More Web Proxy on the site http://driver.im/

research-article

Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends?

Authors:

Christoph Besel,

Jörg Schlötterer,

Michael GranitzerAuthors Info & Claims

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Pages 1152 - 1157

https://doi.org/10.1145/2851613.2851819

Published: 04 April 2016 Publication History

Abstract

Social media based recommendation systems infer users' interests from their social network activity in order to provide personalised recommendations. Typically, the user profiles are generated by analysing the users' posts or tweets. However, there might be a significant difference between what a user produces and what she consumes. We propose an approach for inferring user interests from followees (the accounts the user follows) rather than tweets. This is done by extracting named entities from a user's followees using the English Wikipedia as knowledge base and regarding them as interests. Afterwards, a spreading activation algorithm is performed on a Wikipedia category taxonomy to aggregate the various interests to a more abstract interest profile. With over 7 out of 10 items being relevant to the users in our evaluation, we show that this approach can compete with the state of the art and performs even better in predicting the users' interests than their human friends do.

References

[1]

F. Abel et al. "Analyzing user modeling on twitter for personalized news recommendations". In: UMAP. 2011, pp. 1--12.

Digital Library

[2]

C. G. Akcora et al. "Detecting anomalies in social network data consumption". In: SNAM 4.1 (2014), pp. 1--16.

[3]

A. Edmunds and A. Morris. "The problem of information overload in business organisations: a review of the literature". In: IJIM 20.1 (2000), pp. 17--28.

Digital Library

[4]

T. Flati et al. "Two is bigger (and better) than one: the Wikipedia Bitaxonomy Project". In: ACL. 2014, pp. 945--955.

[5]

S. Gunelius. Facebook's Growing Problem - Passive Users. http://www.corporate-eye.com/main/facebooksgrowing-problem-passive-users/. 2015.

[6]

P. Kapanipathi et al. "User Interests Identification on Twitter Using a Hierarchical Knowledge Base". In: ESWC. 2014, pp. 99--113.

[7]

H. Kwak et al. "What is Twitter, a social network or a news media?" In: WWW. 2010, pp. 591--600.

Digital Library

[8]

S. LeMole et al. "Method and system for presenting customized advertising to a user on the world wide web". Patent US6009410 A (US). A. Corporation. 1999.

[9]

H. Lieberman et al. "Letizia: An agent that assists web browsing". In: IJCAI (1) 1995 (1995), pp. 924--929.

Digital Library

[10]

K. H. Lim and A. Datta. "Interest classification of Twitter users using Wikipedia". In: OpenSym. 2013, p. 22.

Digital Library

[11]

C. Lu, W. Lam, and Y. Zhang. "Twitter user modeling and tweets recommendation based on wikipedia concept graph". In: Workshops at the 26. AAAI Conf. on Artificial Intelligence. 2012.

[12]

C. D. Manning, P. Raghavan, H. Schütze, et al. Introduction to information retrieval. Vol. 1. Cambridge university press Cambridge, 2008.

Digital Library

[13]

P. Resnik. "Using information content to evaluate semantic similarity in a taxonomy". In: arXiv preprint cmp-lg/9511007 (1995).

Digital Library

[14]

P. Siehndel and R. Kawase. "TwikiMe! User profiles that make sense." In: ISWC (posters and demos). 2012.

Digital Library

[15]

L. Tamine-Lechani, M. Boughanem, and N. Zemirli. "Inferring the user interests using the search history". In: LWA '06. 2006, pp. 108--110.

[16]

K. Tao et al. "Tums: twitter-based user modeling service". In: ESWC Workshops. 2012, pp. 269--283.

Digital Library

[17]

W. Youyou, M. Kosinski, and D. Stillwell. "Computerbased personality judgments are more accurate than those made by humans". In: PNAS 112.4 (2015), pp. 1036--1040.

