We present a ranking approach for Twitter documents that exploits social hashtagging behavior. We first map topics of user interest, represented by keywords, to a set of twitter hashtags that we use as query terms to retrieve twitter documents (tweets) based on tf-idf scores, with the additional restrictions that the documents retrieved should occur before the query timestamp. We show that this simple method performs significantly better than a disjunctive baseline based on the topic description. The performance achieved makes it specially attractive for information and collaborative filtering tasks, where a personalized lists of items (e.g., tweets) needs to be computed based on the user-item interactions in the system.
Proceedings of The Twentieth Text REtrieval Conference, TREC 2011, Gaithersburg, Maryland, USA, November 15--18, 2011. National Institute of Standards and Technology (NIST) 2011.
%0 Conference Proceedings
%1 diazaviles_trec_microblog_2012
%A Diaz-Aviles, Ernesto
%A Siehndel, Patrick
%A Naini, Kaweh
%B Proceedings of The Twentieth Text REtrieval Conference, TREC 2011, Gaithersburg, Maryland, USA, November 15--18, 2011. National Institute of Standards and Technology (NIST) 2011.
%D 2012
%E Voorhees, Ellen M.
%E Buckland, Lori P.
%K 2012 microblog myown trec twitter
%T Exploiting Social #-Tagging Behavior in Twitter for Information Filtering and Recommendation (Microblog Track)
%X We present a ranking approach for Twitter documents that exploits social hashtagging behavior. We first map topics of user interest, represented by keywords, to a set of twitter hashtags that we use as query terms to retrieve twitter documents (tweets) based on tf-idf scores, with the additional restrictions that the documents retrieved should occur before the query timestamp. We show that this simple method performs significantly better than a disjunctive baseline based on the topic description. The performance achieved makes it specially attractive for information and collaborative filtering tasks, where a personalized lists of items (e.g., tweets) needs to be computed based on the user-item interactions in the system.
@proceedings{diazaviles_trec_microblog_2012,
abstract = {We present a ranking approach for Twitter documents that exploits social hashtagging behavior. We first map topics of user interest, represented by keywords, to a set of twitter hashtags that we use as query terms to retrieve twitter documents (tweets) based on tf-idf scores, with the additional restrictions that the documents retrieved should occur before the query timestamp. We show that this simple method performs significantly better than a disjunctive baseline based on the topic description. The performance achieved makes it specially attractive for information and collaborative filtering tasks, where a personalized lists of items (e.g., tweets) needs to be computed based on the user-item interactions in the system.},
added-at = {2012-12-07T18:10:44.000+0100},
author = {Diaz-Aviles, Ernesto and Siehndel, Patrick and Naini, Kaweh},
biburl = {https://www.bibsonomy.org/bibtex/26a92af0a1dda8182e7ecc897d1ad00fc/diaz.l3s.de},
booktitle = {Proceedings of The Twentieth Text REtrieval Conference, TREC 2011, Gaithersburg, Maryland, USA, November 15--18, 2011. National Institute of Standards and Technology (NIST) 2011. },
description = {Publication Detail},
editor = {Voorhees, Ellen M. and Buckland, Lori P.},
interhash = {7dbb841effd1c68e56157939efbf48f7},
intrahash = {6a92af0a1dda8182e7ecc897d1ad00fc},
keywords = {2012 microblog myown trec twitter},
series = {TREC'12},
timestamp = {2012-12-07T18:10:44.000+0100},
title = {Exploiting Social #-Tagging Behavior in Twitter for Information Filtering and Recommendation (Microblog Track)},
year = 2012
}