Search (10 results, page 1 of 1)

  • × theme_ss:"Internet"
  • × year_i:[2020 TO 2030}
  1. Schrenk, P.: Gesamtnote 1 für Signal - Telegram-Defizite bei Sicherheit und Privatsphäre : Signal und Telegram im Test (2022) 0.03
    0.027254261 = product of:
      0.054508522 = sum of:
        0.03378847 = weight(_text_:data in 486) [ClassicSimilarity], result of:
          0.03378847 = score(doc=486,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.2794884 = fieldWeight in 486, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0625 = fieldNorm(doc=486)
        0.020720055 = product of:
          0.04144011 = sum of:
            0.04144011 = weight(_text_:22 in 486) [ClassicSimilarity], result of:
              0.04144011 = score(doc=486,freq=2.0), product of:
                0.13388468 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03823278 = queryNorm
                0.30952093 = fieldWeight in 486, 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=486)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Date
    22. 1.2022 14:01:14
    Source
    Open Password. 2022, Nr. 1019 vom 21.01.2022 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzM5OSwiYzgwMjA2ZDE4ZWExIiwwLDAsMzYxLDFd]
  2. Si, L.; Zhou, J.: Ontology and linked data of Chinese great sites information resources from users' perspective (2022) 0.01
    0.011805206 = product of:
      0.047220822 = sum of:
        0.047220822 = weight(_text_:data in 1115) [ClassicSimilarity], result of:
          0.047220822 = score(doc=1115,freq=10.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.39059696 = fieldWeight in 1115, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1115)
      0.25 = coord(1/4)
    
    Abstract
    Great Sites are closely related to the residents' life, urban and rural development. In the process of rapid urbanization in China, the protection and utilization of Great Sites are facing unprecedented pressure. Effective knowl­edge organization with ontology and linked data of Great Sites is a prerequisite for their protection and utilization. In this paper, an interview is conducted to understand the users' awareness towards Great Sites to build the user-centered ontology. As for designing the Great Site ontology, firstly, the scope of Great Sites is determined. Secondly, CIDOC- CRM and OWL-Time Ontology are reused combining the results of literature research and user interviews. Thirdly, the top-level structure and the specific instances are determined to extract knowl­edge concepts of Great Sites. Fourthly, they are transformed into classes, data properties and object properties of the Great Site ontology. Later, based on the linked data technology, taking the Great Sites in Xi'an Area as an example, this paper uses D2RQ to publish the linked data set of the knowl­edge of the Great Sites and realize its opening and sharing. Semantic services such as semantic annotation, semantic retrieval and reasoning are provided based on the ontology.
  3. Ding, J.: Can data die? : why one of the Internet's oldest images lives on wirhout its subjects's consent (2021) 0.01
    0.00834754 = product of:
      0.03339016 = sum of:
        0.03339016 = weight(_text_:data in 423) [ClassicSimilarity], result of:
          0.03339016 = score(doc=423,freq=20.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.27619374 = fieldWeight in 423, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.01953125 = fieldNorm(doc=423)
      0.25 = coord(1/4)
    
    Content
    "Having known Lenna for almost a decade, I have struggled to understand what the story of the image means for what tech culture is and what it is becoming. To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns. While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it's outlived its time, or when it's directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data. including allowing their data to die."
  4. Bredemeier, W.: Trend des Jahrzehnts 2011 - 2020 : Die Entfaltung und Degeneration des Social Web (2021) 0.01
    0.0073912274 = product of:
      0.02956491 = sum of:
        0.02956491 = weight(_text_:data in 293) [ClassicSimilarity], result of:
          0.02956491 = score(doc=293,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.24455236 = fieldWeight in 293, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0546875 = fieldNorm(doc=293)
      0.25 = coord(1/4)
    
    Source
    Open Password. 2021, Nr. 876 vom 25.01.2021 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzIyNywiYjhhZTY3YWExMTdjIiwwLDAsMjA1LDFd]
  5. Hong, H.; Ye, Q.: Crowd characteristics and crowd wisdom : evidence from an online investment community (2020) 0.01
    0.0052794483 = product of:
      0.021117793 = sum of:
        0.021117793 = weight(_text_:data in 5763) [ClassicSimilarity], result of:
          0.021117793 = score(doc=5763,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.17468026 = fieldWeight in 5763, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5763)
      0.25 = coord(1/4)
    
    Abstract
    Fueled by the explosive growth of Web 2.0 and social media, online investment communities have become a popular venue for individual investors to interact with each other. Investor opinions extracted from online investment communities capture "crowd wisdom" and have begun to play an important role in financial markets. Existing research confirms the importance of crowd wisdom in stock predictions, but fails to investigate factors influencing crowd performance (that is, crowd prediction accuracy). In order to help improve crowd performance, our research strives to investigate the impact of crowd characteristics on crowd performance. We conduct an empirical study using a large data set collected from a popular online investment community, StockTwits. Our findings show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Furthermore, crowd size moderates the influence of crowd characteristics on crowd performance. From a theoretical perspective, our work enriches extant literature by empirically testing the relationship between crowd characteristics and crowd performance. From a practical perspective, our findings help investors better evaluate social sensors embedded in user-generated stock predictions, based upon which they can make better investment decisions.
  6. Bredemeier, W.: Mit Materialbergen und klarem moralischen Kompass das illegitime Handeln von Facebook aufgedeckt : "Wenn Du Deine Furcht zu sterben überwindest, wird alles möglich. Dies gab mir die Freiheit zu sagen: Will ich meinem Gewissen folgen?" Wie schaffen wir es, ein Stück weit wie Frances Haugen zu werden? (2022) 0.01
    0.0052794483 = product of:
      0.021117793 = sum of:
        0.021117793 = weight(_text_:data in 597) [ClassicSimilarity], result of:
          0.021117793 = score(doc=597,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.17468026 = fieldWeight in 597, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=597)
      0.25 = coord(1/4)
    
