Cited By
View all- Bhoi APujari SBalabantaray R(2020)A deep learning-based social media text analysis framework for disaster resource managementSocial Network Analysis and Mining10.1007/s13278-020-00692-110:1Online publication date: 9-Sep-2020
Tweets Ranking Considering Dynamic Social Influence and Personal Interests
Pages 276 - 282
Abstract
Social networks play important roles in information propagation and interactions among friends. Billions of information stream are created by users in Twitter and diffuse among relationship networks. Users may receive mass tweets posted by their followees every week. Thus, how to rank and display the tweets users are interested in and willing to interact with, is necessary to improve user's experience. Information propagation is usually driven by user interaction behaviors including retweet and comment. While these behaviors are mainly affected by the social influence and user interest, which dynamically change with time. In this paper, we propose a tweets ranking model to predict information diffusion based on dynamic social influence and personal interests. Our model combines these factors together by exploiting listwise algorithm such as ListNet. Experiments show that our model performs better than several baseline models, and both social influence and personal interests play important roles in tweets ranking.
References
[1]
Java A, Song X, Finin T, et al. 2007. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web Mining and Social Network Analysis. ACM, 2007, 56--65.
[2]
Cao Z, Qin T, Liu T Y, et al. 2007. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th International Conference on Machine Learning. ACM, 2007, 129--136.
[3]
S. Ravikumar, R. Balakrishnan and S. Kambhampati. 2012. Ranking Tweets Considering Trust and Relevance. In Proceedings of the Ninth International Workshop on Information Integration on the Web, 2012, 1--4.
[4]
Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou and Heung-Yeung Shum. 2010. An empirical study on learning to rank of tweets. In Proceedings of the 23rd International Conference on Computational Linguistics, 2010, 295--303.
[5]
Kailong Chen, Tianqi Chen and Guoqing Zheng et al. 2012. Collaborative Personalized Tweet Recommendation. In Proceedings of the 35th international ACM SIGIR Conference on Research and Development in Information Retrieval, 2012, 661--670.
[6]
Rui Yan, Mirella Lapata and Xiaoming Li. 2012. Tweet Recommendation with Graph Co-Ranking. In Proceedings of the 50th Annual Meeting of the Association for Compuational Linguistics, 2012, 516--525.
[7]
Ibrahim Uysal and W. Bruce Croft. 2011. User Oriented Tweet Ranking: A Filtering Approach to Microblogs, In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 2011, 2261--2264.
[8]
Dongxu Huang, Jing Zhou and Dejun Mu. 2015. Retweet Behavior Prediction in Twitter. In Proceedings of the 7th International Symposium on Computational Intelligence and Design, 2015, 30--33.
[9]
Li Q, Shah S, Nourbakhsh A, et al. 2016. Hashtag Recom-mendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016, 2085--2088.
[10]
De Maio C, Fenza G, Gallo M, et al. 2017. Time-aware adaptive tweets ranking through deep learning. In Future Generation Computer Systems, 2017.
[11]
L. Page, S. Brin, R.Motwani and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford InfoLab.
[12]
D. Tunkelang. 2009. A Twitter Analog to PageRank. The Noisy Channel, 2009.
[13]
Xiang Li, Shaoyin Cheng, Wenlong Chen and Fan Jiang. 2013. Novel User Influence Measurement Based on User Interaction in Microblog. In Proceedings of the 2013 IEEE/ACM International Conference on Advance in Social Networks Analysis and Mining, 2013, 615--619.
[14]
Jie Tang, Jimeng Sun, Chi Wang and Zi Yang. 2009. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, 807--816.
[15]
Jing Wang, Zhijing Liu and Hui Zhao. 2015. Topic Oriented User Influence Analysis in Social Networks. In Proceeding of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2015, 123--126.
[16]
Bakshy E, Rosenn I, Marlow C, et al. 2012. The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web. ACM, 2012, 519--528.
[17]
Frederick E L, Lim C H, Clavio G, et al. 2012. Why we follow: An examination of parasocial interaction and fan motivations for following athlete archetypes on Twitter. In International Journal of Sport Communication, 2012, 5(4), 481--502.
[18]
T. Mikolov, I. Sutskever, K. Chen, G Corrado, J Dean, 2013. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems, 2013, 3111--3119.
[19]
ANSJ_SEG. 2016. https://github.com/NLPchina/ansj_seg.
[20]
Blei D M, Ng A Y, Jordan M I. 2003. Latent dirichlet allocation. In Journal of machine learning research, 2003, 3(Jan), 993--1022.
[21]
D. Kempe, J. Kleinberg and E. Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowlege Discovery and Data Mining, 2003, 137--146.
Index Terms
- Tweets Ranking Considering Dynamic Social Influence and Personal Interests
Recommendations
The Impact of Social Diversity and Dynamic Influence Propagation for Identifying Influencers in Social Networks
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01There has been significant recent interest in using the aggregate information from social media sites (e.g., Twitter) to identify influencers. To investigate this issue, one dynamic diversity-dependent algorithm is proposed for detecting the influencers ...
Friendrank: a personalized approach for tweets ranking in social networks
ASONAM '16: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningThe thousands of streaming data overwhelmingly provide for Internet users on Twitter every day, especially for those Twitter users with many friends. However, the useful tweets that users are really interested in personally could be covered by massive ...
User interests in social media sites: an exploration with micro-blogs
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementRecent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
February 2018
411 pages
ISBN:9781450363532
DOI:10.1145/3195106
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Southwest Jiaotong University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 February 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICMLC 2018
ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing
February 26 - 28, 2018
Macau, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 88Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)0
Reflects downloads up to 30 Nov 2024
Other Metrics
Citations
Cited By
View all- Bhoi APujari SBalabantaray R(2020)A deep learning-based social media text analysis framework for disaster resource managementSocial Network Analysis and Mining10.1007/s13278-020-00692-110:1Online publication date: 9-Sep-2020
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in