7. What to Collect to measure engagement? Type of event (Zync player command or a normal chat message) Anonymous hash (uniquely identifies the sender and the receiver, without exposing personal account data) URL to the shared video Timestamp for the event The player time(with respect to the specific video) at the point the event occurred The number of characters and the number words typed (for chat messages) Emoticons used in the chat message
12. Core Stats (opt-in) April 1, 2009 through April 7, 2009 (inclusive). 3.25 million events (URLs, chat, volumes, pause events). 24,258 users • 24,506 sessions 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 76,762 URLs, 23% shared in more than one session. Over 99% of the shared videos came from YouTube. Approximately 2% of all the URLs sent within Yahoo! Messenger at-large.
13. Reciprocity 43.6% of the sessions the invitee played at least one video back to the session’s initiator. 77.7% sharing reciprocation Pairs of people often exchanged more than one set of videos in a session. In the categories of Nonprofit, Technology and Shows, the invitees shared more videos to the initiator (5:4, 9:7, and 5:2 respectably).
14. Social Actions and Live Performance DJs manage three social networks through group of mediums like: MySpace, Webcasts, Twitter, Facebook, and IM.
18. Much of Social Media is about Congregation Something we think about at CHI and CSCW and should think about at WWW and MM. (if you are so definition inclined you can enjoy the above paste)
19. A new form of indirect media-object annotation. 中国没有Twitter
23. a Tweet RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67
24. Anatomy of a Tweet Repeated (retweet) content starts with RT Address other users with an @ RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67 Rich Media embeds via links Tags start with #
25. Indirect Annotation Sept 26, 2009 18:23 EST RT: @jowyang If you are watching the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67
26. Tweet Crawl Three hashtags: #current #debate08 #tweetdebate 97 mins debate + 53 mins following = 2.5 hours total. 3,238 tweets from 1,160 people. 1,824 tweets from 647 people during the debate. 1,414 tweets from 738 people post debate. 577 @ mentions (reciprocity!) 266 mentions during the debate 311 afterwards. Low RT: 24 retweetsin total 6 during 18 afterwards.
27. Volume of Tweets by Minute Crawled from the Twitter RESTful search API.
28. Tweets During and After the Debates Conversation swells after the debate.
32. Automatic Segment Detection 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.
33. Automatic Segment Detection with 92% Accuracy When compared to CSPAN’s editorialized Debate Summary ± 1 minute.
54. People Announce (12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human! (12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo! (12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo (12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.
55. People Reply (12:05) Bastille71: OMG - Obama just messed up the oath - AWESOME! he’s human! (12:07) ryantherobot: LOL Obama messed up his inaugural oath twice! regardless, Obama is the president today! whoooo! (12:46) mattycus: RT @deelah: it wasn’t Obama that messed the oath, it was Chief Justice Roberts: http://is.gd/gAVo (12:53) dawngoldberg: @therichbrooks He flubbed the oath because Chief Justice screwed up the order of the words.
56. HCC and MM and Other Work Indirect annotation through community action Uncollected Sources (read: events) are highly valuable Segmentation, Figure Identification, Term Distance Bigrams? Real Time?
58. Thanks Chloe S., Ben C., Marc S., M. Cameron J., Ryan S., & NodeXL Tweet the Debates: Understanding Community Annotation of Uncollected Sources David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, ACM Multimedia, ACM, 2009 Understanding the Creative Conversation: Modeling to Engagement David A. Shamma; Dan Perkel; Kurt Luther, Creativity and Cognition, ACM, 2009 Spinning Online: A Case Study of Internet Broadcasting by DJs David A. Shamma; Elizabeth Churchill; Nikhil Bobb; Matt Fukuda, Communities & Technology, ACM, 2009 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 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 Supporting creative acts beyond dissemination David A. Shamma; Ryan Shaw, Creativity and Cognition, ACM, 2007 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 Zync: the design of synchronized video sharing Yiming Liu; David A. Shamma; Peter Shafton; Jeannie Yang, Designing for User eXperiences, ACM, 2007
Editor's Notes
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