Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Petra Kralj"'
Publikováno v:
B. Evkoski, A. Pelicon, I. Mozeti\v{c}, N. Ljube\v{s}i\'c, P. Kralj Novak. Retweet communities reveal the main sources of hate speech, PLoS ONE 17(3): e0265602, 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:
http://arxiv.org/abs/2105.14898
Autor:
Cinelli, Matteo, Pelicon, Andraž, Mozetič, Igor, Quattrociocchi, Walter, Novak, Petra Kralj, Zollo, Fabiana
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 this work,
Externí odkaz:
http://arxiv.org/abs/2105.14005
Publikováno v:
PLoS ONE 16(9): 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:
http://arxiv.org/abs/2105.06214
Publikováno v:
Applied Network Science, Vol 8, Iss 1, Pp 1-24 (2023)
Abstract We discuss the added value of various approaches for identifying similarities in social network communities based on the content they produce. We show the limitations of observing communities using topology-only and illustrate the benefits a
Externí odkaz:
https://doaj.org/article/bcda0ffa3b594734890431386c415d43
Autor:
Cinelli, Matteo, Conti, Mauro, Finos, Livio, Grisolia, Francesco, Novak, Petra Kralj, Peruzzi, Antonio, Tesconi, Maurizio, Zollo, Fabiana, Quattrociocchi, Walter
Publikováno v:
Journal of Information Warfare (2019) 18.2: 83-98
The massive diffusion of social media fosters disintermediation and changes the way users are informed, the way they process reality, and the way they engage in public debate. The cognitive layer of users and the related social dynamics define the na
Externí odkaz:
http://arxiv.org/abs/1912.10795
We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings. The results of our experiments in the domain specific LiverpoolFC
Externí odkaz:
http://arxiv.org/abs/1912.01072
Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classif
Externí odkaz:
http://arxiv.org/abs/1804.02233
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:
Applied Network Science, Vol 8, Iss 1, Pp 1-1 (2023)
Externí odkaz:
https://doaj.org/article/7c637ffc09e447058f0e9db5f7f829c4