H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 64 Citations 18,908 170 World Ranking 1200 National Ranking 8

Research.com Recognitions

Awards & Achievements

2020 - ACM Distinguished Member

2014 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • The Internet
  • Artificial intelligence
  • World Wide Web

Carlos Castillo mostly deals with Social media, World Wide Web, Information retrieval, Microblogging and Data science. The various areas that Carlos Castillo examines in his Social media study include Variety, Information extraction, Data mining and Set. Many of his research projects under World Wide Web are closely connected to Focus with Focus, tying the diverse disciplines of science together.

His Information retrieval study frequently draws connections between related disciplines such as Web page. His Microblogging research is multidisciplinary, incorporating elements of Crowdsourcing and Lexicon. His Data science research integrates issues from Context, Discriminative model, Online advertising and Big data.

His most cited work include:

  • Information credibility on twitter (1363 citations)
  • Finding high-quality content in social media (1007 citations)
  • Twitter under crisis: can we trust what we RT? (726 citations)

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

Carlos Castillo focuses on World Wide Web, Social media, Information retrieval, Data science and Artificial intelligence. He studies Microblogging, a branch of Social media. His Information retrieval research includes elements of Data mining and Web modeling.

His biological study spans a wide range of topics, including Machine learning and Natural language processing. His biological study deals with issues like Query expansion, which deal with fields such as Query language. His Search engine study integrates concerns from other disciplines, such as Ranking and The Internet.

He most often published in these fields:

  • World Wide Web (35.86%)
  • Social media (29.08%)
  • Information retrieval (19.12%)

What were the highlights of his more recent work (between 2017-2020)?

  • Social media (29.08%)
  • Data science (12.75%)
  • Artificial intelligence (11.55%)

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

Social media, Data science, Artificial intelligence, Ranking and Machine learning are his primary areas of study. Carlos Castillo integrates Social media with Mainstream in his study. His Data science study deals with Variety intersecting with Product and Web application.

His Classifier and Interpretability study in the realm of Artificial intelligence connects with subjects such as Justice and Statement. The concepts of his Ranking study are interwoven with issues in Risk analysis and Search engine. His study looks at the intersection of Search engine and topics like Transparency with Information retrieval.

Between 2017 and 2020, his most popular works were:

  • Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. (133 citations)
  • The Effect of Extremist Violence on Hateful Speech Online (35 citations)
  • Processing Social Media Messages in Mass Emergency: Survey Summary (31 citations)

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

  • The Internet
  • Artificial intelligence
  • World Wide Web

His scientific interests lie mostly in Social media, Data science, Ranking, Learning to rank and Public policy. His research integrates issues of Digital media, Information overload and Media studies in his study of Social media. His Information overload research includes themes of Risk analysis, Service and Ranking.

His research is interdisciplinary, bridging the disciplines of Parsing and Data science. His work carried out in the field of Ranking brings together such families of science as Transparency and Information retrieval, Search engine. Carlos Castillo has included themes like Training set, Relevance and Measure in his Learning to rank study.

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

Information credibility on twitter

Carlos Castillo;Marcelo Mendoza;Barbara Poblete.
the web conference (2011)

2007 Citations

Finding high-quality content in social media

Eugene Agichtein;Carlos Castillo;Debora Donato;Aristides Gionis.
web search and data mining (2008)

1803 Citations

Twitter under crisis: can we trust what we RT?

Marcelo Mendoza;Barbara Poblete;Carlos Castillo.
knowledge discovery and data mining (2010)

1241 Citations

Processing Social Media Messages in Mass Emergency: A Survey

Muhammad Imran;Carlos Castillo;Fernando Diaz;Sarah Vieweg.
ACM Computing Surveys (2015)

672 Citations

AIDR: artificial intelligence for disaster response

Muhammad Imran;Carlos Castillo;Ji Lucas;Patrick Meier.
the web conference (2014)

463 Citations

Know your neighbors: web spam detection using the web topology

Carlos Castillo;Debora Donato;Aristides Gionis;Vanessa Murdock.
international acm sigir conference on research and development in information retrieval (2007)

458 Citations

Extracting Information Nuggets from Disaster- Related Messages in Social Media

Muhammad Imran;Shady Elbassuoni;Carlos Castillo;Fernando Diaz.
international conference on information systems for crisis response and management (2013)

409 Citations

The query-flow graph: model and applications

Paolo Boldi;Francesco Bonchi;Carlos Castillo;Debora Donato.
conference on information and knowledge management (2008)

403 Citations

What to Expect When the Unexpected Happens: Social Media Communications Across Crises

Alexandra Olteanu;Sarah Vieweg;Carlos Castillo.
conference on computer supported cooperative work (2015)

383 Citations

Information and Influence Propagation in Social Networks

Wei Chen;Laks V. S. Lakshmanan;Carlos Castillo.
(2013)

376 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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