David A. Shamma<br />Internet Experiences<br />Yahoo! Research<br />A movie and a chat…<br />http://www.flickr.com/photos/...
Internet Experiences GroupYahoo! Research<br />(David) AymanShamma<br />Lyndon Kennedy<br />Elizabeth Churchill<br />
Traditional Video<br />
Traditional Comments and Tags<br />Left in Whole, Unattached.<br />
Some tags can be added as annotations<br />Post annotations don’t allow for real time commentary.<br />This part is intere...
Social Conversations happen around videos<br />Well – actually people join in a session and converse afterwards.<br />
What to Collect to measure engagement?<br />Type of event (Zync player command or a normal chat message)<br />Anonymous ha...
A Short Movie<br />
Percent of actions over time.<br />
Volume of actions over time.<br />
Chat follows the video!<br />CHAT<br />
Core Stats (opt-in)<br />April 1, 2009 through April 7, 2009 (inclusive).<br />3.25 million events (URLs, chat, volumes, p...
Reciprocity<br />43.6% of the sessions the invitee played at least one video back to the session’s initiator.<br />77.7% s...
Social Actions and Live Performance<br />DJs manage three social networks through group of mediums like: MySpace, Webcasts...
Y!Live<br />
Y!Live<br />
Y!Live<br />
Much of Social Media is about Congregation<br />Something we think about at CHI and CSCW and should think about at WWW and...
A new form of indirect media-object annotation.<br />中国没有Twitter<br />
People Tweet While They Watch<br />
@kanye dude, not cool #vma<br />Tweeting while watching offers implicit event annotation…one in need of media reification....
CurrentTV: Hack the Debate<br />
a Tweet<br />RT: @jowyang If you are watching the debate you’re<br />invited to participate in #tweetdebate Here is the 41...
Anatomy of a Tweet<br />Repeated (retweet) content starts with RT<br />Address other users with an @<br />RT: @jowyang If ...
Indirect Annotation<br />Sept 26, 2009 18:23 EST<br />RT: @jowyang If you are watching the debate you’re<br />invited to p...
Tweet Crawl<br />Three hashtags: #current #debate08 #tweetdebate<br />97 mins debate + 53 mins following = 2.5 hours total...
Volume of Tweets by Minute<br />Crawled from the Twitter RESTful search API.<br />
Tweets During and After the Debates<br />Conversation swells after the debate.<br />
Volume of Conversation Follows the Debate<br />Post debate<br />
Does Conversation follow After a Segment<br />Think of Isaac Newton<br />Post Segment?<br />
Will the roots of f’(x) find segmentation markers?<br />
Automatic Segment Detection<br />We use Newton’s Method to find extrema outside μ±σ to find candidate markers. Any marker ...
Automatic Segment Detection with 92% Accuracy<br />When compared to CSPAN’s editorialized Debate Summary ± 1 minute.<br />
Tags As Boundary Objects<br />
Directed Communication via @mentions<br />John Tweets: “Hey @mary, my person is winning!” Makes a directed graph from John...
Barack, NewsHour, & McCain automatically discovered.<br />High Eigenvector Centrality Figures on Twitter from the First US...
Sinks in the network<br />High in degree but poor centrality.<br />
Tweets to Terms<br />Common stems in bold-italic.<br />
Tweets are Reaction not Content<br />
What if we look at other events?<br />What can we find?<br />
Argentina v England (1986 FIFA World Cup quarter-final)<br />
Nooo!  #worldcup #hand<br />Event Onset!<br />
July 20, 1969 Apollo 11 Moon Landing<br />
omg! @nasarukddng? #landing #fake #moon<br />Tags as boundary objects can find communities & sets.<br />
Godzilla attacking the Tokyo<br />
やばい!国会議事堂をつぶしている!@radonがんばって!#gojira<br />Tweet Content Comprehension Need Not Be Needed.<br />
Inauguration 2009<br />http://www.flickr.com/photos/twistedart/3212723019/<br />
Data Mining Feed<br />53,000 Tweets @ 600 per minutes<br />
Drop in @conversation as onset<br />
Drop in @conversation as onset (inaug subset)<br />
Less @ means less chars<br />
Terms as topics<br />Using a TF/IDF window of 5 mins<br />
Sustained Interest<br />Some topics continue over time with a higher conversational context.<br />
People Announce<br />(12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human!<br />(12:07) ryanthero...
People Reply<br />(12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human!<br />(12:07) ryantherobot...
HCC and MM and Other Work<br />Indirect annotation through community action<br />Uncollected Sources (read: events) are hi...
Statler<br />http://bit.ly/statler<br />
Thanks Chloe S., Ben C., Marc S., M. Cameron J., Ryan S., & NodeXL<br />Tweet the Debates: Understanding Community Annotat...
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A Movie And A Chat

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From my talk at CWI in Amsterdam: http://bit.ly/2i65R4

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  • http://www.flickr.com/photos/wvs/3833148925/This is a three part talk where I’ll discuss IM, Chatrooms, and Twitter.
