@InCollection{Chorianopoulos_2013a,
  author        = {Chorianopoulos, Konstantinos and Shamma, David Ayman and Kennedy, Lyndon},
  title         = {{Social Video Retrieval: Research Methods in Controlling, Sharing, and Editing of Web Video}},
  booktitle     = {Social Media Retrieval},
  publisher     = {Springer},
  year          = {2013},
  editor        = {Ramzan, Naeem and van Zwol, Roelof and Lee, Jong-Seok and Cl{\"{u}}ver, Kai and Hua, Xian-Sheng},
  pages         = {3--22},
  abstract      = {Content-based video retrieval has been a very efficient technique with new video content, but it has not
regarded the increasingly dynamic interactions between users and content. We present a comprehensive survey on user-based
techniques and instrumentation for social video retrieval researchers. Community-based approaches suggest there is much to
learn about an unstructured video just by analyzing the dynamics of how it is being used. In particular, we explore three
pillars of online user activity with video content: 1) Seeking patterns within a video is linked to interesting video segments,
2) Sharing patterns between users indicate that there is a correlation between social activity and popularity of a video, and
3) Editing of live events is automated through the synchronization of audio across multiple viewpoints of the same event.
Moreover, we present three complementary research methods in social video retrieval: Experimental replication of user activity
data and signal analysis, data mining and prediction on natural user activity data, and hybrid techniques that combine robust
content-based approaches with crowd sourcing of user gener- ated content. Finally, we suggest further research directions in
the combination of richer user- and content-modeling, because it provides an attractive solution to the personalization,
navigation, and social consumption of videos.},
  url_Paper={Chorianopoulos_2013a.pdf},
  isbn          = {978-1-4471-4555-4},
  keywords      = {human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video},
  mendeley-tags = {human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video},
  url           = {http://link.springer.com/chapter/10.1007/978-1-4471-4555-4{\_}1},
  doi = {10.1007/978-1-4471-4555-4_1},
}

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A.","Kennedy, L."],"year":2013,"bibtype":"incollection","biburl":"https://pdf.epidro.me/publications.bib","bibdata":{"bibtype":"incollection","type":"incollection","author":[{"propositions":[],"lastnames":["Chorianopoulos"],"firstnames":["Konstantinos"],"suffixes":[]},{"propositions":[],"lastnames":["Shamma"],"firstnames":["David","Ayman"],"suffixes":[]},{"propositions":[],"lastnames":["Kennedy"],"firstnames":["Lyndon"],"suffixes":[]}],"title":"Social Video Retrieval: Research Methods in Controlling, Sharing, and Editing of Web Video","booktitle":"Social Media Retrieval","publisher":"Springer","year":"2013","editor":[{"propositions":[],"lastnames":["Ramzan"],"firstnames":["Naeem"],"suffixes":[]},{"propositions":["van"],"lastnames":["Zwol"],"firstnames":["Roelof"],"suffixes":[]},{"propositions":[],"lastnames":["Lee"],"firstnames":["Jong-Seok"],"suffixes":[]},{"propositions":[],"lastnames":["Clüver"],"firstnames":["Kai"],"suffixes":[]},{"propositions":[],"lastnames":["Hua"],"firstnames":["Xian-Sheng"],"suffixes":[]}],"pages":"3–22","abstract":"Content-based video retrieval has been a very efficient technique with new video content, but it has not regarded the increasingly dynamic interactions between users and content. We present a comprehensive survey on user-based techniques and instrumentation for social video retrieval researchers. Community-based approaches suggest there is much to learn about an unstructured video just by analyzing the dynamics of how it is being used. In particular, we explore three pillars of online user activity with video content: 1) Seeking patterns within a video is linked to interesting video segments, 2) Sharing patterns between users indicate that there is a correlation between social activity and popularity of a video, and 3) Editing of live events is automated through the synchronization of audio across multiple viewpoints of the same event. Moreover, we present three complementary research methods in social video retrieval: Experimental replication of user activity data and signal analysis, data mining and prediction on natural user activity data, and hybrid techniques that combine robust content-based approaches with crowd sourcing of user gener- ated content. Finally, we suggest further research directions in the combination of richer user- and content-modeling, because it provides an attractive solution to the personalization, navigation, and social consumption of videos.","url_paper":"Chorianopoulos_2013a.pdf","isbn":"978-1-4471-4555-4","keywords":"human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video","mendeley-tags":"human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video","url":"http://link.springer.com/chapter/10.1007/978-1-4471-4555-4\\_1","doi":"10.1007/978-1-4471-4555-4_1","bibtex":"@InCollection{Chorianopoulos_2013a,\n author = {Chorianopoulos, Konstantinos and Shamma, David Ayman and Kennedy, Lyndon},\n title = {{Social Video Retrieval: Research Methods in Controlling, Sharing, and Editing of Web Video}},\n booktitle = {Social Media Retrieval},\n publisher = {Springer},\n year = {2013},\n editor = {Ramzan, Naeem and van Zwol, Roelof and Lee, Jong-Seok and Cl{\\\"{u}}ver, Kai and Hua, Xian-Sheng},\n pages = {3--22},\n abstract = {Content-based video retrieval has been a very efficient technique with new video content, but it has not\nregarded the increasingly dynamic interactions between users and content. We present a comprehensive survey on user-based\ntechniques and instrumentation for social video retrieval researchers. Community-based approaches suggest there is much to\nlearn about an unstructured video just by analyzing the dynamics of how it is being used. In particular, we explore three\npillars of online user activity with video content: 1) Seeking patterns within a video is linked to interesting video segments,\n2) Sharing patterns between users indicate that there is a correlation between social activity and popularity of a video, and\n3) Editing of live events is automated through the synchronization of audio across multiple viewpoints of the same event.\nMoreover, we present three complementary research methods in social video retrieval: Experimental replication of user activity\ndata and signal analysis, data mining and prediction on natural user activity data, and hybrid techniques that combine robust\ncontent-based approaches with crowd sourcing of user gener- ated content. Finally, we suggest further research directions in\nthe combination of richer user- and content-modeling, because it provides an attractive solution to the personalization,\nnavigation, and social consumption of videos.},\n url_Paper={Chorianopoulos_2013a.pdf},\n isbn = {978-1-4471-4555-4},\n keywords = {human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video},\n mendeley-tags = {human-computer interaction,information retrieval,media technology,methodology,multimedia,survey,video},\n url = {http://link.springer.com/chapter/10.1007/978-1-4471-4555-4{\\_}1},\n doi = {10.1007/978-1-4471-4555-4_1},\n}\n","author_short":["Chorianopoulos, K.","Shamma, D. 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