Search (330 results, page 1 of 17)

0.053494282 = product of:
  0.07132571 = sum of:
    0.049533032 = product of:
      0.14859909 = sum of:
        0.14859909 = weight(_text_:3a in 5669) [ClassicSimilarity], result of:
          0.14859909 = score(doc=5669,freq=2.0), product of:
            0.31728324 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.037424255 = queryNorm
            0.46834838 = fieldWeight in 5669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5669)
      0.33333334 = coord(1/3)
    0.013265181 = product of:
      0.026530363 = sum of:
        0.026530363 = weight(_text_:28 in 5669) [ClassicSimilarity], result of:
          0.026530363 = score(doc=5669,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.19789396 = fieldWeight in 5669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5669)
      0.5 = coord(1/2)
    0.008527494 = product of:
      0.025582481 = sum of:
        0.025582481 = weight(_text_:29 in 5669) [ClassicSimilarity], result of:
          0.025582481 = score(doc=5669,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.19432661 = fieldWeight in 5669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5669)
      0.33333334 = coord(1/3)
  0.75 = coord(3/4)

Content

"Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."

Date

28. 1.2020 19:35:33

0.052424937 = product of:
  0.06989992 = sum of:
    0.022678176 = product of:
      0.045356352 = sum of:
        0.045356352 = weight(_text_:media in 178) [ClassicSimilarity], result of:
          0.045356352 = score(doc=178,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.25874978 = fieldWeight in 178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0390625 = fieldNorm(doc=178)
      0.5 = coord(1/2)
    0.013265181 = product of:
      0.026530363 = sum of:
        0.026530363 = weight(_text_:28 in 178) [ClassicSimilarity], result of:
          0.026530363 = score(doc=178,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.19789396 = fieldWeight in 178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.0390625 = fieldNorm(doc=178)
      0.5 = coord(1/2)
    0.033956554 = product of:
      0.05093483 = sum of:
        0.025582481 = weight(_text_:29 in 178) [ClassicSimilarity], result of:
          0.025582481 = score(doc=178,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.19432661 = fieldWeight in 178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=178)
        0.02535235 = weight(_text_:22 in 178) [ClassicSimilarity], result of:
          0.02535235 = score(doc=178,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.19345059 = fieldWeight in 178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=178)
      0.6666667 = coord(2/3)
  0.75 = coord(3/4)

Abstract

Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.

Date

20. 1.2015 18:30:22
12. 3.2021 18:41:28

0.04239948 = product of:
  0.08479896 = sum of:
    0.049533032 = product of:
      0.14859909 = sum of:
        0.14859909 = weight(_text_:3a in 1000) [ClassicSimilarity], result of:
          0.14859909 = score(doc=1000,freq=2.0), product of:
            0.31728324 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.037424255 = queryNorm
            0.46834838 = fieldWeight in 1000, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1000)
      0.33333334 = coord(1/3)
    0.03526593 = product of:
      0.10579779 = sum of:
        0.10579779 = weight(_text_:ermittelt in 1000) [ClassicSimilarity], result of:
          0.10579779 = score(doc=1000,freq=2.0), product of:
            0.26771787 = queryWeight, product of:
              7.1535926 = idf(docFreq=93, maxDocs=44218)
              0.037424255 = queryNorm
            0.3951839 = fieldWeight in 1000, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              7.1535926 = idf(docFreq=93, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1000)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

Vorgestellt wird die Konstruktion eines thematisch geordneten Thesaurus auf Basis der Sachschlagwörter der Gemeinsamen Normdatei (GND) unter Nutzung der darin enthaltenen DDC-Notationen. Oberste Ordnungsebene dieses Thesaurus werden die DDC-Sachgruppen der Deutschen Nationalbibliothek. Die Konstruktion des Thesaurus erfolgt regelbasiert unter der Nutzung von Linked Data Prinzipien in einem SPARQL Prozessor. Der Thesaurus dient der automatisierten Gewinnung von Metadaten aus wissenschaftlichen Publikationen mittels eines computerlinguistischen Extraktors. Hierzu werden digitale Volltexte verarbeitet. Dieser ermittelt die gefundenen Schlagwörter über Vergleich der Zeichenfolgen Benennungen im Thesaurus, ordnet die Treffer nach Relevanz im Text und gibt die zugeordne-ten Sachgruppen rangordnend zurück. Die grundlegende Annahme dabei ist, dass die gesuchte Sachgruppe unter den oberen Rängen zurückgegeben wird. In einem dreistufigen Verfahren wird die Leistungsfähigkeit des Verfahrens validiert. Hierzu wird zunächst anhand von Metadaten und Erkenntnissen einer Kurzautopsie ein Goldstandard aus Dokumenten erstellt, die im Online-Katalog der DNB abrufbar sind. Die Dokumente vertei-len sich über 14 der Sachgruppen mit einer Losgröße von jeweils 50 Dokumenten. Sämtliche Dokumente werden mit dem Extraktor erschlossen und die Ergebnisse der Kategorisierung do-kumentiert. Schließlich wird die sich daraus ergebende Retrievalleistung sowohl für eine harte (binäre) Kategorisierung als auch eine rangordnende Rückgabe der Sachgruppen beurteilt.

Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

0.03838507 = product of:
  0.07677014 = sum of:
    0.060851928 = product of:
      0.121703856 = sum of:
        0.121703856 = weight(_text_:media in 549) [ClassicSimilarity], result of:
          0.121703856 = score(doc=549,freq=10.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.6942985 = fieldWeight in 549, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.046875 = fieldNorm(doc=549)
      0.5 = coord(1/2)
    0.015918218 = product of:
      0.031836435 = sum of:
        0.031836435 = weight(_text_:28 in 549) [ClassicSimilarity], result of:
          0.031836435 = score(doc=549,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.23747274 = fieldWeight in 549, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.046875 = fieldNorm(doc=549)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.

Date

6. 4.2022 19:28:00

0.030655399 = product of:
  0.122621596 = sum of:
    0.122621596 = product of:
      0.1839324 = sum of:
        0.14811692 = weight(_text_:ermittelt in 781) [ClassicSimilarity], result of:
          0.14811692 = score(doc=781,freq=2.0), product of:
            0.26771787 = queryWeight, product of:
              7.1535926 = idf(docFreq=93, maxDocs=44218)
              0.037424255 = queryNorm
            0.55325747 = fieldWeight in 781, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              7.1535926 = idf(docFreq=93, maxDocs=44218)
              0.0546875 = fieldNorm(doc=781)
        0.03581547 = weight(_text_:29 in 781) [ClassicSimilarity], result of:
          0.03581547 = score(doc=781,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.27205724 = fieldWeight in 781, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0546875 = fieldNorm(doc=781)
      0.6666667 = coord(2/3)
  0.25 = coord(1/4)

Abstract

Unter Indexierung versteht man die Zuordnung von inhaltskennzeichnenden Ausdrücken (Indextermen, Indexaten, Erschließungsmerkmalen) zu Dokumenten. Über die zugeteilten Indexterme soll ein gezieltes Auffinden der Dokumente ermöglicht werden. Indexterme können inhaltsbeschreibende Merkmale wie Notationen, Deskriptoren, kontrollierte oder freie Schlagwörter sein; es kann sich auch um reine Stichwörter handeln, die aus dem Text des Dokuments gewonnen werden. Eine Indexierung kann intellektuell, computerunterstützt oder automatisch erfolgen. Computerunterstützte Indexierungsverfahren kombinieren die intellektuelle Indexierung mit automatischen Vorarbeiten. Bei der automatischen Indexierung werden die Indexterme automatisch aus dem Dokumenttext ermittelt und dem Dokument zugeordnet. Automatische Indexierung bedient sich für die Verarbeitung der Zeichenketten im Dokument linguistischer und statistischer Verfahren.

Date

24.11.2022 13:29:16

0.029580362 = product of:
  0.059160724 = sum of:
    0.05070994 = product of:
      0.10141988 = sum of:
        0.10141988 = weight(_text_:media in 263) [ClassicSimilarity], result of:
          0.10141988 = score(doc=263,freq=10.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.5785821 = fieldWeight in 263, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0390625 = fieldNorm(doc=263)
      0.5 = coord(1/2)
    0.008450784 = product of:
      0.02535235 = sum of:
        0.02535235 = weight(_text_:22 in 263) [ClassicSimilarity], result of:
          0.02535235 = score(doc=263,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.19345059 = fieldWeight in 263, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=263)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

When people experience major life changes, this often impacts their self-presentation, networks, and online behavior in substantial ways. To effectively study major life transitions and events, we surveyed a large U.S. sample (n = 554) to create the Major Life Events Taxonomy, a list of 121 life events in 12 categories. We then applied this taxonomy to a second large U.S. survey sample (n = 775) to understand on average how much social readjustment each event required, how likely each event was to be shared on social media with different types of audiences, and how much online network separation each involved. We found that social readjustment is positively correlated with sharing on social media, with both broad audiences and close ties as well as in online spaces separate from one's network of known ties. Some life transitions involve high levels of sharing with both separate audiences and broad audiences on social media, providing evidence for what previous research has called social media as social transition machinery. Researchers can use the Major Life Events Taxonomy to examine how people's life transition experiences relate to their behaviors, technology use, and health and well-being outcomes.

