Users in social networks use hashtags for various reasons, some of them being serving search purposes, gaining attention or popularity or starting new conversation - thus, creating viral memes. In this paper we address the problem of classifying these hashtags in different categories, based on whether they represent a real life event or a social network generated meme. We compute a set of language-agnostic features to aid the classification of hashtags into events and memes and we provide an extensive study of the behavior that characterizes memes and events. We focus on Twitter social network, we apply our methods on a big dataset and reveal interesting characteristics of the two classes of hashtags.

A large number of mainstream applications, like temporal search, event detection, and trend identification, assume knowledge of the timestamp of every document in a given textual collection. In many cases, however, the required timestamps are either unavailable or ambiguous. A charac- teristic instance of this problem emerges in the context of large repositories of old digitized documents. For such doc- uments, the timestamp may be corrupted during the digiti- zation process, or may simply be unavailable. In this paper, we study the task of approximating the timestamp of a doc- ument, so-called document dating. We propose a content- based method and use recent advances in the domain of term burstiness, which allow it to overcome the drawbacks of pre- vious document dating methods, e.g. the fix time partition strategy. We use an extensive experimental evaluation on different datasets to validate the efficacy and advantages of our methodology, showing that our method outperforms the state of the art methods on document dating.

In this demonstration, we are presenting SmartMonitor, a distributed Structural Health Monitoring (SHM) system consisting of smart devices. Over the last few years, the vast majority of smart devices is equipped with accelerometers that can be utilized towards building SHM systems with hundreds of nodes. We describe a scalable, fault-tolerant communication protocol, that performs best-effort time synchronization of the nodes and is used to implement a decentralized version of the popular peak-picking SHM method. The implemented interactive system can be easily installed in any accelerometer-equipped Android device and the user has a number of options for configuring the system or analyzing the collected data and computed outcomes.

As the number and size of large timestamped collections (e.g. sequences of digitized newspapers, periodicals, blogs) increase, the problem of efficiently indexing and searching such data becomes more important. Term burstiness has been extensively researched as a mechanism to address event detection in the context of such collections. In this paper, we explore how burstiness information can be further utilized to enhance the search process. We present a novel approach to model the burstiness of a term, using discrepancy theory concepts. This allows us to build a parameter-free, linear-time approach to identify the time intervals of maximum burstiness for a given term. Finally, we describe the first burstiness-driven search framework and thoroughly evaluate our approach in the context of different scenarios.

Over the last few years, blogs (web logs) have gained massive popularity and have become one of the most influential web social media in our times. Every blog post in the Blogosphere has a well defined timestamp, which is not taken into account by search engines. By conducting research regarding this feature of the Blogosphere, we can attempt to discover bursty terms and correlations between them during a time interval. We apply Kleinberg's automaton on extracted titles of blog posts to discover bursty terms, we introduce a novel representation of a term's burstiness evolution called State Series and we employ a Euclidean-based distance metric to discover potential correlations between terms without taking into account their context. We evaluate the results trying to match them with real life events. Finally, we propose some ideas for further evaluation techniques and future research in the field.