microblogs: information and social network huang yuxin
TRANSCRIPT
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Microblogs: Information and Social Network
Huang Yuxin
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Millions of users in Microblogs
• By July 2009, Twitter has attracted 41 million users.
• By March 2011, size of Twitter has grown to 175 million.
• The registered id in Sina Microblog has reached 100 million by March 2011
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People can publish posts and share information on Microblogs
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Social network in Microblogs
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What information can we extract from Microblogs
• Plain Text• User reference (1/2 posts)• Hashtag (1/9 posts)• Retweet• Emoticons• Shortened URL (resource) (1/2 posts)• Time• Users’ Geology info
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Basic features from text of twitterTiny URL
Users Post Time
Emoticons
Hashtag
Mention (User reference)
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What is Twitter(WWW 2010)
• People who are moreactive tend to have morefollowers• The case is different forpeople with very highpopularity.(Because theyare celebrities)
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Small World
• Average Path length of Twitter: 4.12
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Reciprocity?
(Whole dataset)• 77.9% of user pairs with
any link between them are connected one-way.
• And 67.6% of users are not followed by any of their followings.
• The rate of reciprocity is higher in Asian countries than America.
• (www 2010)
(Part of active users• 72.4% of the users in Twitter
follow more than 80% of their followers
• 80.5% of users have 80% of users they are following follow them back
• (wsdm 2010)The difference of conclusion
between these two papers is caused by different data extraction method
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Celebrities And Popular
Topics
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Users’ participation in topics
• A topic can only attract certain group of users
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Content types on twitter
• Daily Chatter• Conversations• Sharing Information• Reporting and Spreading News
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Understanding following Behavior----a statistics made in a paper
• Why we follow: professional interest, technology, tone of presentation, keeping up with friends
• Why we unfollow: Too many posts in general, too much status/personal info, spam, duplicative posts.
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Interesting Research Topics on Twitter
• Vertical Search on Twitter (partial indexing + time sensitive information retrieval)
• Static Topic Detection (topic model)• Burst Event Detection (topic specific)• Topic Biased Expert Recommendation (graph
feature+ activeness+ textual feature)• Cascading Feature Analysis (Network structure
+ topic spreading behavior on different topics)
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Related Works
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People I need to follow vs. Content I need to know
TWEET Listen
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People I need to follow vs. Content I need to know
• An active publisher may has interest in many topics
• My page is always filled with non-valuable latest chatting
• I may only need to subscribe certain topics of an author
• Can we automatically classify one’s content and filter out irrelevant ones?
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Topics spreads through network
EARTHQUAKE
EARTHQUAKE
EARTHQUAKE
EARTHQUAKE
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Detecting hot Topics with community
• keywords temporal feature• Hot topics are biased to a group of users, or a
certain time period
• Retweet Trees, Social Networks accompanied with users’ expertise can all participate in the model training
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Topic Model with network regularization(WWW 08)
21
e.g. coauthor network
Document d
k
12
O(C,G)=L(C)+ R(G,C)
keyword list
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?????
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Rumors have attracted much attention
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Intuitions
• Rumors spread furiously and cause hot discussion
• Rumors tends to be controversial (people spreading it and people against it)
• The source of Rumor (celebrities? Nobody?)• Maybe a study of the spreading of particular
rumor is interesting.• Celebrities will clarify the truth?
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Challenges
• How to differentiate rumors with personal view
• Most of the comments are subjective (expression of feelings)
• Most of the comments are subjective
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Rumors vs. meaningless Topics
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Suggestions and ideas are really Welcome