trec 06 : finding opinionated posts, either positive or negative, about a query
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Modeling Influence Opinions and Structure in Social Media. Akshay Java Advisor: Tim Finin. Thesis Statement. Modeling Influence. - PowerPoint PPT PresentationTRANSCRIPT
TREC 06: Finding opinionated posts, either positive or negative,about a query2006 TREC Blog corpus:80K blogs, 300K post50 test queries
BlogVox opinion extraction systemDocument and sentence level scorersCombined scores using an SVM meta-learner
Data cleaning: splogs and post identification
Modeling Influence Opinions and Modeling Influence Opinions and
Structure in Social MediaStructure in Social Media Akshay Java Advisor: Tim Finin
Modeling InfluenceThesis Statement
Influence is Topical
Ongoing Research
An accurate model of influence on the Blogosphere must analyze and combine many contributing factors, including topic, social structure, opinions, biases and time. We will develop, implement and experimentally evaluate such a model to demonstrate its improved accuracy over models based on any of these factors.’’
Temporal Trends Indicate Influence
Popular Topics in Feeds That Matter
“Topic Ontology” derived from 83K user feed subscriptions consisting of 500K feeds. Provides a readership-based metric for influence.
Tech
Slashdot
Gizmodo
Wired
Politics
Dems Reps
Dailykos
Talkingpoints
Michellemalkin
RightwingNews
Epidemic Based Influence ModelsLinear Threshold Model Σ bwv ≥ θvw is the active neighbor of v, θv intrinsic threshold for a node
Greedy Heuristic• Assign random θv• Compute approx influenced set• At each step, add the node that increases the marginal gain in the size of the influenced set
Limitations• Selected nodes may belong to different topics• Social structure not considered • Static View of the network
Extended Model• Finds influential nodes for a topic• Models opinions, bias and trust • Identifies communities and social impact• Tracks temporal evolution of a meme• Richer framework to model influence
Opinions and Bias Influence Readers
Bias towards MSM sources
A generalized framework for influence in Social Media
Predictive Models for topical trends and influence
Link Polarity and Trust
Improved sentiment analysis
Generative Models for the Blogosphere
Who started talking about the topic first? Who were the early adopters?
Who were the influencers? Who was the source of the information?
What are the future trends to watch out for?