H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 34 Citations 7,925 139 World Ranking 6418 National Ranking 3084

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • The Internet
  • World Wide Web

His primary areas of study are World Wide Web, Social media, Internet privacy, Social web and Honeypot. His World Wide Web research incorporates themes from Location sharing, Information retrieval and Data science. His work deals with themes such as Text mining, Digital media, Microblogging and Login, which intersect with Information retrieval.

James Caverlee has included themes like Decision tree, Malware, Tie strength and Social system in his Social media study. His biological study spans a wide range of topics, including Language model and Web 2.0. The various areas that James Caverlee examines in his Social web study include Metadata, Ranking, Content management, Online community and Information sharing.

His most cited work include:

  • You are where you tweet: a content-based approach to geo-locating twitter users (926 citations)
  • Exploring Millions of Footprints in Location Sharing Services (585 citations)
  • Uncovering social spammers: social honeypots + machine learning (516 citations)

What are the main themes of his work throughout his whole career to date?

James Caverlee mostly deals with World Wide Web, Social media, Information retrieval, Artificial intelligence and Recommender system. His studies in Social web, Web modeling, Social Semantic Web, The Internet and Web service are all subfields of World Wide Web research. His Social media research includes themes of Internet privacy, Precision and recall, User profile, Social system and Data science.

James Caverlee combines subjects such as Crowdsourcing, Social spam and Computer security, Honeypot with his study of Internet privacy. His Information retrieval course of study focuses on Web page and PageRank. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Natural language processing.

He most often published in these fields:

  • World Wide Web (37.84%)
  • Social media (28.11%)
  • Information retrieval (27.03%)

What were the highlights of his more recent work (between 2019-2021)?

  • Recommender system (12.97%)
  • Artificial intelligence (14.05%)
  • Information retrieval (27.03%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Recommender system, Artificial intelligence, Information retrieval, Machine learning and Natural language processing. His research in Recommender system intersects with topics in Ranking, E-commerce and Key. The concepts of his Artificial intelligence study are interwoven with issues in Code and Identification.

In his study, James Caverlee carries out multidisciplinary Information retrieval and Style research. His work on Relevance as part of general Machine learning study is frequently linked to Estimation, bridging the gap between disciplines. When carried out as part of a general Natural language processing research project, his work on Unified Medical Language System is frequently linked to work in Disease, Medical diagnosis and Code, therefore connecting diverse disciplines of study.

Between 2019 and 2021, his most popular works were:

  • Next-item Recommendation with Sequential Hypergraphs (14 citations)
  • Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations (6 citations)
  • Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems (5 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • The Internet
  • Machine learning

James Caverlee mainly investigates Recommender system, Artificial intelligence, Machine learning, Information retrieval and Key. His Recommender system study combines topics in areas such as Representation, Layer, User modeling and Information sharing. His work on Unified Medical Language System and Language model as part of general Artificial intelligence study is frequently linked to Medical diagnosis and Code, therefore connecting diverse disciplines of science.

In the subject of general Machine learning, his work in Ranking is often linked to Distribution and Mean squared error, thereby combining diverse domains of study. His Information retrieval research is multidisciplinary, incorporating elements of Range, Continuation, Code, Consistency and Exploit. His Key research is multidisciplinary, incorporating perspectives in Graph neural networks, Graph and Internet privacy.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

You are where you tweet: a content-based approach to geo-locating twitter users

Zhiyuan Cheng;James Caverlee;Kyumin Lee.
conference on information and knowledge management (2010)

1402 Citations

Exploring Millions of Footprints in Location Sharing Services

Zhiyuan Cheng;James Caverlee;Kyumin Lee;Daniel Z. Sui.
international conference on weblogs and social media (2011)

885 Citations

Uncovering social spammers: social honeypots + machine learning

Kyumin Lee;James Caverlee;Steve Webb.
international acm sigir conference on research and development in information retrieval (2010)

830 Citations

Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter

Kyumin Lee;Brian David Eoff;James Caverlee.
international conference on weblogs and social media (2011)

458 Citations

PageRank for ranking authors in co-citation networks

Ying Ding;Erjia Yan;Arthur Frazho;James Caverlee.
Journal of the Association for Information Science and Technology (2009)

340 Citations

A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks

James Caverlee;Steve Webb.
international conference on weblogs and social media (2008)

180 Citations

Location prediction in social media based on tie strength

Jeffrey McGee;James Caverlee;Zhiyuan Cheng.
conference on information and knowledge management (2013)

173 Citations

Ranking Comments on the Social Web

Chiao-Fang Hsu;Elham Khabiri;James Caverlee.
computational science and engineering (2009)

163 Citations

Social Honeypots: Making Friends With A Spammer Near You.

Steve Webb;James Caverlee;Calton Pu.
conference on email and anti-spam (2008)

150 Citations

Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically

Steve Webb;James Caverlee;Calton Pu.
conference on email and anti-spam (2006)

133 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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