Search (1512 results, page 1 of 76)

  • × year_i:[2010 TO 2020}
  1. Kumar, S.: Co-authorship networks : a review of the literature (2015) 0.18 
    0.18423879 = product of:
  0.2763582 = sum of:
    0.05296654 = weight(_text_:social in 2586) [ClassicSimilarity], result of:
      0.05296654 = score(doc=2586,freq=2.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.26434162 = fieldWeight in 2586, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=2586)
    0.22339164 = sum of:
      0.18254343 = weight(_text_:networks in 2586) [ClassicSimilarity], result of:
        0.18254343 = score(doc=2586,freq=12.0), product of:
          0.23767339 = queryWeight, product of:
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.050248925 = queryNorm
          0.7680432 = fieldWeight in 2586, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.046875 = fieldNorm(doc=2586)
      0.040848214 = weight(_text_:22 in 2586) [ClassicSimilarity], result of:
        0.040848214 = score(doc=2586,freq=2.0), product of:
          0.17596318 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050248925 = queryNorm
          0.23214069 = fieldWeight in 2586, 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=2586)
  0.6666667 = coord(2/3)
    Abstract
    Purpose - The purpose of this paper is to attempt to provide a review of the growing literature on co-authorship networks and the research gaps that may be investigated for future studies in this field. Design/methodology/approach - The existing literature on co-authorship networks was identified, evaluated and interpreted. Narrative review style was followed. Findings - Co-authorship, a proxy of research collaboration, is a key mechanism that links different sets of talent to produce a research output. Co-authorship could also be seen from the perspective of social networks. An in-depth analysis of such knowledge networks provides an opportunity to investigate its structure. Patterns of these relationships could reveal, for example, the mechanism that shapes our scientific community. The study provides a review of the expanding literature on co-authorship networks. Originality/value - This is one of the first comprehensive reviews of network-based studies on co-authorship. The field is fast evolving, opening new gaps for potential research. The study identifies some of these gaps.
    Date
    20. 1.2015 18:30:22
  2. Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012) 0.16 
    0.15700665 = product of:
  0.23550996 = sum of:
    0.05296654 = weight(_text_:social in 274) [ClassicSimilarity], result of:
      0.05296654 = score(doc=274,freq=2.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.26434162 = fieldWeight in 274, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=274)
    0.18254343 = product of:
      0.36508685 = sum of:
        0.36508685 = weight(_text_:networks in 274) [ClassicSimilarity], result of:
          0.36508685 = score(doc=274,freq=48.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            1.5360864 = fieldWeight in 274, product of:
              6.928203 = tf(freq=48.0), with freq of:
                48.0 = termFreq=48.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=274)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.
  3. Jordan, K.: Separating and merging professional and personal selves online : the structure and process that shape academics' ego-networks on academic social networking sites and Twitter (2019) 0.13 
    0.13450406 = product of:
  0.20175609 = sum of:
    0.11843679 = weight(_text_:social in 5324) [ClassicSimilarity], result of:
      0.11843679 = score(doc=5324,freq=10.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.59108585 = fieldWeight in 5324, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=5324)
    0.0833193 = product of:
      0.1666386 = sum of:
        0.1666386 = weight(_text_:networks in 5324) [ClassicSimilarity], result of:
          0.1666386 = score(doc=5324,freq=10.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.70112437 = fieldWeight in 5324, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=5324)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Academic social networking sites seek to bring the benefits of online networking to an academic audience. The ability to make connections to others is a defining characteristic of the sites, but what types of networks are formed, and what are the implications of the structures? This study addressed that question through mixed methods social network analysis, focusing on Academia.edu, ResearchGate, and Twitter, as three of the main sites used by academics in their professional lives. The structure of academics' ego-networks on social networking sites differs by platform. Networks on academic sites were smaller and more highly clustered, whereas Twitter networks were larger and more diffuse. Institutions and research interests define communities on academic sites, compared with research topics and personal interests on Twitter. The network structures reflect differences in how academics conceptualize different sites and have implications in relation to fostering social capital and research impact.
