What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Linguistics
- Natural language processing
Paolo Rosso spends much of his time researching Artificial intelligence, Natural language processing, Information retrieval, Plagiarism detection and Sentiment analysis.
His study brings together the fields of Context and Artificial intelligence.
His studies deal with areas such as Named-entity recognition, Identification, Word, Discriminative model and Arabic as well as Natural language processing.
Paolo Rosso works mostly in the field of Information retrieval, limiting it down to topics relating to Clef and, in certain cases, Thesaurus, Event and Spatial contextual awareness.
His Plagiarism detection study combines topics in areas such as Scheme, Character, Vocabulary and World Wide Web.
His Sentiment analysis research is multidisciplinary, incorporating elements of Irony, Key and Set.
His most cited work include:
- A multidimensional approach for detecting irony in Twitter (296 citations)
- Overview of the 2nd International Competition on Plagiarism Detection (295 citations)
- An Evaluation Framework for Plagiarism Detection (248 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Natural language processing, Information retrieval, Plagiarism detection and World Wide Web are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Context and Machine learning.
He has included themes like Clef, Arabic and SemEval in his Natural language processing study.
His study on Information retrieval is mostly dedicated to connecting different topics, such as Set.
His studies link Profiling with World Wide Web.
His work carried out in the field of Sentiment analysis brings together such families of science as Irony and Set.
He most often published in these fields:
- Artificial intelligence (56.24%)
- Natural language processing (46.34%)
- Information retrieval (26.34%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (56.24%)
- Natural language processing (46.34%)
- Fake news (3.56%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Natural language processing, Fake news, Social media and Linguistics.
The concepts of his Artificial intelligence study are interwoven with issues in Fusion and Pattern recognition.
Paolo Rosso studies Sentiment analysis which is a part of Natural language processing.
His Fake news study integrates concerns from other disciplines, such as Artificial neural network, Urdu and Affect.
His Social media study incorporates themes from Field, Set, Supervised learning, Masking and Internet privacy.
His Context, Linguistic analysis and Taxonomy study, which is part of a larger body of work in Linguistics, is frequently linked to Immigration, bridging the gap between disciplines.
Between 2019 and 2021, his most popular works were:
- An Emotional Analysis of False Information in Social Media and News Articles (40 citations)
- Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter. (21 citations)
- SardiStance @ EVALITA2020: Overview of the Task on Stance Detection in Italian Tweets (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Linguistics
- Programming language
His primary scientific interests are in Artificial intelligence, Natural language processing, Social media, Fake news and Profiling.
His biological study spans a wide range of topics, including Domain, Software portability and Style.
Paolo Rosso interconnects Word embedding, Jaccard index, Outlier, Offensive and Focus in the investigation of issues within Natural language processing.
His Social media research focuses on subjects like Set, which are linked to Catalan.
The study incorporates disciplines such as Big Five personality traits, Personality, Word and World Wide Web in addition to Fake news.
His Profiling research includes elements of Change detection, Clef, Stylometry and Information retrieval.
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