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\n\n \n \n \n \n \n The useNews dataset.\n \n \n \n\n\n \n Puschmann, C.\n\n\n \n\n\n\n November 2020.\n
Virtuelles Austausch-Format von CrowdTangle/Facebook für internationale Journalisten und Wissenschaftler, auf Einladung von Naomi Shifman\n\n
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@misc{puschmann_usenews_2020,\n\ttitle = {The {useNews} dataset},\n\tauthor = {Puschmann, Cornelius},\n\tmonth = nov,\n\tyear = {2020},\n\tnote = {Virtuelles Austausch-Format von CrowdTangle/Facebook für internationale Journalisten und Wissenschaftler, auf Einladung von Naomi Shifman},\n}\n\n\n\n
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\n\n \n \n \n \n \n Der TCS-Ansatz für die Erforschung digitaler Mediennutzung.\n \n \n \n\n\n \n Puschmann, C.\n\n\n \n\n\n\n November 2020.\n
auf Einladung von Atilla Cevik, via Zoom\n\n
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@misc{puschmann_tcs-ansatz_2020,\n\ttitle = {Der {TCS}-{Ansatz} für die {Erforschung} digitaler {Mediennutzung}},\n\tauthor = {Puschmann, Cornelius},\n\tmonth = nov,\n\tyear = {2020},\n\tnote = {auf Einladung von Atilla Cevik, via Zoom},\n}\n\n\n\n
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\n\n \n \n \n \n \n Der TCS-Ansatz für die Erforschung digitaler Mediennutzung.\n \n \n \n\n\n \n \n\n\n \n\n\n\n February 2020.\n
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@misc{noauthor_tcs-ansatz_2020,\n\ttitle = {Der {TCS}-{Ansatz} für die {Erforschung} digitaler {Mediennutzung}},\n\tmonth = feb,\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n Der TCS-Ansatz für die Erforschung digitaler Mediennutzung.\n \n \n \n\n\n \n Puschmann, C.\n\n\n \n\n\n\n June 2020.\n
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@misc{puschmann_tcs-ansatz_2020,\n\ttitle = {Der {TCS}-{Ansatz} für die {Erforschung} digitaler {Mediennutzung}},\n\tauthor = {Puschmann, Cornelius},\n\tmonth = jun,\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n useNews.\n \n \n \n \n\n\n \n Puschmann, C.; and Haim, M.\n\n\n \n\n\n\n . January 2020.\n
Publisher: OSF\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{puschmann_usenews_2020,\n\ttitle = {{useNews}},\n\turl = {https://osf.io/uzca3/},\n\tdoi = {10.17605/OSF.IO/UZCA3},\n\tabstract = {The useNews dataset has been compiled to enable the study of online news engagement. It relies on the MediaCloud and CrowdTangle APIs as well as on data from the Reuters Digital News Report. The entire dataset builds on data from 2019 and 2020 as well as a total of 12 countries. It is free to use (subject to citing/referencing it).\n\nThe data originates from both the 2019 and the 2020 Reuters Digital News Report (http://www.digitalnewsreport.org/), media content from MediaCloud (https://mediacloud.org/) for 2019 and 2020 from all news outlets that have been used most frequently in the respective year according to the survey data, and engagement metrics for all available news-article URLs through CrowdTangle (https://www.crowdtangle.com/).\n\nTo start using the data, a total of eight data objects exist, namely one each for 2019 and 2020 for the survey, news-article meta information, news-article DFM's, and engagement metrics. To make your life easy, we've provided several packaged download options:\n\n- survey data for 2019, 2020, or both (also available in CSV format)\n- news-article meta data for 2019, 2020, or both (also available in CSV format)\n- news-article DFM's for 2019, 2020, or both\n- engagement data for 2019, 2020, or both (also available in CSV format)\n- all of 2019 or 2020\n\nAlso, if you are working with R, we have prepared a simple file to automatically download all necessary data ({\\textasciitilde}1.5 GByte) at once: https://osf.io/fxmgq/\n\nNote that all .rds files are .xz-compressed, which shouldn't bother you when you are in R. You can import all the .rds files through `variable\\_name \\<- readRDS('filename.rds')`, .RData (also .xz-compressed) can be imported by simply using `load('filename.RData')` which will load several already named objects into your R environment. To import data through other programming languages, we also provide all data in respective CSV files. These files are rather large, however, which is why we have also .xz-compressed them. DFM's, unfortunately, are not available as CSV's due to their sparsity and size.\n\nFind out more about the data variables and dig into plenty of examples in the useNews-examples workbook: https://osf.io/snuk2/ \n Hosted on the Open Science Framework},\n\tlanguage = {en},\n\turldate = {2023-11-09},\n\tauthor = {Puschmann, Cornelius and Haim, Mario},\n\tmonth = jan,\n\tyear = {2020},\n\tnote = {Publisher: OSF},\n}\n\n\n\n
\n
\n\n\n
