The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Investigating topic models for social media user recommendation
Proceedings of the 20th international conference companion on World wide web
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Influence and passivity in social media
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
User oriented tweet ranking: a filtering approach to microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding interesting posts in Twitter based on retweet graph analysis
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Expert Systems with Applications: An International Journal
Representative reviewers for Internet social media
Expert Systems with Applications: An International Journal
Detecting malicious tweets in trending topics using a statistical analysis of language
Expert Systems with Applications: An International Journal
Web objectionable text content detection using topic modeling technique
Expert Systems with Applications: An International Journal
Followee recommendation based on text analysis of micro-blogging activity
Information Systems
Hi-index | 12.05 |
Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet's popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods.