Search (69 results, page 4 of 4)

  • × theme_ss:"Social tagging"
  • × year_i:[2010 TO 2020}
  1. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.00 
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    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
    Type
    a
  2. Xu, C.; Zhang, Q.: ¬The dominant factor of social tags for users' decision behavior on e-commerce websites : color or text (2019) 0.00 
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    Abstract
    Colored Tags (abbr.Tag) as a unique type of social tags is used on e-commerce websites (e.g., Taobao) to summarize the high-frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e-commerce websites and other online platforms.
    Type
    a
  3. Peters, I.; Schumann, L.; Terliesner, J.: Folksonomy-basiertes Information Retrieval unter der Lupe (2012) 0.00 
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  4. Stuart, E.: Flickr: organizing and tagging images online (2019) 0.00 
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  5. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.00 
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  6. Heuwing, B.: Erfahrungen an der Universitätsbibliothek Hildesheim : Social Tagging in Bibliotheken (2010) 0.00 
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  7. Heck, T.: Analyse von sozialen Informationen für Autorenempfehlungen (2012) 0.00 
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  8. Niemann, C.: Intelligenz im Chaos : erste Schritte zur Analyse des Kreativen Potenzials eines Tagging-Systems (2010) 0.00 
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  9. Hänger, C.; Krätzsch, C.; Niemann, C.: Was vom Tagging übrig blieb : Erkenntnisse und Einsichten aus zwei Jahren Projektarbeit (2011) 0.00 
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