Catching a Viral Video
The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to...
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creator | Broxton, T Interian, Y Vaver, J Wattenhofer, M |
description | The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to video popularity using 1.5 million You Tube videos. The social ness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link) or non-social (e.g. a link from related videos). By segmenting videos according to their fraction of social views, we find that highly social videos behave differently than less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. And, despite their ability to generate large volumes of views over a short period of time, only 21% of the most popular videos (in terms of 30-day views) can be classified as viral. The observations made here lay the ground work for future work related to the creation of classification and predictive models for online videos. |
doi_str_mv | 10.1109/ICDMW.2010.160 |
format | Conference Proceeding |
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In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to video popularity using 1.5 million You Tube videos. The social ness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link) or non-social (e.g. a link from related videos). By segmenting videos according to their fraction of social views, we find that highly social videos behave differently than less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. And, despite their ability to generate large volumes of views over a short period of time, only 21% of the most popular videos (in terms of 30-day views) can be classified as viral. 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The observations made here lay the ground work for future work related to the creation of classification and predictive models for online videos.</description><subject>Blogs</subject><subject>Facebook</subject><subject>Media</subject><subject>Twitter</subject><subject>viral videos</subject><subject>Watches</subject><subject>YouTube</subject><issn>2375-9232</issn><issn>2375-9259</issn><isbn>9781424492442</isbn><isbn>1424492440</isbn><isbn>9780769542577</isbn><isbn>0769542573</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9jEtPwzAQhM1Loiq59sKlfyDF3s16s0cUKFQq4lLBsVrHNgSVh5Je-PeEhxhpZjT6pDFm5uzCOSsXq-bq7nEB9nt7e2AK4dqyF6qAmA_NBJCpFCA5-mGugqqS0XD8zxBOTTEML3YUATPTxMwa3bfP3dvTXOcPXa-7MWN6PzMnWXdDKv56ajbL601zW67vb1bN5brsxO5LEl-TpvEuqgUHjDm5KNlBzQEkEGGkYFvBJFnrHGLQBK0SBR9azTg157-3XUpp-9F3r9p_bskLokP8AtGePoU</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Broxton, T</creator><creator>Interian, Y</creator><creator>Vaver, J</creator><creator>Wattenhofer, M</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201012</creationdate><title>Catching a Viral Video</title><author>Broxton, T ; Interian, Y ; Vaver, J ; Wattenhofer, M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-59685ae000da021273fe1d9f1287b29b553d5b0c93e9fa8fbdbae2ca55b6bcaf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Blogs</topic><topic>Facebook</topic><topic>Media</topic><topic>Twitter</topic><topic>viral videos</topic><topic>Watches</topic><topic>YouTube</topic><toplevel>online_resources</toplevel><creatorcontrib>Broxton, T</creatorcontrib><creatorcontrib>Interian, Y</creatorcontrib><creatorcontrib>Vaver, J</creatorcontrib><creatorcontrib>Wattenhofer, M</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Broxton, T</au><au>Interian, Y</au><au>Vaver, J</au><au>Wattenhofer, M</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Catching a Viral Video</atitle><btitle>2010 IEEE International Conference on Data Mining Workshops</btitle><stitle>icdmw</stitle><date>2010-12</date><risdate>2010</risdate><spage>296</spage><epage>304</epage><pages>296-304</pages><issn>2375-9232</issn><eissn>2375-9259</eissn><isbn>9781424492442</isbn><isbn>1424492440</isbn><eisbn>9780769542577</eisbn><eisbn>0769542573</eisbn><abstract>The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to video popularity using 1.5 million You Tube videos. The social ness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link) or non-social (e.g. a link from related videos). By segmenting videos according to their fraction of social views, we find that highly social videos behave differently than less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. And, despite their ability to generate large volumes of views over a short period of time, only 21% of the most popular videos (in terms of 30-day views) can be classified as viral. 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ispartof | 2010 IEEE International Conference on Data Mining Workshops, 2010, p.296-304 |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Blogs Media viral videos Watches YouTube |
title | Catching a Viral Video |
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