Y. Duan, L. Jiang, T. Qin, M. Zhou, и H. Shum. Proceedings of the 23rd International Conference on Computational Linguistics, стр. 295--303. Stroudsburg, PA, USA, Association for Computational Linguistics, (2010)
Аннотация
Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.
%0 Conference Paper
%1 Duan:2010:ESL:1873781.1873815
%A Duan, Yajuan
%A Jiang, Long
%A Qin, Tao
%A Zhou, Ming
%A Shum, Heung-Yeung
%B Proceedings of the 23rd International Conference on Computational Linguistics
%C Stroudsburg, PA, USA
%D 2010
%I Association for Computational Linguistics
%K learning rank thema thema:learning-to-rank to tweet twitter
%P 295--303
%T An empirical study on learning to rank of tweets
%U http://dl.acm.org/citation.cfm?id=1873781.1873815
%X Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.
@inproceedings{Duan:2010:ESL:1873781.1873815,
abstract = {Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.},
acmid = {1873815},
added-at = {2016-09-23T11:36:38.000+0200},
address = {Stroudsburg, PA, USA},
author = {Duan, Yajuan and Jiang, Long and Qin, Tao and Zhou, Ming and Shum, Heung-Yeung},
biburl = {https://www.bibsonomy.org/bibtex/2cf6a7ced9092f46b77b5cd3c979c47d0/nosebrain},
booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics},
interhash = {2f032e61db8d2a0ae39314ccd228e41f},
intrahash = {cf6a7ced9092f46b77b5cd3c979c47d0},
keywords = {learning rank thema thema:learning-to-rank to tweet twitter},
location = {Beijing, China},
numpages = {9},
pages = {295--303},
publisher = {Association for Computational Linguistics},
series = {COLING '10},
timestamp = {2016-10-05T18:45:59.000+0200},
title = {An empirical study on learning to rank of tweets},
url = {http://dl.acm.org/citation.cfm?id=1873781.1873815},
year = 2010
}