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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.24
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.23
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    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Schrodt, R.: Tiefen und Untiefen im wissenschaftlichen Sprachgebrauch (2008) 0.20
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    Content
    Vgl. auch: https://studylibde.com/doc/13053640/richard-schrodt. Vgl. auch: http%3A%2F%2Fwww.univie.ac.at%2FGermanistik%2Fschrodt%2Fvorlesung%2Fwissenschaftssprache.doc&usg=AOvVaw1lDLDR6NFf1W0-oC9mEUJf.
  4. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.20
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  5. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.19
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  6. Suchenwirth, L.: Sacherschliessung in Zeiten von Corona : neue Herausforderungen und Chancen (2019) 0.19
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    Footnote
    https%3A%2F%2Fjournals.univie.ac.at%2Findex.php%2Fvoebm%2Farticle%2Fdownload%2F5332%2F5271%2F&usg=AOvVaw2yQdFGHlmOwVls7ANCpTii.
  7. Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013) 0.19
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    Content
    Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.
  8. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.17
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  9. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.15
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  10. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.15
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  11. Zemmels, D.: Accessing virtual social worlds : a unique methodology for research in new media spaces (2014) 0.13
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    Abstract
    Qualitative research into contemporary media engagement is particularly challenged by the complexity and diversity of today's media channels. Further, the very conception of media is rapidly changing: social activities have become closely linked with media engagement. As media saturation in everyday life continues to expand, the study of everyday "practices" in "ordinary people's lives," or habitus , to use Bourdieu's term (1980), constitutes social identity and thus an important object for media research. I offer a reconceptualization of media engagement as a participatory media culture and outline a unique methodological approach developed for the study of this culture in new media spaces. The methodology incorporated use of a laptop to record and analyze the auto-ethnographic reports and actions of a panel of respondents. As an example of an outcome using this methodology, an extensive taxonomy of web sites is outlined, which can be helpful in disaggregating data on new media practices for media research. The methodology could be adapted widely to many disciplines interested in the everyday practices of media engagement.
  12. Donsbach, W.: Wahrheit in den Medien : über den Sinn eines methodischen Objektivitätsbegriffes (2001) 0.12
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    Source
    Politische Meinung. 381(2001) Nr.1, S.65-74 [https%3A%2F%2Fwww.dgfe.de%2Ffileadmin%2FOrdnerRedakteure%2FSektionen%2FSek02_AEW%2FKWF%2FPublikationen_Reihe_1989-2003%2FBand_17%2FBd_17_1994_355-406_A.pdf&usg=AOvVaw2KcbRsHy5UQ9QRIUyuOLNi]
  13. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.12
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  14. Costas, R.; Zahedi, Z.; Wouters, P.: ¬The thematic orientation of publications mentioned on social media : large-scale disciplinary comparison of social media metrics with citations (2015) 0.12
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    Abstract
    Purpose - The purpose of this paper is to analyze the disciplinary orientation of scientific publications that were mentioned on different social media platforms, focussing on their differences and similarities with citation counts. Design/methodology/approach - Social media metrics and readership counts, associated with 500,216 publications and their citation data from the Web of Science database, were collected from Altmetric.com and Mendeley. Results are presented through descriptive statistical analyses together with science maps generated with VOSviewer. Findings - The results confirm Mendeley as the most prevalent social media source with similar characteristics to citations in their distribution across fields and their density in average values per publication. The humanities, natural sciences, and engineering disciplines have a much lower presence of social media metrics. Twitter has a stronger focus on general medicine and social sciences. Other sources (blog, Facebook, Google+, and news media mentions) are more prominent in regards to multidisciplinary journals. Originality/value - This paper reinforces the relevance of Mendeley as a social media source for analytical purposes from a disciplinary perspective, being particularly relevant for the social sciences (together with Twitter). Key implications for the use of social media metrics on the evaluation of research performance (e.g. the concentration of some social media metrics, such as blogs, news items, etc., around multidisciplinary journals) are identified.
    Date
    20. 1.2015 18:30:22
    Footnote
    Teil eines Special Issue: Social Media Metrics in Scholarly Communication: exploring tweets, blogs, likes and other altmetrics.
  15. Borgman, C.L.: Children's use of an interactive science library : exploratory research (1990) 0.11
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    Source
    School library media quarterly. 18(1990) no.2, S.108-111
  16. Kozma, R.B.: Learning with media (1991) 0.11
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    Source
    Review of educational research. 61(1991) no.2, S.179-211
  17. Fasick, A.M.: What research tells us about children's use of information media (1992) 0.11
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  18. Meier, F.: Informationsverhalten in Social Media (2015) 0.10
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    Abstract
    Der Beitrag plädiert für Social Media (social media) als Untersuchungsgegenstand der Informationsverhaltensforschung. Dabei wird vorgestellt, welche Charakteristika mit Facebook, Twitter und Co. als Informationsquellen verbunden sind, welche Fragestellungen für die Informationsverhaltensforschung im Kontext von social media relevant sind und welche Herausforderungen bei der Untersuchung solcher Plattformen bestehen. Studien und Forschungsarbeiten zur microblogging-Plattform Twitter, werden im Zuge einer allgemeinen Argumentation als Beispiele für konkrete Forschungsinteressen herangezogen.
    Source
    Information - Wissenschaft und Praxis. 66(2015) H.1, S.22-28
  19. December, J.: Units of analysis for Internet communication (1996) 0.10
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    Abstract
    Proposes an approach to defining units of analysis for Internet communication research. Defines the term Internet computer mediated communication, identifying the characteristics of Internet communication. Develops a set of definitions: media space, media class, media object and media instance, as units of analysis for Internet communication studies. Illustrates these units of analysis with some examples
  20. Zheng, H.; Aung, H.H.; Erdt, M.; Peng, T.-Q.; Raamkumar, A.S.; Theng, Y.-L.: Social media presence of scholarly journals (2019) 0.10
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    Abstract
    Recently, social media has become a potentially new way for scholarly journals to disseminate and evaluate research outputs. Scholarly journals have started promoting their research articles to a wide range of audiences via social media platforms. This article aims to investigate the social media presence of scholarly journals across disciplines. We extracted journals from Web of Science and searched for the social media presence of these journals on Facebook and Twitter. Relevant metrics and content relating to the journals' social media accounts were also crawled for data analysis. From our results, the social media presence of scholarly journals lies between 7.1% and 14.2% across disciplines; and it has shown a steady increase in the last decade. The popularity of scholarly journals on social media is distinct across disciplines. Further, we investigated whether social media metrics of journals can predict the Journal Impact Factor (JIF). We found that the number of followers and disciplines have significant effects on the JIF. In addition, a word co-occurrence network analysis was also conducted to identify popular topics discussed by scholarly journals on social media platforms. Finally, we highlight challenges and issues faced in this study and discuss future research directions.

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