  4. Goggins, S.P.; Mascaro, C.; Valetto, G.: Group informatics : a methodological approach and ontology for sociotechnical group research (2013) 0.13 
    0.1268515 = product of:
  0.19027725 = sum of:
    0.074906 = weight(_text_:social in 665) [ClassicSimilarity], result of:
      0.074906 = score(doc=665,freq=4.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.3738355 = fieldWeight in 665, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=665)
    0.11537126 = sum of:
      0.07452305 = weight(_text_:networks in 665) [ClassicSimilarity], result of:
        0.07452305 = score(doc=665,freq=2.0), product of:
          0.23767339 = queryWeight, product of:
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.050248925 = queryNorm
          0.31355235 = fieldWeight in 665, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.046875 = fieldNorm(doc=665)
      0.040848214 = weight(_text_:22 in 665) [ClassicSimilarity], result of:
        0.040848214 = score(doc=665,freq=2.0), product of:
          0.17596318 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050248925 = queryNorm
          0.23214069 = fieldWeight in 665, 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=665)
  0.6666667 = coord(2/3)
    Abstract
    We present a methodological approach, called Group Informatics, for understanding the social connections that are created between members of technologically mediated groups. Our methodological approach supports focused thinking about how online groups differ from each other, and diverge from their face-to-face counterparts. Group Informatics is grounded in 5 years of empirical studies of technologically mediated groups in online learning, software engineering, online political discourse, crisis informatics, and other domains. We describe the Group Informatics model and the related, 2-phase methodological approach in detail. Phase one of the methodological approach centers on a set of guiding research questions aimed at directing the application of Group Informatics to new corpora of integrated electronic trace data and qualitative research data. Phase 2 of the methodological approach is a systematic set of steps for transforming electronic trace data into weighted social networks.
    Date
    22. 3.2013 19:36:45
  5. Agosto, D.E.; Abbas, J.; Naughton, R.: Relationships and social rules : teens' social network and other ICT selection practices (2012) 0.12 
    0.1241325 = product of:
  0.18619874 = sum of:
    0.13241635 = weight(_text_:social in 252) [ClassicSimilarity], result of:
      0.13241635 = score(doc=252,freq=18.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.66085404 = fieldWeight in 252, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=252)
    0.05378238 = product of:
      0.10756476 = sum of:
        0.10756476 = weight(_text_:networks in 252) [ClassicSimilarity], result of:
          0.10756476 = score(doc=252,freq=6.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.45257387 = fieldWeight in 252, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=252)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The issue of how teens choose social networks and information communication technologies (ICT's) for personal communication is complex. This study focused on describing how U.S. teens from a highly technological suburban high school select ICT's for personal communication purposes. Two research questions guided the study: (a) What factors influence high school seniors' selection of online social networks and other ICT's for everyday communication? (b) How can social network theory (SNT) help to explain how teens select online social networks and other ICT's for everyday communication purposes? Using focus groups, a purposive sample of 45 teens were asked to discuss (a) their preferred methods for communicating with friends and family and why, (b) the reasons why they chose to engage (or not to engage) in online social networking, (c) how they selected ICT's for social networking and other communication purposes, and (d) how they decided whom to accept as online "friends." Findings indicated that many factors influenced participants' ICT selection practices including six major categories of selection factors: relationship factors, information/communication factors, social factors, systems factors, self-protection factors, and recipient factors. SNT was also helpful in explaining how "friendship" was a major determining factor in their communication media and platform choices.
  6. Nguyen, T.T.; Tho Thanh Quan, T.T.; Tuoi Thi Phan, T.T.: Sentiment search : an emerging trend on social media monitoring systems (2014) 0.12 
    0.12294686 = product of:
  0.18442029 = sum of:
    0.08827756 = weight(_text_:social in 1625) [ClassicSimilarity], result of:
      0.08827756 = score(doc=1625,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.44056937 = fieldWeight in 1625, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1625)
    0.096142724 = sum of:
      0.06210254 = weight(_text_:networks in 1625) [ClassicSimilarity], result of:
        0.06210254 = score(doc=1625,freq=2.0), product of:
          0.23767339 = queryWeight, product of:
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.050248925 = queryNorm
          0.26129362 = fieldWeight in 1625, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1625)
      0.03404018 = weight(_text_:22 in 1625) [ClassicSimilarity], result of:
        0.03404018 = score(doc=1625,freq=2.0), product of:
          0.17596318 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050248925 = queryNorm
          0.19345059 = fieldWeight in 1625, 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=1625)
  0.6666667 = coord(2/3)
    Abstract
    Purpose - The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach - The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings - The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications - The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value - The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.
    Date
    20. 1.2015 18:30:22
  7. Assis, J.; Aparecida Moura, M.: Consensus analysis on the development of meta-languages: : a study of the semantic domain of biotechnology (2014) 0.12 
    0.12285873 = product of:
  0.1842881 = sum of:
    0.06242167 = weight(_text_:social in 1446) [ClassicSimilarity], result of:
      0.06242167 = score(doc=1446,freq=4.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.3115296 = fieldWeight in 1446, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1446)
    0.121866435 = sum of:
      0.08782625 = weight(_text_:networks in 1446) [ClassicSimilarity], result of:
        0.08782625 = score(doc=1446,freq=4.0), product of:
          0.23767339 = queryWeight, product of:
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.050248925 = queryNorm
          0.369525 = fieldWeight in 1446, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            4.72992 = idf(docFreq=1060, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1446)
      0.03404018 = weight(_text_:22 in 1446) [ClassicSimilarity], result of:
        0.03404018 = score(doc=1446,freq=2.0), product of:
          0.17596318 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050248925 = queryNorm
          0.19345059 = fieldWeight in 1446, 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=1446)
  0.6666667 = coord(2/3)
    Abstract
    Knowledge representation and organization and their respective tools and methodologies are based on a model of production and diffusion of knowledge that is currently promoted by diversifying ways of creating, sharing and appropriating knowledge. This study investigated the dimensions of formation and expression of consensus within Biotechnology in order to analyze the possibilities and limits of Consensus Analysis as a methodological tool applied to the knowledge organization. The research explored co-authorship networks and semantic networks derived from the scientific production of the domain. The methodology was established by triangulating method and through theories of Social Network Analysis, Consensus Analysis and the semiotic approach. The freelisting technique was employed for the collection and analysis of concepts belonging to the domain. There is a relationship between the centrality of social actors and thematic centrality. The dynamics of the formation and expression of consensus in the digital context can reveal the configuration of a type of warranty that has not been explored in the literature of knowledge organization yet.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  8. Squicciarini, A.C; Heng Xu, H.; Zhang, X.(L.): CoPE: enabling collaborative privacy management in online social networks (2011) 0.12 
    0.12198377 = product of:
  0.18297565 = sum of:
    0.11843679 = weight(_text_:social in 4128) [ClassicSimilarity], result of:
      0.11843679 = score(doc=4128,freq=10.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.59108585 = fieldWeight in 4128, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=4128)
    0.06453886 = product of:
      0.12907772 = sum of:
        0.12907772 = weight(_text_:networks in 4128) [ClassicSimilarity], result of:
          0.12907772 = score(doc=4128,freq=6.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.5430886 = fieldWeight in 4128, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=4128)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Online Social Networks (OSNs) facilitate the creation and maintenance of interpersonal online relationships. Unfortunately, the availability of personal data on social networks may unwittingly expose users to numerous privacy risks. As a result, establishing effective methods to control personal data and maintain privacy within these OSNs have become increasingly important. This research extends the current access control mechanisms employed by OSNs to protect private information shared among users of OSNs. The proposed approach presents a system of collaborative content management that relies on an extended notion of a "content stakeholder." A tool, Collaborative Privacy Management (CoPE), is implemented as an application within a popular social-networking site, facebook.com, to ensure the protection of shared images generated by users. We present a user study of our CoPE tool through a survey-based study (n=80). The results demonstrate that regardless of whether Facebook users are worried about their privacy, they like the idea of collaborative privacy management and believe that a tool such as CoPE would be useful to manage their personal information shared within a social network.
