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Copyright of International Review of Education / Internationale Zeitschrift für Erziehungswissenschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Qualitative computing has been part of our lives for thirty years. Today, we urgently call for an evaluation of its international impact on qualitative research. Evaluating the international impact of qualitative research and qualitative computing requires a consideration of the vast amount of qualitative research over the last decades, as well as thoughtfulness about the uneven and unequal way in which qualitative research and qualitative computing are present in different fields of study and geographical regions. To understand the international impact of qualitative computing requires evaluation of the digital divide and the huge differences between center and peripheries. The international impact of qualitative research, and, in particular qualitative computing, is the question at the heart of this array of selected papers from the "Qualitative Computing: Diverse Worlds and Research Practices Conference." In this article, we introduce the reader to the goals, motivation, and atmosphere at the conference, taking place in Istanbul, Turkey, in 2011. The dialogue generated there is still in the air, and this introduction is a call to spread that voice. [ABSTRACT FROM AUTHOR]

Copyright of Vergentis. Revista de Investigacion de la Catedra Internacional conjunta Inocencio III is the property of Vergentis. Revista de Investigacion de la Catedra Internacional conjunta Inocencio III and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts. [ABSTRACT FROM AUTHOR]