\n The useNews dataset has been compiled to enable the study of online news engagement. It relies on the MediaCloud and CrowdTangle APIs as well as on data from the Reuters Digital News Report. The entire dataset builds on data from 2019 and 2020 as well as a total of 12 countries. It is free to use (subject to citing/referencing it). The data originates from both the 2019 and the 2020 Reuters Digital News Report (http://www.digitalnewsreport.org/), media content from MediaCloud (https://mediacloud.org/) for 2019 and 2020 from all news outlets that have been used most frequently in the respective year according to the survey data, and engagement metrics for all available news-article URLs through CrowdTangle (https://www.crowdtangle.com/). To start using the data, a total of eight data objects exist, namely one each for 2019 and 2020 for the survey, news-article meta information, news-article DFM's, and engagement metrics. To make your life easy, we've provided several packaged download options: - survey data for 2019, 2020, or both (also available in CSV format) - news-article meta data for 2019, 2020, or both (also available in CSV format) - news-article DFM's for 2019, 2020, or both - engagement data for 2019, 2020, or both (also available in CSV format) - all of 2019 or 2020 Also, if you are working with R, we have prepared a simple file to automatically download all necessary data (~1.5 GByte) at once: https://osf.io/fxmgq/ Note that all .rds files are .xz-compressed, which shouldn't bother you when you are in R. You can import all the .rds files through `variable_name <- readRDS('filename.rds')`, .RData (also .xz-compressed) can be imported by simply using `load('filename.RData')` which will load several already named objects into your R environment. To import data through other programming languages, we also provide all data in respective CSV files. These files are rather large, however, which is why we have also .xz-compressed them. DFM's, unfortunately, are not available as CSV's due to their sparsity and size. Find out more about the data variables and dig into plenty of examples in the useNews-examples workbook: https://osf.io/snuk2/ Hosted on the Open Science Framework\n
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\n\n \n \n \n \n \n \n Text as Data for Conflict Research: A Literature Survey.\n \n \n \n \n\n\n \n Maerz, S. F.; and Puschmann, C.\n\n\n \n\n\n\n In Deutschmann, E.; Lorenz, J.; Nardin, L. G.; Natalini, D.; and Wilhelm, A. F. X., editor(s),
Computational Conflict Research, of Computational Social Sciences, pages 43–65. Springer International Publishing, Cham, 2020.\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@incollection{maerz_text_2020,\n\taddress = {Cham},\n\tseries = {Computational {Social} {Sciences}},\n\ttitle = {Text as {Data} for {Conflict} {Research}: {A} {Literature} {Survey}},\n\tcopyright = {All rights reserved},\n\tisbn = {978-3-030-29333-8},\n\tshorttitle = {Text as {Data} for {Conflict} {Research}},\n\turl = {https://doi.org/10.1007/978-3-030-29333-8_3},\n\tabstract = {Computer-aided text analysis (CATA) offers exciting new possibilities for conflict research that this contribution describes using a range of exemplary studies from a variety of disciplines including sociology, political science, communication studies, and computer science. The chapter synthesizes empirical research that investigates conflict in relation to text across different formats and genres. This includes both conflict as it is verbalized in the news media, in political speeches, and other public documents and conflict as it occurs in online spaces (social media platforms, forums) and that is largely confined to such spaces (e.g., flaming and trolling). Particular emphasis is placed on research that aims to find commonalities between online and offline conflict, and that systematically investigates the dynamics of group behavior. Both work using inductive computational procedures, such as topic modeling, and supervised machine learning approaches are assessed, as are more traditional forms of content analysis, such as dictionaries. Finally, cross-validation is highlighted as a crucial step in CATA, in order to make the method as useful as possible to scholars interested in enlisting text mining for conflict research.},\n\tlanguage = {en},\n\turldate = {2022-09-26},\n\tbooktitle = {Computational {Conflict} {Research}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Maerz, Seraphine F. and Puschmann, Cornelius},\n\teditor = {Deutschmann, Emanuel and Lorenz, Jan and Nardin, Luis G. and Natalini, Davide and Wilhelm, Adalbert F. X.},\n\tyear = {2020},\n\tdoi = {10.1007/978-3-030-29333-8_3},\n\tkeywords = {Computer-aided text analysis, Cross-validation, Dictionary, Supervised and unsupervised machine learning, Text as data, Topic models},\n\tpages = {43--65},\n}\n\n\n\n