  • There is MORE to tagging and comments in social media than how we think of it currently as the single browser/site/startup.
  • These tags and comments are regulated to anchored explicit annotation. This is the problem. Temporally, there is a gap – we cannot leverage these components like we have with photos.
  • Several sites (including YouTube and my own past research) tried to make deep comments prevalent.
  • Look for people
  • Look at chat.
  • Look at people.It’s a scanning pattern not about people’s movements in the room but rather activity that happens spatially.The bathroom break part is not observed explicitly aside from a “BRB” or an empty camera frame where we observed via participation.
  • Enter Twitter. (explain it quickly) With twitter, when something happens and you wanna shout, you tweet.
  • Many People Tweet while they watch tv, many TV shows call for people to follow the twitter stream.
  • (this is a fake tweet)
  • Not only of the tweet to the video but the rich data within the tweet.
  • Some techniques from may be applicable: Wei Hao Lin, Alexander Haputmann: Identifying News Videos ideological viewpoint or bias
  • A Movie And A Chat

    1. David A. Shamma<br />Internet Experiences<br />Yahoo! Research<br />A movie and a chat…<br />http://www.flickr.com/photos/wvs/3833148925/<br />
    2. Internet Experiences GroupYahoo! Research<br />(David) AymanShamma<br />Lyndon Kennedy<br />Elizabeth Churchill<br />
    3. Traditional Video<br />
    4. Traditional Comments and Tags<br />Left in Whole, Unattached.<br />
    5. Some tags can be added as annotations<br />Post annotations don’t allow for real time commentary.<br />This part is interesting.<br />
    6. Social Conversations happen around videos<br />Well – actually people join in a session and converse afterwards.<br />
    7. What to Collect to measure engagement?<br />Type of event (Zync player command or a normal chat message)<br />Anonymous hash (uniquely identifies the sender and the receiver, without exposing personal account data)<br />URL to the shared video<br />Timestamp for the event<br />The player time(with respect to the specific video) at the point the event occurred<br />The number of characters and the number words typed (for chat messages)<br />Emoticons used in the chat message<br />
    8. A Short Movie<br />
    9. Percent of actions over time.<br />
    10. Volume of actions over time.<br />
    11. Chat follows the video!<br />CHAT<br />
    12. Core Stats (opt-in)<br />April 1, 2009 through April 7, 2009 (inclusive).<br />3.25 million events (URLs, chat, volumes, pause events). <br />24,258 users • 24,506 sessions<br />Of these users, 35.29% (μ=2:02, σ= 2:72,σ2= 7:40) of the users engaged in more than one session during that week<br />76,762 URLs, 23% shared in more than one session.<br />Over 99% of the shared videos came from YouTube.<br />Approximately 2% of all the URLs sent within Yahoo! Messenger at-large.<br />
    13. Reciprocity<br />43.6% of the sessions the invitee played at least one video back to the session’s initiator.<br />77.7% sharing reciprocation<br />Pairs of people often exchanged more than one set of videos in a session.<br />In the categories of Nonprofit, Technology and Shows, the invitees shared more videos to the initiator (5:4, 9:7, and 5:2 respectably).<br />
    14. Social Actions and Live Performance<br />DJs manage three social networks through group of mediums like: MySpace, Webcasts, Twitter, Facebook, and IM.<br />
    15. Y!Live<br />
    16. Y!Live<br />
    17. Y!Live<br />
    18. Much of Social Media is about Congregation<br />Something we think about at CHI and CSCW and should think about at WWW and MM.<br />(if you are so definition inclined you can enjoy the above paste)<br />
    19. A new form of indirect media-object annotation.<br />中国没有Twitter<br />
    20. People Tweet While They Watch<br />
    21. @kanye dude, not cool #vma<br />Tweeting while watching offers implicit event annotation…one in need of media reification.<br />
    22. CurrentTV: Hack the Debate<br />
    23. a Tweet<br />RT: @jowyang If you are watching the debate you’re<br />invited to participate in #tweetdebate Here is the 411<br />http://tinyurl.com/3jdy67<br />
    24. Anatomy of a Tweet<br />Repeated (retweet) content starts with RT<br />Address other users with an @<br />RT: @jowyang If you are watching the debate you’re<br />invited to participate in #tweetdebate Here is the 411<br />http://tinyurl.com/3jdy67<br />Rich Media embeds via links<br />Tags start with #<br />
    25. Indirect Annotation<br />Sept 26, 2009 18:23 EST<br />RT: @jowyang If you are watching the debate you’re<br />invited to participate in #tweetdebate Here is the 411<br />http://tinyurl.com/3jdy67<br />