Date

10. 6.2021 19:22:47

0.026903568 = product of:
  0.053807136 = sum of:
    0.045356352 = product of:
      0.090712704 = sum of:
        0.090712704 = weight(_text_:media in 1054) [ClassicSimilarity], result of:
          0.090712704 = score(doc=1054,freq=8.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.51749957 = fieldWeight in 1054, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1054)
      0.5 = coord(1/2)
    0.008450784 = product of:
      0.02535235 = sum of:
        0.02535235 = weight(_text_:22 in 1054) [ClassicSimilarity], result of:
          0.02535235 = score(doc=1054,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.19345059 = fieldWeight in 1054, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1054)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

To effectively manage a crisis, most decisions made by governments, organizations, communities, and individuals are based on "shared situational awareness" (SSA) derived from multiple information sources. Developing SSA depends on the alignment of mental models, which "represent our shared version of truth and reality on which we can act." Social media has facilitated public sensemaking during a crisis; however, it has also encouraged mental model dissonance, resulting in the digital destruction of mental models and undermining adequate SSA. The study is concerned with the challenges of creating SSA during the COVID-19 pandemic in Australia. This paper documents a netnography of Australian public health agencies' Facebook communication, exploring the initial impact of COVID-19 on SSA creation. Chaos theory is used as a theoretical lens to examine information perception, meaning, and assumptions relating to SSA from pre to post-pandemic periods. Our study highlights how the initial COVID-19 "butterfly effect" swamped the public health communication channel, leaving little space for other important health issues. This research contributes to information systems, information science, and communications by illustrating how the emergence of a crisis impacts social media communication, the creation of SSA, and what this means for social media adoption for crisis communication purposes.

Date

22. 9.2023 16:02:26

0.02615121 = product of:
  0.05230242 = sum of:
    0.031836435 = product of:
      0.06367287 = sum of:
        0.06367287 = weight(_text_:28 in 5742) [ClassicSimilarity], result of:
          0.06367287 = score(doc=5742,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.4749455 = fieldWeight in 5742, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.09375 = fieldNorm(doc=5742)
      0.5 = coord(1/2)
    0.020465985 = product of:
      0.061397955 = sum of:
        0.061397955 = weight(_text_:29 in 5742) [ClassicSimilarity], result of:
          0.061397955 = score(doc=5742,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.46638384 = fieldWeight in 5742, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.09375 = fieldNorm(doc=5742)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Date

18. 3.2020 18:48:28

Source

Spektrum der Wissenschaft. 2020, H.3, S.29

0.024964536 = product of:
  0.04992907 = sum of:
    0.03628508 = product of:
      0.07257016 = sum of:
        0.07257016 = weight(_text_:media in 20) [ClassicSimilarity], result of:
          0.07257016 = score(doc=20,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.41399965 = fieldWeight in 20, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0625 = fieldNorm(doc=20)
      0.5 = coord(1/2)
    0.01364399 = product of:
      0.04093197 = sum of:
        0.04093197 = weight(_text_:29 in 20) [ClassicSimilarity], result of:
          0.04093197 = score(doc=20,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.31092256 = fieldWeight in 20, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=20)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

This article presents Gérard Genette's concept of the paratext by defining the term and by describing its characteristics. The use of the concept in disciplines other than literary studies and for media other than printed books is discussed. The last section shows the relevance of the concept for library and information science in general and for knowledge organization, in which paratext in particular is connected to the concept "metadata."