\n
\n\n\n
\n Computer-aided text analysis (CATA) offers exciting new possibilities for conflict research that this contribution describes using a range of exemplary studies from a variety of disciplines including sociology, political science, communication studies, and computer science. The chapter synthesizes empirical research that investigates conflict in relation to text across different formats and genres. This includes both conflict as it is verbalized in the news media, in political speeches, and other public documents and conflict as it occurs in online spaces (social media platforms, forums) and that is largely confined to such spaces (e.g., flaming and trolling). Particular emphasis is placed on research that aims to find commonalities between online and offline conflict, and that systematically investigates the dynamics of group behavior. Both work using inductive computational procedures, such as topic modeling, and supervised machine learning approaches are assessed, as are more traditional forms of content analysis, such as dictionaries. Finally, cross-validation is highlighted as a crucial step in CATA, in order to make the method as useful as possible to scholars interested in enlisting text mining for conflict research.\n
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\n\n \n \n \n \n \n \n Technische Faktoren bei der Verbreitung propagandistischer Inhalte im Internet und den sozialen Medien.\n \n \n \n \n\n\n \n Puschmann, C.\n\n\n \n\n\n\n In Schmitt, J. B.; Ernst, J.; Rieger, D.; and Roth, H., editor(s),
Propaganda und Prävention: Forschungsergebnisse, didaktische Ansätze, interdisziplinäre Perspektiven zur pädagogischen Arbeit zu extremistischer Internetpropaganda, of Interkulturelle Studien, pages 539–549. Springer Fachmedien, Wiesbaden, 2020.\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@incollection{puschmann_technische_2020,\n\taddress = {Wiesbaden},\n\tseries = {Interkulturelle {Studien}},\n\ttitle = {Technische {Faktoren} bei der {Verbreitung} propagandistischer {Inhalte} im {Internet} und den sozialen {Medien}},\n\tcopyright = {All rights reserved},\n\tisbn = {978-3-658-28538-8},\n\turl = {https://doi.org/10.1007/978-3-658-28538-8_29},\n\tabstract = {Dieser Beitrag nimmt die technischen Faktoren in den Blick, die bei der Erstellung, Weitergabe, Verbreitung und Rezeption von Propagandainhalten im Netz eine Rolle spielen. Folgend wird die Rolle von automatisierten AlgorithmenAlgorithmusdiskutiert, die mittlerweile bei zahlreichen Online-Plattformen im Hintergrund agieren und in diesen Prozessen wirken. Konkret werden zunächst die Verfahren des Rankings, der Selektion und der Filterung von Inhalten besprochen, und dann die algorithmische Personalisierung als Form des Informationsmanagements beschrieben, die zu negativen Auswirkungen führen kann (etwa sog. Filterblasen und Echokammern), um anschließend auf automatisierte Programme einzugehen, die in Social Media-Plattformen wirken. Zum Abschluss werden knapp Maßnahmen und konkrete Handlungsstrategien vorgestellt.},\n\tlanguage = {de},\n\turldate = {2022-09-26},\n\tbooktitle = {Propaganda und {Prävention}: {Forschungsergebnisse}, didaktische {Ansätze}, interdisziplinäre {Perspektiven} zur pädagogischen {Arbeit} zu extremistischer {Internetpropaganda}},\n\tpublisher = {Springer Fachmedien},\n\tauthor = {Puschmann, Cornelius},\n\teditor = {Schmitt, Josephine B. and Ernst, Julian and Rieger, Diana and Roth, Hans-Joachim},\n\tyear = {2020},\n\tdoi = {10.1007/978-3-658-28538-8_29},\n\tpages = {539--549},\n}\n\n\n\n
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\n Dieser Beitrag nimmt die technischen Faktoren in den Blick, die bei der Erstellung, Weitergabe, Verbreitung und Rezeption von Propagandainhalten im Netz eine Rolle spielen. Folgend wird die Rolle von automatisierten AlgorithmenAlgorithmusdiskutiert, die mittlerweile bei zahlreichen Online-Plattformen im Hintergrund agieren und in diesen Prozessen wirken. Konkret werden zunächst die Verfahren des Rankings, der Selektion und der Filterung von Inhalten besprochen, und dann die algorithmische Personalisierung als Form des Informationsmanagements beschrieben, die zu negativen Auswirkungen führen kann (etwa sog. Filterblasen und Echokammern), um anschließend auf automatisierte Programme einzugehen, die in Social Media-Plattformen wirken. Zum Abschluss werden knapp Maßnahmen und konkrete Handlungsstrategien vorgestellt.\n
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\n\n \n \n \n \n \n \n Explaining Online News Engagement Based on Browsing Behavior: Creatures of Habit?.\n \n \n \n \n\n\n \n Möller, J.; van de Velde, R. N.; Merten, L.; and Puschmann, C.\n\n\n \n\n\n\n
