Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Igor Mozetič"'
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 3, Pp 1149-1175 (2023)
Massive text collections are the backbone of large language models, the main ingredient of the current significant progress in artificial intelligence. However, as these collections are mostly collected using automatic methods, researchers have few i
Externí odkaz:
https://doaj.org/article/8d75265faa2e488582f37176aac5cb7a
Publikováno v:
Applied Network Science, Vol 6, Iss 1, Pp 1-20 (2021)
Abstract Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. I
Externí odkaz:
https://doaj.org/article/681643cab21f4bce821abdae23b5fac7
Autor:
Matteo Cinelli, Andraž Pelicon, Igor Mozetič, Walter Quattrociocchi, Petra Kralj Novak, Fabiana Zollo
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In t
Externí odkaz:
https://doaj.org/article/ed8256497a1b4f2ca0646267f13994e7
Publikováno v:
PLoS ONE, Vol 17, Iss 3 (2022)
We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification mod
Externí odkaz:
https://doaj.org/article/eed9ecea899447869eee65749f3cd9c1
Publikováno v:
Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave, Vol 9, Iss 1 (2021)
Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embeddings transform vector spaces of different languages so that similar
Externí odkaz:
https://doaj.org/article/1edffad0c40f4ae3b7460e49679c0430
Publikováno v:
PLoS ONE, Vol 16, Iss 9, p e0256175 (2021)
Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks: flow of the members in, out and between
Externí odkaz:
https://doaj.org/article/f94bb48c88b7406abe5bff68619dc156
Publikováno v:
Applied Network Science, Vol 3, Iss 1, Pp 1-19 (2018)
Abstract Creating a map of actors and their leanings is important for policy makers and stakeholders in the European Commission’s ‘Better Regulation Agenda’. We explore publicly available information about the European lobby organizations from
Externí odkaz:
https://doaj.org/article/e6876b84a7174b5c9aa76136daa452db
Publikováno v:
Applied Network Science, Vol 3, Iss 1, Pp 1-20 (2018)
Abstract The 2008 financial crisis unveiled the intrinsic failures of the financial system as we know it. As a consequence, impact investing started to receive increasing attention, as evidenced by the high market growth rates. The goal of impact inv
Externí odkaz:
https://doaj.org/article/ed9709a12bd3468eb384628b317f4be2
Publikováno v:
PLoS ONE, Vol 13, Iss 3, p e0194317 (2018)
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentime
Externí odkaz:
https://doaj.org/article/1f6ddb84f190490fa927937194b6792f
Publikováno v:
PLoS ONE, Vol 12, Iss 2, p e0173151 (2017)
We investigate the relationship between social media, Twitter in particular, and stock market. We provide an in-depth analysis of the Twitter volume and sentiment about the 30 companies in the Dow Jones Industrial Average index, over a period of thre
Externí odkaz:
https://doaj.org/article/b6da5b86117c4de5afde8c74735935aa