the debate you’re invited to participate in #tweetdebate Here is the 411 http://tinyurl.com/3jdy67 Repeated (retweet) content starts with RT Address other users with an @ Tags start with # Rich Media embeds via links Thursday, March 15, 2012
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. 15 Thursday, March 15, 2012
and Shamma, D. A. Characterizing debate performance via aggregated twitter sentiment. In CHI ’10: Proceedings of the 28th international conference on Human factors in computing systems (New York, NY, USA, 2010), ACM, pp. 1195–1198. Thursday, March 15, 2012
Shamma, D. A. Characterizing debate performance via aggregated twitter sentiment. In CHI ’10: Proceedings of the 28th international conference on Human factors in computing systems (New York, NY, USA, 2010), ACM, pp. 1195–1198. Thursday, March 15, 2012
circa 2009: Data Mining Feed • 600 Tweets per minute • 90 Minutes • 54,000 Tweets from 1.5 hours • Constant data rate means the volume method doesn’t work. Thursday, March 15, 2012
mins, find terms that are only relevant to that slice, subtract out salient, non-stop listed terms like: Obama, president, and speech. No significant occurrence of “remaking” Thursday, March 15, 2012
mins, find terms that are only relevant to that slice, subtract out salient, non-stop listed terms like: Obama, president, and speech. No significant occurrence of “remaking”. Contains an occurrence of “remaking” less significant than peak. Peak occurrence of “remaking”. Thursday, March 15, 2012
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. Thursday, March 15, 2012
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. Thursday, March 15, 2012
In this case the conversational patterns stayed the same. Firehose data is ≈ 26% dissimilar from other collection methods. What about people and deep motivations? Thursday, March 15, 2012
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 Thursday, March 15, 2012
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). Thursday, March 15, 2012
has been the golden egg for recommendation systems so far; implicit human cooperative sharing activity works better. If comedy is a social construct, lets train on the construct. Thursday, March 15, 2012
categorize videos, as Comedy in our study, at the time of creation and consumption? RQ2: Can a predictive model be built to automatically categorize media in a manner that is contextually and socially appropriate? Thursday, March 15, 2012
get judgements on 20 randomly chosen videos from a sample of videos. (43 complete responses) • In general, the human as a classifier categorizes at 60.9% accuracy. • For Comedy, their accuracy was 52.3% 72 Thursday, March 15, 2012
Not sure it should be categorize as a comedy.” (Respondent 16 on Video 8) “Its funny but its only an animal getting startled by her sneezing baby. It is not comedy because the actions were not specifically done to make us laugh.” (Respondent 9 on Video 9) Thursday, March 15, 2012
to fit categories more narrowly...If NetFlix says that something is a screwball comedy, I know what to expect. I think on YouTube the range of possibilities for the content of the videos is much less constrained. Cause it might literally be a segment from a film or it could be something shot on a cheap digital camera.” 78 Thursday, March 15, 2012
the laughter track, it doesn’t even have to be funny, it’s intended to be Comedy. The style of it...even if it’s a Music Video.” “Anything comedy is always impressed upon you with laughter in the background or some funny accompanying music...Those contextual cues.” 79 Thursday, March 15, 2012
to have for something to be identified as comedy … when I see a video that I have no context for, I don’t know whether to identify it as funny. But if people are interacting with it in a way that makes me believe that it’s funny. Same thing for the wedding dance (JK wedding video) … my interaction with it is, ‘people are saying that this is funny.’” 80 Thursday, March 15, 2012
of videos) are uploading things and categorizing it as ‘Comedy’ because they are proposing that there’s something funny in it. Even though the content itself may not be ‘Comedy’.” 81 Thursday, March 15, 2012
Views Rating* Duration (session)* # of Play/Pause* # of Scrubs* # of Chats* You Tube Zync Duration (video) Views Rating* Duration (session)* # of Play/Pause* # of Scrubs* # of Chats* You Tube (not used) Zync (not used) Tags Comments Favorites Emoticons User ID data # of Sessions # of Loads Thursday, March 15, 2012
23.0% You Tube Features 14.6% Humans 60.9% YouTube Features 75.9% Social sharing patterns from Zync are highly predictive of content type. Here we can predict if a video is Sports, Comedy, Entertainment, People, or Film just from how the video is shared. This brings better recommendations for content and advertising. Zync Features 87.8% Thursday, March 15, 2012
98.5% (F1 = 0.78) 87 Does a video have over 10M views? We did this using data from 10 people in 5 shared sessions. The drawback is you need to use a synchronous connected experience. Thursday, March 15, 2012
isn’t necessarily better data • Corporate design & building patterns can deliver you bad data. • Big Data tends to answer “what can we do with all this data” but not “how can we create and properly instrument the world” • People and their communication patterns can be found and utilized effectively, often requiring significantly less data to solve similar problems. Thursday, March 15, 2012
A. Khan, N. Diakopoulos, A. Brooks, Y. Liu, S. Pentland, J. Antin, J. Dunning, Chloe S., Marc S., & M. Cameron J. Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors David A. Shamma; Jude Yew; Lyndon Kennedy; Elizabeth F. Churchill, ICWSM 2011 - International AAAI Conference on Weblogs and Social Media, AAAI, 2011 Knowing Funny: Genre Perception and Categorization in Social Video Sharing Jude Yew; David A. Shamma; Elizabeth F. Churchill, CHI 2011, ACM, 2011 Peaks and Persistence: Modeling the Shape of Microblog Conversations David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, CSCW 2011, ACM, 2011 Know Your Data: Understanding Implicit Usage versus Explicit Action in Video Content Classification Jude Yew; David A. Shamma, Electronic Imaging, IS&T/SPIE, 2011 Beyond Freebird David A. Shamma, XRDS: Crossroads, ACM, 2010, 2 Conversational Shadows: Describing Live Media Events Using Short Messages David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, International AAAI Conference on Weblogs and Social Media, AAAI, 2010 Characterizing Debate Performance via Aggregated Twitter Sentiment Nicholas A. Diakopoulos; David A. Shamma, CHI 2010, ACM, 2010 Statler: Summarizing Media through Short-Message Services David A. Shamma; Lyndon Kennedy; Elizabeth F. Churchill, CSCW, 2010 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, Konstantinos Chorianopoulos, 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 Thursday, March 15, 2012
ayman
kuri8ive
ontime11
sgnm
cybozuinsideout
hashi0203
upura
kitachan_black
kionawalker
daimoriwaki
gaelvaroquaux
unzaih
maltzj
geoffreycrofte
davidbonilla
jasonvnalue
iamctodd
deanohume
destraynor
shlominoach
myddelton
lauravandoore
keavy
jrom
![27 Sen$ment/Affect judgements from the debate. [1] Diakopoulos, N. A.,](https://files.speakerdeck.com/presentations/4f837b05fee04b001f011ce3/slide_26.jpg){kind=link}
![Sen$ment/Affect judgements by candidate. 28 [1] Diakopoulos, N. A., and](https://files.speakerdeck.com/presentations/4f837b05fee04b001f011ce3/slide_27.jpg){kind=link}