    Source
    Open Password. 2022, Nr. 1028 vom 11.02.2022 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzQwNiwiMTY2ZjQ0NjVkNzJhIiwwLDAsMzY4LDFd]
  7. Bredemeier, W.: "Strategische Deökonomisierung und Demokratisierung der Informationszugänge" : Eine Alternative zu Google und den Sozialen Medien? (2022) 0.01
    0.0052794483 = product of:
      0.021117793 = sum of:
        0.021117793 = weight(_text_:data in 598) [ClassicSimilarity], result of:
          0.021117793 = score(doc=598,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.17468026 = fieldWeight in 598, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=598)
      0.25 = coord(1/4)
    
    Source
    Open Password. 2022, Nr. 1077 vom 27.05.2022 [https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzQ2MCwiOTIwMzk1Zjg2YWU1IiwwLDAsNDIwLDFd
  8. Zhang, Y.; Zheng, G.; Yan, H.: Bridging information and communication technology and older adults by social network : an action research in Sichuan, China (2023) 0.01
    0.0052794483 = product of:
      0.021117793 = sum of:
        0.021117793 = weight(_text_:data in 1089) [ClassicSimilarity], result of:
          0.021117793 = score(doc=1089,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.17468026 = fieldWeight in 1089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1089)
      0.25 = coord(1/4)
    
    Abstract
    The extant literature demonstrates that the age-related digital divide prevents older adults from enhancing their quality of life. To bridge this gap and promote active aging, this study explores the interplay between social networks and older adults' use of information and communication technology (ICT). Using an action-oriented field research approach, we offered technical help (29 help sessions) to older adult participants recruited from western China. Then, we conducted content analysis to examine the obtained video, audio, and text data. Our results show that, first, different types of social networks significantly influence older adults' ICT use in terms of digital skills, engagement, and attitudes; however, these effects vary from person to person. In particular, our results highlight the crucial role of a stable and long-term supportive social network in learning and mastering ICT for older residents. Second, technical help facilitates the building and reinforcing of such a social network for the participants. Our study has strong implications in that policymakers can foster the digital inclusion of older people through supportive social networks.
  9. Zhang, L.; Gou, Z.; Fang, Z.; Sivertsen, G.; Huang, Y.: Who tweets scientific publications? : a large-scale study of tweeting audiences in all areas of research (2023) 0.01
    0.0052794483 = product of:
      0.021117793 = sum of:
        0.021117793 = weight(_text_:data in 1189) [ClassicSimilarity], result of:
          0.021117793 = score(doc=1189,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.17468026 = fieldWeight in 1189, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1189)
      0.25 = coord(1/4)
    
    Abstract
    The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.
  10. Aral, S.: ¬The hype machine : how social media disrupts our elections, our economy, and our health - and how we must adapt (2020) 0.00
    0.0042235586 = product of:
      0.016894234 = sum of:
        0.016894234 = weight(_text_:data in 550) [ClassicSimilarity], result of:
          0.016894234 = score(doc=550,freq=2.0), product of:
            0.120893985 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03823278 = queryNorm
            0.1397442 = fieldWeight in 550, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03125 = fieldNorm(doc=550)
      0.25 = coord(1/4)
    
    Abstract
    Social media connected the world--and gave rise to fake news and increasing polarization. Now a leading researcher at MIT draws on 20 years of research to show how these trends threaten our political, economic, and emotional health in this eye-opening exploration of the dark side of technological progress. Today we have the ability, unprecedented in human history, to amplify our interactions with each other through social media. It is paramount, MIT social media expert Sinan Aral says, that we recognize the outsized impact social media has on our culture, our democracy, and our lives in order to steer today's social technology toward good, while avoiding the ways it can pull us apart. Otherwise, we could fall victim to what Aral calls "The Hype Machine." As a senior researcher of the longest-running study of fake news ever conducted, Aral found that lies spread online farther and faster than the truth--a harrowing conclusion that was featured on the cover of Science magazine. Among the questions Aral explores following twenty years of field research: Did Russian interference change the 2016 election? And how is it affecting the vote in 2020? Why does fake news travel faster than the truth online? How do social ratings and automated sharing determine which products succeed and fail? How does social media affect our kids? First, Aral links alarming data and statistics to three accelerating social media shifts: hyper-socialization, personalized mass persuasion, and the tyranny of trends. Next, he grapples with the consequences of the Hype Machine for elections, businesses, dating, and health. Finally, he maps out strategies for navigating the Hype Machine, offering his singular guidance for managing social media to fulfill its promise going forward. Rarely has a book so directly wrestled with the secret forces that drive the news cycle every day"