Social Science Computer Review, 38(5): 616–632. October 2020.\n
Publisher: SAGE Publications Inc\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@article{moller_explaining_2020,\n\ttitle = {Explaining {Online} {News} {Engagement} {Based} on {Browsing} {Behavior}: {Creatures} of {Habit}?},\n\tvolume = {38},\n\tcopyright = {All rights reserved},\n\tissn = {0894-4393},\n\tshorttitle = {Explaining {Online} {News} {Engagement} {Based} on {Browsing} {Behavior}},\n\turl = {https://doi.org/10.1177/0894439319828012},\n\tdoi = {10.1177/0894439319828012},\n\tabstract = {Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow modes of engagement with news compared to the act of routinely accessing a news organization?s website. In this study, we examine classic predictors of news consumption to explain the preference for three modes of news engagement in online tracking data: routine news use, news use triggered by social media, and news use as part of a general search for information. In pursuit of this aim, we make use of a unique data set that combines tracking data with survey data. Our findings show differences in predictors between preference for regular (direct) engagement, general search-driven, and social media?driven modes of news engagement. In describing behavioral differences in news consumption patterns, we demonstrate a clear need for further analysis of behavioral tracking data in relation to self-reported measures in order to further qualify differences in modes of news engagement.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2022-09-26},\n\tjournal = {Social Science Computer Review},\n\tauthor = {Möller, Judith and van de Velde, Robbert Nicolai and Merten, Lisa and Puschmann, Cornelius},\n\tmonth = oct,\n\tyear = {2020},\n\tnote = {Publisher: SAGE Publications Inc},\n\tpages = {616--632},\n}\n\n\n\n
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\n Understanding how citizens keep themselves informed about current affairs is crucial for a functioning democracy. Extant research suggests that in an increasingly fragmented digital news environment, search engines and social media platforms promote more incidental, but potentially more shallow modes of engagement with news compared to the act of routinely accessing a news organization?s website. In this study, we examine classic predictors of news consumption to explain the preference for three modes of news engagement in online tracking data: routine news use, news use triggered by social media, and news use as part of a general search for information. In pursuit of this aim, we make use of a unique data set that combines tracking data with survey data. Our findings show differences in predictors between preference for regular (direct) engagement, general search-driven, and social media?driven modes of news engagement. In describing behavioral differences in news consumption patterns, we demonstrate a clear need for further analysis of behavioral tracking data in relation to self-reported measures in order to further qualify differences in modes of news engagement.\n
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\n\n \n \n \n \n \n \n Converging on a nativist core? Comparing issues on the Facebook pages of the Pegida movement and the Alternative for Germany.\n \n \n \n \n\n\n \n Puschmann, C.; Ausserhofer, J.; and Šlerka, J.\n\n\n \n\n\n\n
European Journal of Communication, 35(3): 230–248. June 2020.\n
Publisher: SAGE Publications Ltd\n\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@article{puschmann_converging_2020,\n\ttitle = {Converging on a nativist core? {Comparing} issues on the {Facebook} pages of the {Pegida} movement and the {Alternative} for {Germany}},\n\tvolume = {35},\n\tcopyright = {All rights reserved},\n\tissn = {0267-3231},\n\tshorttitle = {Converging on a nativist core?},\n\turl = {https://doi.org/10.1177/0267323120922068},\n\tdoi = {10.1177/0267323120922068},\n\tabstract = {Computational methods offer a new perspective on the evolving agendas of right-wing movements and parties online. This article showcases computational approaches to text analysis (specifically so-called topic models) to diachronically investigate nativist right-wing issues in social media by comparing comments posted on the Facebook page of the Pegida movement to those of the Alternative for Germany. After describing topic modelling as an increasingly popular method and drawing on the literature on right-wing nativism online, we investigate a set of shared issues relevant to the mobilization of commentators, including opposition to Islam, migration, the government and the media. We furthermore show contrastively how issue prevalence differs between the two groups, and how issue shares change over time, in some instances converging on a shared nativist core. We close with a series of suggestions on the utility of computation content analysis for the study of rapidly evolving political agendas.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2022-09-26},\n\tjournal = {European Journal of Communication},\n\tauthor = {Puschmann, Cornelius and Ausserhofer, Julian and Šlerka, Josef},\n\tmonth = jun,\n\tyear = {2020},\n\tnote = {Publisher: SAGE Publications Ltd},\n\tpages = {230--248},\n}\n\n\n\n
