TY - JOUR A1 - Vakkari, Pertti A1 - Völske, Michael A1 - Potthast, Martin A1 - Hagen, Matthias A1 - Stein, Benno T1 - Predicting essay quality from search and writing behavior JF - Journal of Association for Information Science and Technology N2 - Few studies have investigated how search behavior affects complex writing tasks. We analyze a dataset of 150 long essays whose authors searched the ClueWeb09 corpus for source material, while all querying, clicking, and writing activity was meticulously recorded. We model the effect of search and writing behavior on essay quality using path analysis. Since the boil-down and build-up writing strategies identified in previous research have been found to affect search behavior, we model each writing strategy separately. Our analysis shows that the search process contributes significantly to essay quality through both direct and mediated effects, while the author's writing strategy moderates this relationship. Our models explain 25–35% of the variation in essay quality through rather simple search and writing process characteristics alone, a fact that has implications on how search engines could personalize result pages for writing tasks. Authors' writing strategies and associated searching patterns differ, producing differences in essay quality. In a nutshell: essay quality improves if search and writing strategies harmonize—build-up writers benefit from focused, in-depth querying, while boil-down writers fare better with a broader and shallower querying strategy. KW - Information Retrieval KW - Textproduktion KW - Suchverfahren KW - Aufsatz KW - Suchverhalten KW - Pfadanalyse KW - Suchmaschine Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210804-44692 UR - https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24451 VL - 2021 IS - volume 72, issue 7 SP - 839 EP - 852 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Wiegmann, Matti A1 - Kersten, Jens A1 - Senaratne, Hansi A1 - Potthast, Martin A1 - Klan, Friederike A1 - Stein, Benno T1 - Opportunities and risks of disaster data from social media: a systematic review of incident information JF - Natural Hazards and Earth System Sciences N2 - Compiling and disseminating information about incidents and disasters are key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: (1) impact assessment and verification of model predictions, (2) narrative generation, (3) recruiting citizen volunteers, (4) supporting weakly institutionalized areas, (5) narrowing surveillance areas, and (6) reporting triggers for periodical surveillance. Furthermore, we discuss the benefits and shortcomings of using social media data for closing information gaps related to incidents and disasters. KW - Katastrophe KW - Social Media KW - Datenbank KW - Information KW - Katastrophenmanagement KW - Soziale Medien KW - Datensammlung Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210804-44634 UR - https://nhess.copernicus.org/articles/21/1431/2021/nhess-21-1431-2021.html VL - 2021 IS - Volume 21, Issue 5 SP - 1431 EP - 1444 PB - European Geophysical Society CY - Katlenburg-Lindau ER - TY - THES A1 - Al Khatib, Khalid T1 - Computational Analysis of Argumentation Strategies N2 - The computational analysis of argumentation strategies is substantial for many downstream applications. It is required for nearly all kinds of text synthesis, writing assistance, and dialogue-management tools. While various tasks have been tackled in the area of computational argumentation, such as argumentation mining and quality assessment, the task of the computational analysis of argumentation strategies in texts has so far been overlooked. This thesis principally approaches the analysis of the strategies manifested in the persuasive argumentative discourses that aim for persuasion as well as in the deliberative argumentative discourses that aim for consensus. To this end, the thesis presents a novel view of argumentation strategies for the above two goals. Based on this view, new models for pragmatic and stylistic argument attributes are proposed, new methods for the identification of the modelled attributes have been developed, and a new set of strategy principles in texts according to the identified attributes is presented and explored. Overall, the thesis contributes to the theory, data, method, and evaluation aspects of the analysis of argumentation strategies. The models, methods, and principles developed and explored in this thesis can be regarded as essential for promoting the applications mentioned above, among others. KW - Argumentation KW - Natürliche Sprache KW - Argumentation Strategies KW - Sprachverarbeitung KW - Natural Language Processing KW - Computational Argumentation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210719-44612 ER -