  9. Zhang, H.; Qiu, B.; Ivanova, K.; Giles, C.L.; Foley, H.C.; Yen, J.: Locality and attachedness-based temporal social network growth dynamics analysis : a case study of evolving nanotechnology scientific collaboration networks (2010) 0.12 
    0.120567515 = product of:
  0.18085127 = sum of:
    0.09869733 = weight(_text_:social in 3455) [ClassicSimilarity], result of:
      0.09869733 = score(doc=3455,freq=10.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.49257156 = fieldWeight in 3455, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3455)
    0.08215394 = product of:
      0.16430788 = sum of:
        0.16430788 = weight(_text_:networks in 3455) [ClassicSimilarity], result of:
          0.16430788 = score(doc=3455,freq=14.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.691318 = fieldWeight in 3455, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3455)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fast-evolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.
  10. Ravindran, T.; Kuan, A.C.Y.; Lian, D.G.H.: Antecedents and effects of social network fatigue (2014) 0.12 
    0.119083814 = product of:
  0.17862572 = sum of:
    0.12484334 = weight(_text_:social in 1528) [ClassicSimilarity], result of:
      0.12484334 = score(doc=1528,freq=16.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.6230592 = fieldWeight in 1528, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1528)
    0.05378238 = product of:
      0.10756476 = sum of:
        0.10756476 = weight(_text_:networks in 1528) [ClassicSimilarity], result of:
          0.10756476 = score(doc=1528,freq=6.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.45257387 = fieldWeight in 1528, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1528)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Guided by literature on "fatigue" from within the domains of clinical and occupational studies, the present article seeks to define the phenomenon termed social network fatigue in the context of one of the popular uses of social networks, namely, to stay socially connected. This is achieved through an identification of the antecedents and effects of experiences that contribute to negative emotions or to a reduction in interest in using social networks with the help of a mixed-methods study. Five generic antecedents and varying effects of these antecedents on individual user activities have been identified. Fatigue experiences could result from social dynamics or social interactions of the members of the community, content made available on social networks, unwanted changes to the platform that hosts the network, self-detected immersive tendencies of the users themselves, or a natural maturing of the life cycle of the community to which the user belongs. The intensity of the fatigue experience varies along a continuum ranging from a mild or transient experience to a more severe experience, which may eventually result in the user's decision to quit the environment that causes stress. Thus, users were found to take short rest breaks from the environment, moderate their activities downward, or suspend their social network activities altogether as a result of fatigue experiences.
  11. Shen, C.; Monge, P.; Williams, D.: ¬The evolution of social ties online : a longitudinal study in a massively multiplayer online game (2014) 0.12 
    0.11826521 = product of:
  0.17739782 = sum of:
    0.1401363 = weight(_text_:social in 1498) [ClassicSimilarity], result of:
      0.1401363 = score(doc=1498,freq=14.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.69938225 = fieldWeight in 1498, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=1498)
    0.037261523 = product of:
      0.07452305 = sum of:
        0.07452305 = weight(_text_:networks in 1498) [ClassicSimilarity], result of:
          0.07452305 = score(doc=1498,freq=2.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.31355235 = fieldWeight in 1498, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=1498)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    How do social ties in online worlds evolve over time? This research examined the dynamic processes of relationship formation, maintenance, and demise in a massively multiplayer online game. Drawing from evolutionary and ecological theories of social networks, this study focuses on the impact of three sets of evolutionary factors in the context of social relationships in the online game EverQuest II (EQII): the aging and maturation processes, social architecture of the game, and homophily and proximity. A longitudinal analysis of tie persistence and decay demonstrated the transient nature of social relationships in EQII, but ties became considerably more durable over time. Also, character level similarity, shared guild membership, and geographic proximity were powerful mechanisms in preserving social relationships.
