users provide general textual information about their surroundings, such as the city they live in, county or state (or equivalents), without using a GPS-enabled device, which is still valuable information for a number of applications. In this paper, we tackle the problem of extracting location information, usually referred to as geocoding, from additional user-provided content. Instead of using sophisticated and complex algorithms, which are common in online map services but require heavy development and tuning, we rely on software and data which are available online and publically accessible. We discuss the particularities of geocoding in online social networks and present a simple, lightweight, yet efficient approach for location extraction in such a setting. We finally evaluate our approach experimentally on a large corpus of Twitter users.