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\n Computational methods offer a new perspective on the evolving agendas of right-wing movements and parties online. This article showcases computational approaches to text analysis (specifically so-called topic models) to diachronically investigate nativist right-wing issues in social media by comparing comments posted on the Facebook page of the Pegida movement to those of the Alternative for Germany. After describing topic modelling as an increasingly popular method and drawing on the literature on right-wing nativism online, we investigate a set of shared issues relevant to the mobilization of commentators, including opposition to Islam, migration, the government and the media. We furthermore show contrastively how issue prevalence differs between the two groups, and how issue shares change over time, in some instances converging on a shared nativist core. We close with a series of suggestions on the utility of computation content analysis for the study of rapidly evolving political agendas.\n
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\n\n \n \n \n \n \n DATAx revised. Die Vermittlung digitaler Kompetenz im Leuphana Semester unter Berücksichtigung von Gender- und Diversityaspekten.\n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n March 2020.\n
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@misc{hill_datax_2020,\n\ttitle = {{DATAx} revised. {Die} {Vermittlung} digitaler {Kompetenz} im {Leuphana} {Semester} unter {Berücksichtigung} von {Gender}- und {Diversityaspekten}},\n\tauthor = {Hill, Miira},\n\tmonth = mar,\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n Videobasierte Forschung, Öffentlichkeiten und ‚Perspektiven des Partikularen.\n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n September 2020.\n
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@misc{hill_videobasierte_2020,\n\taddress = {TU Berlin},\n\ttitle = {Videobasierte {Forschung}, Öffentlichkeiten und ‚{Perspektiven} des {Partikularen}},\n\tauthor = {Hill, Miira},\n\tmonth = sep,\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n Videobasierte Forschung, Öffentlichkeiten und Perspektiven des Partikularen.\n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n September 2020.\n
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@misc{hill_videobasierte_2020,\n\ttitle = {Videobasierte {Forschung}, Öffentlichkeiten und {Perspektiven} des {Partikularen}},\n\tauthor = {Hill, Miira},\n\tmonth = sep,\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n Innovative Popular Science Communication? Materiality, Aesthetics and Gender in Science Slams.\n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n In Morcillo, J. M.; and Robertson-von Trotha, C. Y., editor(s),
Genealogy of Popular Science: From Ancient Ecphrasis to Virtual Reality. Transcript Verlag, Bielefeld, 2020.\n
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@incollection{hill_innovative_2020,\n\taddress = {Bielefeld},\n\ttitle = {Innovative {Popular} {Science} {Communication}? {Materiality}, {Aesthetics} and {Gender} in {Science} {Slams}},\n\tbooktitle = {Genealogy of {Popular} {Science}: {From} {Ancient} {Ecphrasis} to {Virtual} {Reality}},\n\tpublisher = {transcript Verlag},\n\tauthor = {Hill, Miira},\n\teditor = {Morcillo, J. M. and Robertson-von Trotha, C. Y.},\n\tyear = {2020},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Data journalism’s many futures: Diagrammatic displays and prospective probabilities in data-driven news predictions.\n \n \n \n \n\n\n \n Pentzold, C.; and Fechner, D.\n\n\n \n\n\n\n
Convergence: The International Journal of Research into New Media Technologies, 26(4): 732–750. August 2020.\n