  12. Liu, Y.; Du, F.; Sun, J.; Silva, T.; Jiang, Y.; Zhu, T.: Identifying social roles using heterogeneous features in online social networks (2019) 0.11 
    0.11408837 = product of:
  0.17113255 = sum of:
    0.11843679 = weight(_text_:social in 5293) [ClassicSimilarity], result of:
      0.11843679 = score(doc=5293,freq=10.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.59108585 = fieldWeight in 5293, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=5293)
    0.052695755 = product of:
      0.10539151 = sum of:
        0.10539151 = weight(_text_:networks in 5293) [ClassicSimilarity], result of:
          0.10539151 = score(doc=5293,freq=4.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.44343 = fieldWeight in 5293, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=5293)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Role analysis plays an important role when exploring social media and knowledge-sharing platforms for designing marking strategies. However, current methods in role analysis have overlooked content generated by users (e.g., posts) in social media and hence focus more on user behavior analysis. The user-generated content is very important for characterizing users. In this paper, we propose a novel method which integrates both user behavior and posted content by users to identify roles in online social networks. The proposed method models a role as a joint distribution of Gaussian distribution and multinomial distribution, which represent user behavioral feature and content feature respectively. The proposed method can be used to determine the number of roles concerned automatically. The experimental results show that the proposed method can be used to identify various roles more effectively and to get more insights on such characteristics.
  13. 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) 0.11 
    0.11328089 = product of:
  0.16992134 = sum of:
    0.15290125 = weight(_text_:social in 2598) [ClassicSimilarity], result of:
      0.15290125 = score(doc=2598,freq=24.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.7630886 = fieldWeight in 2598, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2598)
    0.01702009 = product of:
      0.03404018 = sum of:
        0.03404018 = weight(_text_:22 in 2598) [ClassicSimilarity], result of:
          0.03404018 = score(doc=2598,freq=2.0), product of:
            0.17596318 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050248925 = queryNorm
            0.19345059 = fieldWeight in 2598, 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=2598)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    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.
  14. Meier, F.: Informationsverhalten in Social Media (2015) 0.11 
    0.1123175 = product of:
  0.16847625 = sum of:
    0.14124411 = weight(_text_:social in 1739) [ClassicSimilarity], result of:
      0.14124411 = score(doc=1739,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.704911 = fieldWeight in 1739, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0625 = fieldNorm(doc=1739)
    0.027232144 = product of:
      0.054464288 = sum of:
        0.054464288 = weight(_text_:22 in 1739) [ClassicSimilarity], result of:
          0.054464288 = score(doc=1739,freq=2.0), product of:
            0.17596318 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050248925 = queryNorm
            0.30952093 = fieldWeight in 1739, 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=1739)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    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
  15. Schumacher, S.: ¬Die psychologischen Grundlagen des Social-Engineerings (2014) 0.11 
    0.1123175 = product of:
  0.16847625 = sum of:
    0.14124411 = weight(_text_:social in 1744) [ClassicSimilarity], result of:
      0.14124411 = score(doc=1744,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.704911 = fieldWeight in 1744, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0625 = fieldNorm(doc=1744)
    0.027232144 = product of:
      0.054464288 = sum of:
        0.054464288 = weight(_text_:22 in 1744) [ClassicSimilarity], result of:
          0.054464288 = score(doc=1744,freq=2.0), product of:
            0.17596318 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050248925 = queryNorm
            0.30952093 = fieldWeight in 1744, 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=1744)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Social-Engineering ist eine Angriffsstrategie, die nicht die Technik als Opfer auserkoren hat. Stattdessen wird hier viel lieber - und vor allem effizienter - der Mensch bzw. sein Verhalten angegriffen. Dieser Artikel zeigt, wie Social-Engineering funktioniert und erklärt die zugrunde liegenden Tricks anhand sozialpsychologischer Studien und Experimente. Außerdem werden Beispiele, Warnsignale und Gegenmaßnahmen vorgestellt. Er richtet sich an Sicherheitsverantwortliche und Systemadministratoren, die verstehen wollen, wie Social-Engineering funktioniert, und dieses Wissen in ihre Sicherheitsmaßnahmen integrieren wollen.