    26. Tweet Crawl<br />Three hashtags: #current #debate08 #tweetdebate<br />97 mins debate + 53 mins following = 2.5 hours total. <br />3,238 tweets from 1,160 people.<br />1,824 tweets from 647 people during the debate.<br />1,414 tweets from 738 people post debate.<br />577 @ mentions (reciprocity!)<br />266 mentions during the debate<br />311 afterwards.<br />Low RT: 24 retweetsin total<br />6 during<br />18 afterwards.<br />
    27. Volume of Tweets by Minute<br />Crawled from the Twitter RESTful search API.<br />
    28. Tweets During and After the Debates<br />Conversation swells after the debate.<br />
    29. Volume of Conversation Follows the Debate<br />Post debate<br />
    30. Does Conversation follow After a Segment<br />Think of Isaac Newton<br />Post Segment?<br />
    31. Will the roots of f’(x) find segmentation markers?<br />
    32. Automatic Segment Detection<br />We use Newton’s Method to find extrema outside μ±σ to find candidate markers. Any marker that follows from the a marker on the previous minute is ignored.<br />
    33. Automatic Segment Detection with 92% Accuracy<br />When compared to CSPAN’s editorialized Debate Summary ± 1 minute.<br />
    34. Tags As Boundary Objects<br />
    35. Directed Communication via @mentions<br />John Tweets: “Hey @mary, my person is winning!” Makes a directed graph from John to Mary.<br />
    36. Barack, NewsHour, & McCain automatically discovered.<br />High Eigenvector Centrality Figures on Twitter from the First US Presidential Debate of 2008.<br />
    37. Sinks in the network<br />High in degree but poor centrality.<br />
    38. Tweets to Terms<br />Common stems in bold-italic.<br />
    39. Tweets are Reaction not Content<br />
    40. What if we look at other events?<br />What can we find?<br />
    41. Argentina v England (1986 FIFA World Cup quarter-final)<br />
    42. Nooo! #worldcup #hand<br />Event Onset!<br />
    43. July 20, 1969 Apollo 11 Moon Landing<br />
    44. omg! @nasarukddng? #landing #fake #moon<br />Tags as boundary objects can find communities & sets.<br />
    45. Godzilla attacking the Tokyo<br />
    46. やばい!国会議事堂をつぶしている!@radonがんばって!#gojira<br />Tweet Content Comprehension Need Not Be Needed.<br />
    47. Inauguration 2009<br />http://www.flickr.com/photos/twistedart/3212723019/<br />
    48. Data Mining Feed<br />53,000 Tweets @ 600 per minutes<br />
    49. Drop in @conversation as onset<br />
    50. Drop in @conversation as onset (inaug subset)<br />
    51. Less @ means less chars<br />
    52. Terms as topics<br />Using a TF/IDF window of 5 mins<br />
    53. Sustained Interest<br />Some topics continue over time with a higher conversational context.<br />
    54. People Announce<br />(12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human!<br />(12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo!<br />(12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo<br />(12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.<br />
    55. People Reply<br />(12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human!<br />(12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo!<br />(12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo<br />(12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.<br />
    56. HCC and MM and Other Work<br />Indirect annotation through community action<br />Uncollected Sources (read: events) are highly valuable<br />Segmentation, Figure Identification, Term Distance<br />Bigrams? Real Time?<br />
    57. Statler<br />http://bit.ly/statler<br />
    58. Thanks Chloe S., Ben C., Marc S., M. Cameron J., Ryan S., & NodeXL<br />Tweet the Debates: Understanding Community Annotation of Uncollected Sources David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, ACM Multimedia, ACM, 2009<br />Understanding the Creative Conversation: Modeling to Engagement David A. Shamma; Dan Perkel; Kurt Luther, Creativity and Cognition, ACM, 2009<br />Spinning Online: A Case Study of Internet Broadcasting by DJs David A. Shamma; Elizabeth Churchill; Nikhil Bobb; Matt Fukuda, Communities & Technology, ACM, 2009<br />Zync with Me: Synchronized Sharing of Video through Instant Messaging David A. Shamma; Yiming Liu; Pablo Cesar, David Geerts, KonstantinosChorianopoulos, Social Interactive Television: Immersive Shared Experiences and Perspectives, Information Science Reference, IGI Global, 2009<br />Enhancing online personal connections through the synchronized sharing of online video Shamma, D. A.; Bastéa-Forte, M.; Joubert, N.; Liu, Y., Human Factors in Computing Systems (CHI), ACM, 2008<br />Supporting creative acts beyond dissemination David A. Shamma; Ryan Shaw, Creativity and Cognition, ACM, 2007<br />Watch what I watch: using community activity to understand content David A. Shamma; Ryan Shaw; Peter Shafton; Yiming Liu, ACM Multimedia Workshop on Multimedia Information Retrival (MIR), ACM, 2007<br />Zync: the design of synchronized video sharing Yiming Liu; David A. Shamma; Peter Shafton; Jeannie Yang, Designing for User eXperiences, ACM, 2007<br />

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