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Social media like Twitter and micro-blogs provide a goldmine of text, shallow markup annotations and network structure. These information sources can all be exploited together in order to automatically acquire vast amounts of up-to-date, wide-coverage structured knowledge. This knowledge, in turn, can be used to measure the pulse of a variety of social phenomena like political events, activism and stock prices, as well as to detect emerging events such as natural disasters (earthquakes, tsunami, etc.). The main purpose of this tutorial is to introduce social media as a resource to the Natural Language Processing (NLP) community both from a scientific and an application-oriented perspective. To this end, we focus on micro-blogs such as Twitter, and show how it can be successfully mined to perform complex NLP tasks such as the identification of events, topics and trends. Furthermore, this information can be used to build high-end socially intelligent applications that tap the wisdom of the crowd on a large scale, thus successfully bridging the gap between computational text analysis and real-world, mission-critical applications such as financial forecasting and natural crisis management.

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline.

In this paper, we describe our effort to create a new corpus for the evaluation of detecting and linking so-called survey variables in social science publications (e.g., "Do you believe in Heaven?"). The task is to recognize survey variable mentions in a given text, disambiguate
them, and link them to the corresponding variable within a knowledge base. Since there are generally hundreds of candidates to link to and due to the wide variety of forms they can take, this is a challenging task within NLP. The contribution of our work is the first gold standard corpus for the variable detection and linking task. We describe the annotation guidelines and the annotation process. The produced corpus is multilingual - German and English - and includes manually curated word and phrase alignments. Moreover, it includes text samples that could not be assigned to any variables, denoted as negative examples. Based on the new dataset, we conduct an evaluation of several state-of-the-art text classification and textual similarity methods. The annotated corpus is made available along with an open-source baseline system for variable mention identification and linking.

Die gegenständliche Studie untersucht, ob sich neue 'Politikstile' - insbesondere das Konzept der 'neuen Missionsorientierung' - in der Forschungs- und Innovations- (F&I) Politik beobachten lassen, wie diese charakterisiert werden können, und welche Schlussfolgerungen für die deutsche F&I-Politik zu ziehen wären. Diese neuen Ansätze sind theoretisch schon gut beschrieben und in einigen Ländern schon in verschiedenen Formen ("Akzelerator/Transformator Missionen") zu beobachten. Diese Missions-Typen weisen jeweils unterschiedliche Anforderungen an Governance auf den Ebenen der Strategiefindung, der Koordination und der Implementierung auf. In Deutschland finden sich diese Ansätze explizit erst in jüngerer Zeit in Strategien und Programmen wieder. Eine Reihe von Politikinitiativen konnten als tendenziell diesem Politikstil zurechenbar identifiziert werden, obgleich sie bislang nicht explizit als missionsorientierte Politik betrieben werden (z.B. Mikroelektronik, Industrie 4.0, Nanotechnologie aus der HTS III, Rohstoffstrategie 2020 u.a.). Diese Befunde könnten dazu dienen, die identifizierten Politiken zu missionsorientierten Politiken weiterzuentwickeln oder zumindest die vermuteten Potentiale zu überprüfen. Auf diesen Befunden aufbauend werden Bedingungen für die erfolgreiche Umsetzung dieser neuen Ansätze in Deutschland beschrieben.