    Date
    22. 9.2014 18:52:13
  16. Zhitomirsky-Geffet, M.; Bratspiess, Y.: Professional information disclosure on social networks : the case of Facebook and LinkedIn in Israel (2016) 0.11 
    0.10955822 = product of:
  0.16433732 = sum of:
    0.08827756 = weight(_text_:social in 2766) [ClassicSimilarity], result of:
      0.08827756 = score(doc=2766,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.44056937 = fieldWeight in 2766, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2766)
    0.07605976 = product of:
      0.15211952 = sum of:
        0.15211952 = weight(_text_:networks in 2766) [ClassicSimilarity], result of:
          0.15211952 = score(doc=2766,freq=12.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.640036 = fieldWeight in 2766, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2766)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Disclosure of personal information on social networks has been extensively researched in recent years from different perspectives, including the influence of demographic, personality, and social parameters on the extent and type of disclosure. However, although some of the most widespread uses of these networks nowadays are for professional, academic, and business purposes, a thorough investigation of professional information disclosure is still needed. This study's primary aim, therefore, is to conduct a systematic and comprehensive investigation into patterns of professional information disclosure and various factors involved on different types of social networks. To this end, a user survey was conducted. We focused specifically on Facebook and LinkedIn, the 2 diverse networks most widely used in Israel. Significant differences were found between these networks. For example, we found that on Facebook professional pride is a factor in professional information disclosure, whereas on LinkedIn, work seniority and income have a significant effect. Thus, our findings shed light on the attitudes and professional behavior of network members, leading to recommendations regarding advertising strategies and network-appropriate self-presentation, as well as approaches that companies might adopt according to the type of manpower required.
  17. Veinot, T.: ¬A multilevel model of HIV/AIDS information/help network development (2010) 0.11 
    0.10951792 = product of:
  0.16427687 = sum of:
    0.07645062 = weight(_text_:social in 4155) [ClassicSimilarity], result of:
      0.07645062 = score(doc=4155,freq=6.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.3815443 = fieldWeight in 4155, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4155)
    0.08782625 = product of:
      0.1756525 = sum of:
        0.1756525 = weight(_text_:networks in 4155) [ClassicSimilarity], result of:
          0.1756525 = score(doc=4155,freq=16.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.73905 = fieldWeight in 4155, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4155)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Purpose - This paper aims to describe the personal information and help networks of people with HIV/AIDS (PHAs) in rural Canada, and to present a research-based model of how and why these networks developed. This model seeks to consider the roles of PHAs, their family members/friends and formal health systems in network formation. Design/methodology/approach - In-depth, semi-structured interviews were conducted with 114 PHAs, their friends/family members (FFs) and formal caregivers in three rural regions of Canada. A network solicitation procedure elicited PHAs' HIV/AIDS information/help networks. Interviews were analyzed qualitatively, and network data were analyzed statistically. Documents describing health systems in each region were also analyzed. Analyses used social capital theory, supplemented by stress/coping and stigma management theories. Findings - PHAs' HIV/AIDS-related information/help networks emphasized linking and bonding social capital with minimal bridging social capital. This paper presents a model that explains how and why such networks developed. The model shows that networks grew from the actions of PHAs, their FFs and health systems. PHAs experienced considerable stress, which led them to develop information/help networks to cope with HIV/AIDS - both individually and collaboratively. Because of stigmatization, many PHAs disclosed their illness selectively, thus constraining the size and composition of their networks. Health system actors created network-building opportunities for PHAs by providing them with care, referrals and support programs. Originality/value - This study describes and explains an understudied type of information behavior: information/help network development at individual, group and institutional levels. As such, it illuminates the complex dynamics that made individual acts of interpersonal information acquisition and sharing possible.
