Predicting discussions on the social semantic web
|Conference paper (help)|
|Predicting discussions on the social semantic web|
|Authors:||Matthew Rowe, Sofia Angeletou, Harith Alani|
|Citation:||The Semantic Web: Research and Applications 6644 in Lecture Notes in Computer Science : 2011 June|
|Editors:||Grigoris Antoniou, Marko Grobelnik, Elena Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter de Leenheer, Jeff Pan|
|Publisher:||Springer-Verlag, Berlin Heidelberg 2011|
|Meeting:||8th Extended Semantic Web Conference|
|Web:||DuckDuckGo Bing Google Yahoo! — Google PDF|
|Article:||Google Scholar PubMed|
|Restricted:||DTU Digital Library|
Predicting discussions on the social semantic web describes a study on identifying key indicators for postings on Twitter that generate discussions: "Will a given post start a discussion?"
Description of features connected:
These include user features: in degree, out degree, list degree, post count, user age, post rate as well content features: post length, complexity, uppercase count, readability, verb count, noubn count, adective count, referral count, time in the day, informativeness (terminological novelty), politary (using SentiWordnet)
User list degree, user in degree, content- time in day were important featuree in discussion posts.