Cited By

Hayama T(2021)Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s InterestsIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493979(476-481)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493979
Hayama TZhang Q(2021)Topic-model based Estimation of Passive Twitter-User's Interests from Followed Users' Tweets2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI53430.2021.00057(319-324)Online publication date: Jul-2021
https://doi.org/10.1109/IIAI-AAI53430.2021.00057
Kovačević ŽMernik MRavber MČrepinšek M(2020)From Grammar Inference to Semantic Inference—An Evolutionary ApproachMathematics10.3390/math80508168:5(816)Online publication date: 18-May-2020
https://doi.org/10.3390/math8050816
Show More Cited By

Index Terms

Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends?
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social networks
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Personalization

Recommendations

On the quality of semantic interest profiles for onine social network consumers

Social media based recommendation systems infer user' interests and preferences from their social network activity in order to provide personalised recommendations. Typically, the user profiles are generated by analysing the users' posts or tweets. ...
Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter
HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media

User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-...
Twitter message recommendation based on user interest profiles
ASONAM '16: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Twitter has become one of the most important platforms for gathering information, where users follow breaking news, track ongoing events and learn about their topics of interest. Considering the sheer volume of Twitter data and the ever-growing number ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

April 2016

2360 pages

ISBN:9781450337397

DOI:10.1145/2851613

Conference Chair:
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2016 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 the author(s) 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].

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 April 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Seventh Framework Programme

Conference

SAC 2016

Sponsor:

SIGAPP

SAC 2016: Symposium on Applied Computing

April 4 - 8, 2016

Pisa, Italy

Acceptance Rates

SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
261
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hayama T(2021)Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s InterestsIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493979(476-481)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493979
Hayama TZhang Q(2021)Topic-model based Estimation of Passive Twitter-User's Interests from Followed Users' Tweets2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI53430.2021.00057(319-324)Online publication date: Jul-2021
https://doi.org/10.1109/IIAI-AAI53430.2021.00057
Kovačević ŽMernik MRavber MČrepinšek M(2020)From Grammar Inference to Semantic Inference—An Evolutionary ApproachMathematics10.3390/math80508168:5(816)Online publication date: 18-May-2020
https://doi.org/10.3390/math8050816
Lim KLee KKendal DRashidi LNaghizade EWinter SVasardani MChampin PGandon FMédini LLalmas MIpeirotis P(2018)The Grass is Greener on the Other SideCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186337(275-282)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3186337
Nechaev YCorcoglioniti FGiuliano C(2018)SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profilesProgress in Artificial Intelligence10.1007/s13748-018-0160-x7:4(251-272)Online publication date: 8-Sep-2018
https://doi.org/10.1007/s13748-018-0160-x
Piao GBreslin J(2018)Inferring user interests in microblogging social networksUser Modeling and User-Adapted Interaction10.1007/s11257-018-9207-828:3(277-329)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11257-018-9207-8
Al-Qurishi MAlhuzami SAlrubaian MHossain MAlamri ARahman M(2018)User profiling for big social media data using standing ovation modelMultimedia Tools and Applications10.1007/s11042-017-5402-677:9(11179-11201)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11042-017-5402-6
Di Tommaso GFaralli SStilo GVelardi P(2018)Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to WikipediaThe Semantic Web – ISWC 201810.1007/978-3-030-00668-6_3(36-52)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-00668-6_3
Piao GBreslin JDolog PVojtas PBonchi FHelic D(2017)Leveraging Followee List Memberships for Inferring User Interests for Passive Users on TwitterProceedings of the 28th ACM Conference on Hypertext and Social Media10.1145/3078714.3078730(155-164)Online publication date: 4-Jul-2017
https://dl.acm.org/doi/10.1145/3078714.3078730
Nechaev YCorcoglioniti FGiuliano C(2017)SocialLink: Linking DBpedia Entities to Corresponding Twitter AccountsThe Semantic Web – ISWC 201710.1007/978-3-319-68204-4_17(165-174)Online publication date: 21-Oct-2017
https://dl.acm.org/doi/10.1007/978-3-319-68204-4_17
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents