TwitInfo: Aggregating and Visualizing Microblogs for Event ... · Journalist Interview...
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1 TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration Adam Marcus, Michael Bernstein, Osama Badar, David Karger, Sam Madden, Rob Miller MIT CSAIL
Transcript of TwitInfo: Aggregating and Visualizing Microblogs for Event ... · Journalist Interview...
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TwitInfo: Aggregating and Visualizing Microblogsfor Event Exploration
Adam Marcus, Michael Bernstein, Osama Badar,David Karger, Sam Madden, Rob Miller
MIT CSAIL
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Tell stories using Twitter
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too much
too little
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Identify events of interest
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Identify events of interestLabel events
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Identify events of interestLabel eventsAdd context: sentiment? location?
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Identify events of interestLabel eventsAdd context: sentiment? location?Streaming
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Identify events of interestLabel eventsAdd context: sentiment? location?Streaming
TwitInfo automates the process of telling stories using Twitter
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TwitInfo
event detectionevent labeling
contextstreaming algorithms
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TwitInfo
event detectionevent labeling
contextstreaming algorithms
sentiment correction algorithmevaluation: news consumers
evaluation: pulitzer prize journalist
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[Andre et al. UIST2007] [Diakopoulos et al. VAST 2010][Diakopoulos+Shamma CHI2010] [Dork et al. InfoVis2010][Go et al. 2010] [Leskovec et al. KDD2009][Shamma et al. CSCW2010+2011]
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[Andre et al. UIST2007] [Diakopoulos et al. VAST 2010][Diakopoulos+Shamma CHI2010] [Dork et al. InfoVis2010][Go et al. 2010] [Leskovec et al. KDD2009][Shamma et al. CSCW2010+2011]
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[Andre et al. UIST2007] [Diakopoulos et al. VAST 2010][Diakopoulos+Shamma CHI2010] [Dork et al. InfoVis2010][Go et al. 2010] [Leskovec et al. KDD2009][Shamma et al. CSCW2010+2011]
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[Andre et al. UIST2007] [Diakopoulos et al. VAST 2010][Diakopoulos+Shamma CHI2010] [Dork et al. InfoVis2010][Go et al. 2010] [Leskovec et al. KDD2009][Shamma et al. CSCW2010+2011]
TwitInfo is a streaming storytelling layer that complements these visualizations
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ui walkthroughalgorithm designstudy/evaluation
discussion
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ui walkthroughalgorithm designstudy/evaluation
discussion
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Large
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LargeRelatively
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LargeRelatively
Streaming
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LargeRelatively
Streamingmean deviation
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LargeRelatively
Adaptive Streamingmean deviation
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LargeRelatively
Adaptive StreamingExponentially weighted moving mean deviation
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LargeRelatively
Adaptive Streaming
RFC 2988: ComputingTCP’s retransmission timer
Exponentially weighted moving mean deviation
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LargeRelatively
Adaptive Streaming
RFC 2988: ComputingTCP’s retransmission timer
Exponentially weighted moving mean deviation
TF-IDF
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LargeRelatively
Adaptive Streaming
RFC 2988: ComputingTCP’s retransmission timer
Exponentially weighted moving mean deviation
TF-IDF
2 datasets: earthquakes, soccer games
Precision: 80-100%Recall: 80-100%
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42MAYBE CUT!
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ui walkthroughalgorithm designstudy/evaluation
discussion
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12 participants: four female/8 malefirst half: directed tasks
second half: 5 min write article (soccer, Obama)
how do they use the interface?what can they learn from TwitInfo?
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“After the peace talks, Obama traveled to the ASEAN conference and to NATO to work on issues in those parts of the world. He then spent the week dedicated to domestic economic issues. First he proposed a research tax break, then a $50 billion investment in infrastructure, then the issue came up about whether he should keep some tax breaks that Bush had implemented, and he’s asking for some tax breaks from business, and these are generating some controversy because [. . . ]”
Participant 6:
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Everyone
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EveryoneSplit
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EveryoneSplit
NewsJunkies
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earthquakes
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“my thoughts and prayers go out to everyone in that country. I hope...”
earthquakes
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Journalist Interview
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Journalist Interview
backgrounding
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Journalist Interview
backgrounding eyewitnesses
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ui walkthroughalgorithm designstudy/evaluation
discussion
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appropriate algorithms for the medium
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appropriate algorithms for the medium
other uses for streaming algorithms/signal processing
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appropriate algorithms for the medium
other uses for streaming algorithms/signal processing
sentiment: caution!
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appropriate algorithms for the medium
other uses for streaming algorithms/signal processing
sentiment: caution!
mixed-initiative storytelling
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TwitInfo
http://twitinfo.csail.mit.edu
[email protected] / @marcua
Adam Marcus, Michael Bernstein, Osama Badar,David Karger, Sam Madden, Rob Miller
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Twitter data for fun and stories
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Twitter data for fun and stories
How can we tell stories with the tweet stream?
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Twitter data for fun and stories
How can we tell stories with the tweet stream?
How can we extract data from the tweet stream?
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Twitter data for fun and stories
How can we extract data from the tweet stream?
TwitInfo
How can we tell stories with the tweet stream?
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Twitter data for fun and stories
How can we extract data from the tweet stream?
TwitInfo
TweeQL
How can we tell stories with the tweet stream?
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TwitInfo
How can we tell stories with the tweet stream?
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TwitInfo
How can we tell stories with the tweet stream?
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TwitInfo Uses
Automatically identify eventse.g., goals, earthquakes
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TwitInfo Uses
Automatically identify eventse.g., goals, earthquakes
Backgrounding e.g., Obama's last two weeks
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TwitInfo Uses
Automatically identify eventse.g., goals, earthquakes
Backgrounding e.g., Obama's last two weeks
Identifying sources on the grounde.g., interviewing earthquake survivors
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Cautionary Tweet TalesSentiment is tricky e.g., “My warmest prayers go out to the people of Christchurch.”
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Cautionary Tweet Tales
Location is evasive
Sentiment is tricky e.g., “My warmest prayers go out to the people of Christchurch.”
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Cautionary Tweet Tales
Location is evasive
Sentiment is tricky e.g., “My warmest prayers go out to the people of Christchurch.”
Spam is everywhere
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TwitInfo
Automated event detection makes tweet-basedstory-telling possible
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TwitInfo
Automated event detection makes tweet-basedstory-telling possible
Interfaces tell a story, people add context
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TwitInfo
Automated event detection makes tweet-basedstory-telling possible
Interfaces tell a story, people add context
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TweeQL
How can we extract data from the tweet stream?
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TweeQL
“It's a balmy 89°C in Phoenix”
location=Phoenix, temperatureC=89
How can we extract data from the tweet stream?
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TweeQL
“It's a balmy 89°C in Phoenix”
location=Phoenix, temperatureC=89
“I'm starting to dig Obamacare!”
topic=Obama, sentiment=positive
How can we extract data from the tweet stream?
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Twitter data hacking is hard
● Learn the API● Transform data● Stuff it into a database
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Twitter data hacking is hard
● Learn the API● Transform data● Stuff it into a database
Ad-hoc data processing
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Twitter data hacking is hard
● Learn the API● Transform data● Stuff it into a database
Ad-hoc data processing
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TweeQL extracts data from tweets as they pass through the stream
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TweeQL extracts data from tweets as they pass through the stream
Data extractione.g., location, sentiment, temperature, opencalais
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TweeQL extracts data from tweets as they pass through the stream
Data extractione.g., location, sentiment, temperature, opencalais
SQL-like queriese.g., SELECT location, text FROM twitter
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TweeQL extracts data from tweets as they pass through the stream
Data extractione.g., location, sentiment, temperature, opencalais
SQL-like queriese.g., SELECT location, text FROM twitter
stream, not table
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TweeQL demo*nerd alert*
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TweeQL's other features
● Aggregation● Outlier detection● Joins/mashups with other sources
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Unstructureddata
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+
Structuredqueries
Unstructureddata
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Structuredqueries
=> Structureddata
Unstructureddata
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Structuredqueries
=> Structureddata
Meaningfulvisualizations
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Unstructureddata
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Thanks!
● TweeQL: http://github.com/marcua/tweeql● TwitInfo: http://twitinfo.csail.mit.edu/● Ask me about Mechanical Turk + Journalism!
Adam Marcus@[email protected]://people.csail.mit.edu/marcua
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TweeQL Lessons Learned
● Geographic limitations● Requires grungy regular expressions● Data is not necessarily relational
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Breadth
Depth
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Challenges
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Challenges
Event annotation
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Challenges
Event annotation
Streaming
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Challenges
Event annotation
StreamingSentiment
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Challenges
Event annotation
StreamingLocationSentiment