  18. Brown, S.A.; Dennis, A.R.; Burley, D.; Arling, P.: Knowledge sharing and knowledge management system avoidance : the role of knowledge type and the social network in bypassing an organizational knowledge management system (2013) 0.11 
    0.10575256 = product of:
  0.15862884 = sum of:
    0.10593308 = weight(_text_:social in 1099) [ClassicSimilarity], result of:
      0.10593308 = score(doc=1099,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.52868325 = fieldWeight in 1099, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=1099)
    0.052695755 = product of:
      0.10539151 = sum of:
        0.10539151 = weight(_text_:networks in 1099) [ClassicSimilarity], result of:
          0.10539151 = score(doc=1099,freq=4.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.44343 = fieldWeight in 1099, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=1099)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Knowledge sharing is a difficult task for most organizations, and there are many reasons for this. In this article, we propose that the nature of the knowledge shared and an individual's social network influence employees to find more value in person-to-person knowledge sharing, which could lead them to bypass the codified knowledge provided by a knowledge management system (KMS). We surveyed employees of a workman's compensation board in Canada and used social network analysis and hierarchical linear modeling to analyze the data. The results show that knowledge complexity and knowledge teachability increased the likelihood of finding value in person-to-person knowledge transfer, but knowledge observability did not. Contrary to expectations, whether the knowledge was available in the KMS had no impact on the value of person-to-person knowledge transfer. In terms of the social network, individuals with larger networks tended to perceive more value in the person-to-person transfer of knowledge than those with smaller networks.
  19. Chen, Y.-L.; Chuang, C.-H.; Chiu, Y.-T.: Community detection based on social interactions in a social network (2014) 0.11 
    0.10575256 = product of:
  0.15862884 = sum of:
    0.10593308 = weight(_text_:social in 1219) [ClassicSimilarity], result of:
      0.10593308 = score(doc=1219,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.52868325 = fieldWeight in 1219, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=1219)
    0.052695755 = product of:
      0.10539151 = sum of:
        0.10539151 = weight(_text_:networks in 1219) [ClassicSimilarity], result of:
          0.10539151 = score(doc=1219,freq=4.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.44343 = fieldWeight in 1219, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.046875 = fieldNorm(doc=1219)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.
  20. Girdhar, N.; Bharadwaj, K.K.: Community detection in signed social networks using multiobjective genetic algorithm (2019) 0.11 
    0.10514021 = product of:
  0.15771031 = sum of:
    0.08827756 = weight(_text_:social in 5318) [ClassicSimilarity], result of:
      0.08827756 = score(doc=5318,freq=8.0), product of:
        0.20037155 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.050248925 = queryNorm
        0.44056937 = fieldWeight in 5318, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5318)
    0.06943275 = product of:
      0.1388655 = sum of:
        0.1388655 = weight(_text_:networks in 5318) [ClassicSimilarity], result of:
          0.1388655 = score(doc=5318,freq=10.0), product of:
            0.23767339 = queryWeight, product of:
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.050248925 = queryNorm
            0.5842703 = fieldWeight in 5318, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.72992 = idf(docFreq=1060, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5318)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Clustering of like-minded users is basically the goal of community detection (CD) in social networks and many researchers have proposed different algorithms for the same. In signed social networks (SSNs) where type of link is also considered besides the links itself, CD aims to partition the network in such a way to have less positive inter-connections and less negative intra-connections among communities. So, approaches used for CD in unsigned networks do not perform well when directly applied on signed networks. Most of the CD algorithms are based on single objective optimization criteria of optimizing modularity which focuses only on link density without considering the type of links existing in the network. In this work, a multiobjective approach for CD in SSNs is proposed considering both the link density as well as the sign of links. Precisely we are developing a method using modularity, frustration and social balance factor as multiple objectives to be optimized (M-F-SBF model). NSGA-II algorithm is used to maintain elitism and diversity in the solutions. Experiments are performed on both existing benchmarked and real-world datasets show that our approach has led to better solutions, clearly indicating the effectiveness of our proposed M-F-SBF model.

Authors

Languages

  • e 1238
  • d 256
  • a 1
  • f 1
  • hu 1
  • i 1
  • m 1
  • pt 1
  • More… Less…

Types

  • a 1312
  • m 137
  • el 108
  • s 54
  • x 17
  • r 8
  • b 5
  • i 2
  • z 1
  • More… Less…

Themes

Subjects

Classifications