Date

31.10.2020 18:51:29

0.024903167 = product of:
  0.049806334 = sum of:
    0.03628508 = product of:
      0.07257016 = sum of:
        0.07257016 = weight(_text_:media in 318) [ClassicSimilarity], result of:
          0.07257016 = score(doc=318,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.41399965 = fieldWeight in 318, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0625 = fieldNorm(doc=318)
      0.5 = coord(1/2)
    0.013521253 = product of:
      0.04056376 = sum of:
        0.04056376 = weight(_text_:22 in 318) [ClassicSimilarity], result of:
          0.04056376 = score(doc=318,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.30952093 = fieldWeight in 318, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=318)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

In der Session "Knowledge Representation" auf der ISI 2021 wurden unter der Moderation von Jürgen Reischer (Uni Regensburg) drei Projekte vorgestellt, in denen Knowledge Representation mit RDF umgesetzt wird. Die Domänen sind erfreulich unterschiedlich, die gemeinsame Klammer indes ist die Absicht, den Zugang zu Forschungsdaten zu verbessern: - Japanese Visual Media Graph - Taxonomy of Digital Research Activities in the Humanities - Forschungsdaten im konzeptuellen Modell von FRBR

Date

22. 5.2021 12:43:05

0.023567852 = product of:
  0.09427141 = sum of:
    0.09427141 = product of:
      0.18854281 = sum of:
        0.18854281 = weight(_text_:media in 349) [ClassicSimilarity], result of:
          0.18854281 = score(doc=349,freq=24.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            1.0756027 = fieldWeight in 349, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.046875 = fieldNorm(doc=349)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Mass media sentiment of financial news significantly influences investment decisions of investors. Hence, studying how this sentiment emerges is important. In years past, this was straightforward, often dictated by journalists who cover financial news, but this has become more complex now. In this paper, we focus on how social media sentiment affects mass media sentiment. Using data from Sina Weibo and Sina Finance (around 60 million weibos and 6.2 million news articles), we show that social media does influence mass media sentiment emergence for financial news. The sentiment consistency between social media reaction and prior news articles amplifies the persistence of mass media sentiment over time. By contrast, we found limited evidence of social media reducing the persistence of mass media sentiment over time. The results have significant implications for understanding how 2 types of media, treated separately in the literature, may be connected.

0.021715965 = product of:
  0.04343193 = sum of:
    0.026530363 = product of:
      0.053060725 = sum of:
        0.053060725 = weight(_text_:28 in 5906) [ClassicSimilarity], result of:
          0.053060725 = score(doc=5906,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.39578792 = fieldWeight in 5906, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.078125 = fieldNorm(doc=5906)
      0.5 = coord(1/2)
    0.016901568 = product of:
      0.0507047 = sum of:
        0.0507047 = weight(_text_:22 in 5906) [ClassicSimilarity], result of:
          0.0507047 = score(doc=5906,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.38690117 = fieldWeight in 5906, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=5906)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Date

4. 5.2020 17:22:28

0.021715965 = product of:
  0.04343193 = sum of:
    0.026530363 = product of:
      0.053060725 = sum of:
        0.053060725 = weight(_text_:28 in 1001) [ClassicSimilarity], result of:
          0.053060725 = score(doc=1001,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.39578792 = fieldWeight in 1001, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.078125 = fieldNorm(doc=1001)
      0.5 = coord(1/2)
    0.016901568 = product of:
      0.0507047 = sum of:
        0.0507047 = weight(_text_:22 in 1001) [ClassicSimilarity], result of:
          0.0507047 = score(doc=1001,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.38690117 = fieldWeight in 1001, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1001)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Date

28. 2.2020 15:06:34

Source

Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.22-25

0.021522854 = product of:
  0.043045707 = sum of:
    0.03628508 = product of:
      0.07257016 = sum of:
        0.07257016 = weight(_text_:media in 1003) [ClassicSimilarity], result of:
          0.07257016 = score(doc=1003,freq=8.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.41399965 = fieldWeight in 1003, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.03125 = fieldNorm(doc=1003)
      0.5 = coord(1/2)
    0.0067606266 = product of:
      0.02028188 = sum of:
        0.02028188 = weight(_text_:22 in 1003) [ClassicSimilarity], result of:
          0.02028188 = score(doc=1003,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.15476047 = fieldWeight in 1003, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1003)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

