TY - GEN
T1 - RAProp
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
AU - Ravikumar, Srijith
AU - Talamadupula, Kartik
AU - Balakrishnan, Raju
AU - Kambhampati, Subbarao
PY - 2013
Y1 - 2013
N2 - The increasing popularity of Twitter renders improved trust-worthiness and relevance assessment of tweets critical for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweets' content alone. We propose a method of ranking tweets by generating a Feature Score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the webpages that the tweets link to. The Feature Score is propagated over an agreement graph based on tweets' content similarity. The propagated Feature Score that is sensitive to content popularity and trustworthiness is used to rank the tweets for a query. An evaluation of our method on 16 million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over the baseline Twitter Search, and outperforms the best-performing method on the TREC 2011 Microblog dataset.
AB - The increasing popularity of Twitter renders improved trust-worthiness and relevance assessment of tweets critical for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweets' content alone. We propose a method of ranking tweets by generating a Feature Score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the webpages that the tweets link to. The Feature Score is propagated over an agreement graph based on tweets' content similarity. The propagated Feature Score that is sensitive to content popularity and trustworthiness is used to rank the tweets for a query. An evaluation of our method on 16 million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over the baseline Twitter Search, and outperforms the best-performing method on the TREC 2011 Microblog dataset.
UR - http://www.scopus.com/inward/record.url?scp=84898877639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898877639&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898877639
SN - 9781577356288
T3 - AAAI Workshop - Technical Report
SP - 101
EP - 103
BT - Late-Breaking Developments in the Field of Artificial Intelligence - Papers Presented at the 27th AAAI Conference on Artificial Intelligence, Technical Report
PB - AI Access Foundation
Y2 - 14 July 2013 through 18 July 2013
ER -