“Social Relevance: Toward Understanding
the Impact of the Individual in an
Information Cascade”
April 19th, 2016
Robert Hall, Joshua White, Jeremy Fields
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● Introduction● Information Cascades● Current CGUD Modeling
● Representation using FOAF● Limitations
● Introduce FGUD● Posts and Inferencing ● Actors
● Future Work
Overview
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Information Cascades (IC)– Given input (object), a person (subject), engages
(predicate) in the same act• Even when the act is contrary to expected behavior
Information Cascades in Social Networks– The dissemination of content
• Defined as User Diffusion (UD)
Most IC models treat a user as a Node in a graph– Reception of a idea is represented by an activation state
• The probability of activation is a function of connectedness to activated nodes
Introduction
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Epidemiological models have been employed to describe the spread of information in social networks [4]
– If knowledge of key individuals in the network is known then method is more effective
– GGUD (Coarse-Grained User Diffusion) models result
• Knowledge of the group dynamic and a few key individuals
Models
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● CGUD● Utilize a connection graph to describe the existence
of a communication link between users of a social network.
● Information dissemination is modeled as a traversal of the graph emanating from activated users.
● The object of interest within an information cascade: URL mention, a hashtag mention, or an unambiguously resolved named entity, may be represented as well.
CGUD
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CGUD
● CGUD modeling extended with FOAF ● Represents Posts and Nodes
● Our CGUD extensions include● Metadata about posts as nodes● Allows inferring, *foaf:made, *:mentions
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CGUD / FOAF Limits
● FOAF for CGUD ● Missing relationships between individuals
● (Dublin Core) also missing this● You can infer these relationships
● Shared vocabulary and interactions● We represent these as an addon
● Infer relationships between foaf:OnlineAccounts by introducing :communicatesTo
● Not all networks will have reciprocation (Twitter vs. Facebook)
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FGUD
● At some point, CGUD is not enough● To many enhancements with FOAF
● Missing many FOAF descriptors
● We present FGUD (Fine Grain User Diffusion) Modeling● Extends the CGUD Model, introduces semantics for
advanced diffusion concepts● How do “all” actors affect diffusion● Represent certain inferred properties as “static”
communication links
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FGUD Additional Vocabulary
● Additional vocabulary has been included in our FGUD model (Mostly NLP Related)
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FGUD Continued
● Data representation reduction● Rather than keep entire “documents” in the graph
● If a document mentions a concept then the concept is stored for the analysis, the document is then linked
● We can take inferred values to a new level● “Sentiment belongs to a Subject, an Object and a
Community”● We can infer “ownership” of an account, or object
● Even when the “owner” did not create the post
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FGUD Continued
● Reply Concept● Abstract away from the idea that a reply it to a
person● SoundCloud: Reply is a tag on an audio file● Mailing list: Reply is to a list with mention of original
message● Twitter: Reply is directed @ an individual with no
message chain● Reply is an instance of communication
● Inferring a reply is is also possible
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● Influence measurement in FGUD● CGUD didn't allow for inferred sna:communicatesTo● We are working on adding PageRank to measure
actor relationships ● Initialized as an adjacency matrix and then normalized
to form a stochastic matrix● Weights are applied differently to sna:communicatesTo
vs inferred sna:communicatesTo● Weights are applied differently for Actor types
Ongoing Work