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Clustering facebook for biased context extraction
Valentina Franzoni
*
,
Yuanxi Li
,
Paolo Mengoni
, Alfredo Milani
*
Corresponding author for this work
Department of Computer Science
Department of Interactive Media
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
15
Citations (Scopus)
Overview
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Dive into the research topics of 'Clustering facebook for biased context extraction'. Together they form a unique fingerprint.
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Keyphrases
Facebook
100%
Facebook Comments
100%
Context Extraction
100%
Bag of Visual Words (BoVW)
66%
Context Word
66%
Social Media
33%
Semantic Similarity
33%
Application Domain
33%
Associated Sets
33%
Similarity Measure
33%
Collective Knowledge
33%
Group Similarity
33%
Object Similarity
33%
Politics
33%
Stamping
33%
Cognitive Factors
33%
Artificial Intelligence
33%
Information Security
33%
Information Technology
33%
Wikipedia
33%
Cognitive Behavior
33%
Information Content
33%
Information Spreading
33%
Behavioral Patterns
33%
Content Consumption
33%
Path-based
33%
Semantic Similarity Measure
33%
Human-readable
33%
Word Cloud
33%
Generated Content
33%
WordNet
33%
Human Bias
33%
Third-party Knowledge
33%
Frequency-based Approach
33%
Computer Science
Facebook
100%
Common Sense
50%
Application Domain
25%
Information Technology
25%
Data Mining
25%
Artificial Intelligence
25%
Information Security
25%
Use Case
25%
semantic similarity measure
25%
Experimental Evidence
25%
Behavioral Pattern
25%
Generated Content
25%
Content Consumption
25%
Human Bias
25%
Structured Information
25%