D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 58 Citations 12,303 189 World Ranking 1763 National Ranking 977

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • The Internet
  • Statistics

Her scientific interests lie mostly in Social media, Artificial intelligence, Internet privacy, World Wide Web and News aggregator. Her work on Social media optimization as part of general Social media research is frequently linked to Visibility, thereby connecting diverse disciplines of science. Her studies in Artificial intelligence integrate themes in fields like Machine learning, Metric and Component.

Her Internet privacy research includes themes of Presentation, Peer production and Social system. Her work on Social network, XML and Information agents as part of general World Wide Web study is frequently linked to Human use, therefore connecting diverse disciplines of science. Her research investigates the connection between Social dynamics and topics such as Popularity that intersect with problems in Quality.

Her most cited work include:

  • Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks (395 citations)
  • Using a model of social dynamics to predict popularity of news (273 citations)
  • A Survey on Bias and Fairness in Machine Learning. (261 citations)

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

The scientist’s investigation covers issues in Social media, World Wide Web, Artificial intelligence, Internet privacy and Information retrieval. Her biological study deals with issues like Popularity, which deal with fields such as Quality and Social dynamics. Her work on World Wide Web is being expanded to include thematically relevant topics such as Stochastic modelling.

She has researched Artificial intelligence in several fields, including Machine learning and Task. Kristina Lerman combines subjects such as Structure, Metadata and Cluster analysis with her study of Information retrieval. Her Structure study combines topics from a wide range of disciplines, such as Inference and Data mining.

She most often published in these fields:

  • Social media (27.48%)
  • World Wide Web (18.13%)
  • Artificial intelligence (14.45%)

What were the highlights of her more recent work (between 2018-2021)?

  • Social media (27.48%)
  • Artificial intelligence (14.45%)
  • Pandemic (2.55%)

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

Her scientific interests lie mostly in Social media, Artificial intelligence, Pandemic, Machine learning and Data science. Her Social media study incorporates themes from Misinformation, Perception and Internet privacy. As a part of the same scientific family, Kristina Lerman mostly works in the field of Misinformation, focusing on Conversation and, on occasion, Data collection.

Her work carried out in the field of Artificial intelligence brings together such families of science as Meaning and Natural language processing. Her Machine learning research incorporates themes from Quality and Sampling. Kristina Lerman interconnects Key, Point, Scale and Personalization in the investigation of issues within Data science.

Between 2018 and 2021, her most popular works were:

  • A Survey on Bias and Fairness in Machine Learning. (261 citations)
  • Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. (153 citations)
  • MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (94 citations)

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

  • Artificial intelligence
  • The Internet
  • Statistics

Kristina Lerman mainly focuses on Social media, Internet privacy, Misinformation, Ground truth and Machine learning. Many of her studies involve connections with topics such as Advertising and Social media. Her Misinformation study combines topics in areas such as Context and Conversation.

Her Ground truth research is multidisciplinary, relying on both High dimensional, Affect and Human–computer interaction. The concepts of her Machine learning study are interwoven with issues in Artificial intelligence and Taxonomy. Her study in the field of Perceptron, Random forest and Benchmark also crosses realms of Human error.

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.

Best Publications

Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks

Kristina Lerman;Rumi Ghosh.
international conference on weblogs and social media (2010)

608 Citations

A Survey on Bias and Fairness in Machine Learning.

Ninareh Mehrabi;Fred Morstatter;Nripsuta Saxena;Kristina Lerman.
arXiv: Learning (2019)

394 Citations

Using a model of social dynamics to predict popularity of news

Kristina Lerman;Tad Hogg.
the web conference (2010)

371 Citations

Distributed online localization in sensor networks using a moving target

Aram Galstyan;Bhaskar Krishnamachari;Kristina Lerman;Sundeep Pattem.
information processing in sensor networks (2004)

364 Citations

The DARPA Twitter Bot Challenge

V.S. Subrahmanian;Amos Azaria;Skylar Durst;Vadim Kagan.
IEEE Computer (2016)

348 Citations

Social Information Processing in News Aggregation

K. Lerman.
IEEE Internet Computing (2007)

340 Citations

Analysis of Dynamic Task Allocation in Multi-Robot Systems

Kristina Lerman;Chris Jones;Aram Galstyan;Maja J Mataríc.
The International Journal of Robotics Research (2006)

299 Citations

Mathematical Model of Foraging in a Group of Robots: Effect of Interference

Kristina Lerman;Aram Galstyan.
Autonomous Robots (2002)

263 Citations

Wrapper maintenance: a machine learning approach

Kristina Lerman;Steven N. Minton;Craig A. Knoblock.
Journal of Artificial Intelligence Research (2003)

253 Citations

Accurately and reliably extracting data from the Web: a machine learning approach

Craig A. Knoblock;Kristina Lerman;Steven Minton;Ion Muslea.
Intelligent exploration of the web (2003)

246 Citations

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