Increasingly we live through our personal screens; we work, play, socialize, and learn digitally. The shift to remote everything during the pandemic was another step in a decades-long march toward the digitization of everyday life made possible by innovations in media, information, and communication technology. In The Digital Environment, Pablo Boczkowski and Eugenia Mitchelstein offer a new way to understand the role of the digital in our daily lives, calling on us to turn our attention from our discrete devices and apps to the array of artifacts and practices that make up the digital environment that envelops every aspect of our social experience. Boczkowski and Mitchelstein explore a series of issues raised by the digital takeover of everyday life, drawing on interviews with a variety of experts. They show how existing inequities of gender, race, ethnicity, education, and class are baked into the design and deployment of technology, and describe emancipatory practices that counter this--including the use of Twitter as a platform for activism through such hashtags as #BlackLivesMatter and #MeToo. They discuss the digitization of parenting, schooling, and dating--noting, among other things, that today we can both begin and end relationships online. They describe how digital media shape our consumption of sports, entertainment, and news, and consider the dynamics of political campaigns, disinformation, and social activism. Finally, they report on developments in three areas that will be key to our digital future: data science, virtual reality, and space exploration.

Date

22. 6.2023 18:25:18

LCSH

Digital media / Social aspects

Subject

Digital media / Social aspects

0.020283977 = product of:
  0.08113591 = sum of:
    0.08113591 = product of:
      0.16227181 = sum of:
        0.16227181 = weight(_text_:media in 954) [ClassicSimilarity], result of:
          0.16227181 = score(doc=954,freq=10.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.92573136 = fieldWeight in 954, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0625 = fieldNorm(doc=954)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Ten recommendations for the advanced use of online media. Mit Links auf historische und weiterführende Beiträge.

Content

"1. Use a range of different media 2. Access paywalled media content 3. Use an advertising and tracking blocker 4. Use alternatives to Google Search 5. Use alternatives to YouTube 6. Use alternatives to Facebook and Twitter 7. Caution with Wikipedia 8. Web browser, email, and internet access 9. Access books and scientific papers 10. Access deleted web content"

Source

https://swprs.org/advanced-online-media-use/

0.018723402 = product of:
  0.037446804 = sum of:
    0.02721381 = product of:
      0.05442762 = sum of:
        0.05442762 = weight(_text_:media in 210) [ClassicSimilarity], result of:
          0.05442762 = score(doc=210,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.31049973 = fieldWeight in 210, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.046875 = fieldNorm(doc=210)
      0.5 = coord(1/2)
    0.0102329925 = product of:
      0.030698977 = sum of:
        0.030698977 = weight(_text_:29 in 210) [ClassicSimilarity], result of:
          0.030698977 = score(doc=210,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.23319192 = fieldWeight in 210, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=210)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

Game development artifacts resulting from the creation process of video games, such as design documents, style guides, test builds, and marketing materials, provide rich contextual information about how and why the game was created. Better organizing and preserving these materials will not only enrich our understanding of the history of these media but also educate and inspire the next generation of video game creators. This research aims to improve our theoretical understanding of how to organize and represent game development artifacts by examining the various types of artifacts created and their attendant issues and challenges. We adopted a multimethod approach employing an examination of existing collections and 29 interviews with creators, information professionals, and game researchers. From these data, we analyze the current practices, expressed values, and perceived challenges of these stakeholders, produce a taxonomy of game development artifacts, and provide best practices recommendations for describing them.

0.018723402 = product of:
  0.037446804 = sum of:
    0.02721381 = product of:
      0.05442762 = sum of:
        0.05442762 = weight(_text_:media in 411) [ClassicSimilarity], result of:
          0.05442762 = score(doc=411,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.31049973 = fieldWeight in 411, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.046875 = fieldNorm(doc=411)
      0.5 = coord(1/2)
    0.0102329925 = product of:
      0.030698977 = sum of:
        0.030698977 = weight(_text_:29 in 411) [ClassicSimilarity], result of:
          0.030698977 = score(doc=411,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.23319192 = fieldWeight in 411, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=411)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

As the prevalence of online misinformation grows increasingly apparent, our need to understand its spread becomes more essential. Trolling, in particular, may aggravate the spread of misinformation online. While many studies have investigated the negative impact of trolling and misinformation on social media, less attention has been devoted to the relationships between the two and their manifestation on social question and answer (SQA) sites. We examine the extent of and relationships between trolling and misinformation on SQA sites. Through content analysis of 8,401 posts (159 questions and 8,242 answers) from the Yahoo Answers! Politics & Government and Society & Culture categories, we identified levels of and relationships between misinformation and trolling. We find that trolling and misinformation tend to reinforce themselves and each other and that trolling and misinformation are more common in the Politics & Government category than in the Society & Culture category. Our study is among the first to consider the prevalence of and relationship between misinformation and trolling on SQA sites.

Date

9.11.2021 18:44:29

0.018677374 = product of:
  0.03735475 = sum of:
    0.02721381 = product of:
      0.05442762 = sum of:
        0.05442762 = weight(_text_:media in 887) [ClassicSimilarity], result of:
          0.05442762 = score(doc=887,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.31049973 = fieldWeight in 887, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.046875 = fieldNorm(doc=887)
      0.5 = coord(1/2)
    0.01014094 = product of:
      0.030422818 = sum of:
        0.030422818 = weight(_text_:22 in 887) [ClassicSimilarity], result of:
          0.030422818 = score(doc=887,freq=2.0), product of:
            0.13105336 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.037424255 = queryNorm
            0.23214069 = fieldWeight in 887, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=887)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Abstract

This study investigates how privacy as an ethical concept exists in two languages: Mandarin Chinese and American English. The exploration relies on two genres of corpora from 10 years: social media posts and news articles, 2010-2019. A mixed-methods approach combining structural topic modeling (STM) and human interpretation were used to work with the data. Findings show various privacy-related topics across the two languages. Moreover, some of these different topics revealed fundamental incompatibilities for understanding privacy across these two languages. In other words, some of the variations of topics do not just reflect contextual differences; they reveal how the two languages value privacy in different ways that can relate back to the society's ethical tradition. This study is one of the first empirically grounded intercultural explorations of the concept of privacy. It has shown that natural language is promising to operationalize intercultural and comparative privacy research, and it provides an examination of the concept as it is understood in these two languages.

Date

22. 1.2023 18:59:40

0.01797168 = product of:
  0.03594336 = sum of:
    0.022678176 = product of:
      0.045356352 = sum of:
        0.045356352 = weight(_text_:media in 3940) [ClassicSimilarity], result of:
          0.045356352 = score(doc=3940,freq=2.0), product of:
            0.17529039 = queryWeight, product of:
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.037424255 = queryNorm
            0.25874978 = fieldWeight in 3940, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.6838713 = idf(docFreq=1110, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3940)
      0.5 = coord(1/2)
    0.013265181 = product of:
      0.026530363 = sum of:
        0.026530363 = weight(_text_:28 in 3940) [ClassicSimilarity], result of:
          0.026530363 = score(doc=3940,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.19789396 = fieldWeight in 3940, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3940)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Content

"Ich gehe stark davon aus, dass wir in Zukunft cloudbasierte Rechercheplattformen sehen werden, die folgende Features beinhalten: - Kollaboration und Zusammenarbeit (ähnlich beispielsweise Office 365) in einem Tool über diverse Datenquellen hinweg, - die Möglichkeit, interne und externe Stakeholder zu Recherchen einzuladen bzw. Ergebnisse effektiver zu teilen (Integration von firmeninternen Kommunikationssystemen wie Slack in die Plattform), - Kommentieren und Diskutieren von Dashboards, Charts und Datenpunkten im Social Media Stil, - Aufzeichnen von Recherchen mit "Screen Capture Technologie", - Nutzerprofile im Stile von LinkedIn, die es einfacher machen, Experten in einem Bereich zu identifizieren und eine sichtbare Auszeichnung darstellen, - neue KPI, die den Return on Investment moderner Technologien messen (zum Beispiel Time from Data Ingestion to Shared Insights, Number of People Reached in Company, Number of Insights Generated from Data Source).

Date

28. 1.2020 13:12:00

0.01743414 = product of:
  0.03486828 = sum of:
    0.02122429 = product of:
      0.04244858 = sum of:
        0.04244858 = weight(_text_:28 in 630) [ClassicSimilarity], result of:
          0.04244858 = score(doc=630,freq=2.0), product of:
            0.13406353 = queryWeight, product of:
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.037424255 = queryNorm
            0.31663033 = fieldWeight in 630, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5822632 = idf(docFreq=3342, maxDocs=44218)
              0.0625 = fieldNorm(doc=630)
      0.5 = coord(1/2)
    0.01364399 = product of:
      0.04093197 = sum of:
        0.04093197 = weight(_text_:29 in 630) [ClassicSimilarity], result of:
          0.04093197 = score(doc=630,freq=2.0), product of:
            0.13164683 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.037424255 = queryNorm
            0.31092256 = fieldWeight in 630, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=630)
      0.33333334 = coord(1/3)
  0.5 = coord(2/4)

Date

28. 1.2022 11:05:29