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@article{pentzold_data_2020,\n\ttitle = {Data journalism’s many futures: {Diagrammatic} displays and prospective probabilities in data-driven news predictions},\n\tvolume = {26},\n\tissn = {1354-8565, 1748-7382},\n\tshorttitle = {Data journalism’s many futures},\n\turl = {http://journals.sagepub.com/doi/10.1177/1354856519880790},\n\tdoi = {10.1177/1354856519880790},\n\tabstract = {This article explores how newsmakers exploit numeric records in order to anticipate the future. As this nascent area of data journalism experiments with predictive analytics, we examine its reports and computer-generated presentations, often infographics and data visualizations, and ask what time frames and topics are covered by these diagrammatic displays. We also interrogate the strategies that are employed in order to modulate the uncertainty involved in calculating for more than one possible outlook. Based on a comprehensive sample of projects, our analysis shows how data journalism seeks accuracy but has to cope with a number of different prospective probabilities and the puzzle of how to address this multiplicity of futures. Despite their predictive ambition, these forecasts are inherently grounded in the past because they are based on archival data. We conclude that this form of quantified premediation limits the range of imaginable future thoughts to one preferred mode, namely extrapolation.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-10-23},\n\tjournal = {Convergence: The International Journal of Research into New Media Technologies},\n\tauthor = {Pentzold, Christian and Fechner, Denise},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {732--750},\n}\n\n\n\n
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\n This article explores how newsmakers exploit numeric records in order to anticipate the future. As this nascent area of data journalism experiments with predictive analytics, we examine its reports and computer-generated presentations, often infographics and data visualizations, and ask what time frames and topics are covered by these diagrammatic displays. We also interrogate the strategies that are employed in order to modulate the uncertainty involved in calculating for more than one possible outlook. Based on a comprehensive sample of projects, our analysis shows how data journalism seeks accuracy but has to cope with a number of different prospective probabilities and the puzzle of how to address this multiplicity of futures. Despite their predictive ambition, these forecasts are inherently grounded in the past because they are based on archival data. We conclude that this form of quantified premediation limits the range of imaginable future thoughts to one preferred mode, namely extrapolation.\n
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\n\n \n \n \n \n \n \n Wissenschaft und Öffentlichkeit im Zeichen der Digitalisierung: Die Produktion und Kommunikation des Science-Slams.\n \n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n In Niemann, P.; Bittner, L.; Hauser, C.; and Schrögel, P., editor(s),
Science-Slam, pages 149–180. Springer Fachmedien Wiesbaden, Wiesbaden, 2020.\n
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@incollection{niemann_wissenschaft_2020,\n\taddress = {Wiesbaden},\n\ttitle = {Wissenschaft und Öffentlichkeit im {Zeichen} der {Digitalisierung}: {Die} {Produktion} und {Kommunikation} des {Science}-{Slams}},\n\tisbn = {9783658288600 9783658288617},\n\tshorttitle = {Wissenschaft und Öffentlichkeit im {Zeichen} der {Digitalisierung}},\n\turl = {http://link.springer.com/10.1007/978-3-658-28861-7_9},\n\tlanguage = {de},\n\turldate = {2023-10-23},\n\tbooktitle = {Science-{Slam}},\n\tpublisher = {Springer Fachmedien Wiesbaden},\n\tauthor = {Hill, Miira},\n\teditor = {Niemann, Philipp and Bittner, Laura and Hauser, Christiane and Schrögel, Philipp},\n\tyear = {2020},\n\tdoi = {10.1007/978-3-658-28861-7_9},\n\tpages = {149--180},\n}\n\n\n\n
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\n\n \n \n \n \n \n \n Innovative Popular Science Communication? Materiality, Aesthetics, and Gender in Science Slams.\n \n \n \n \n\n\n \n Hill, M.\n\n\n \n\n\n\n In Muñoz Morcillo, J.; and Robertson-von Trotha, C. Y., editor(s),
Genealogy of Popular Science, pages 517–544. Transcript Verlag, December 2020.\n
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@incollection{munoz_morcillo_innovative_2020,\n\ttitle = {Innovative {Popular} {Science} {Communication}? {Materiality}, {Aesthetics}, and {Gender} in {Science} {Slams}},\n\tisbn = {9783839448359},\n\tshorttitle = {Innovative {Popular} {Science} {Communication}?},\n\turl = {https://www.degruyter.com/document/doi/10.1515/9783839448359-025/html},\n\turldate = {2023-10-23},\n\tbooktitle = {Genealogy of {Popular} {Science}},\n\tpublisher = {transcript Verlag},\n\tauthor = {Hill, Miira},\n\teditor = {Muñoz Morcillo, Jesús and Robertson-von Trotha, Caroline Y.},\n\tmonth = dec,\n\tyear = {2020},\n\tdoi = {10.1515/9783839448359-025},\n\tpages = {517--544},\n}\n\n\n\n
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