Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Andreas Kaltenbrunner"'
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1234-1265 (2023)
Identifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbit
Externí odkaz:
https://doaj.org/article/f356f514957141fe92aab1c80616ab23
Publikováno v:
EPJ Data Science, Vol 12, Iss 1, Pp 1-22 (2023)
Abstract We employ Natural Language Processing techniques to analyse 377,808 English song lyrics from the “Two Million Song Database” corpus, focusing on the expression of sexism across five decades (1960–2010) and the measurement of gender bia
Externí odkaz:
https://doaj.org/article/d5b82bf836764920912c9c081effec95
Publikováno v:
The International Journal of Information, Diversity, & Inclusion, Vol 5, Iss 4 (2021)
In the past several years, the Wikimedia Movement has become more aware of the lack of representation of specific communities, that is, content gaps. Next to geographical and gender-related initiatives, the LGBT+ Wikimedia community has organized to
Externí odkaz:
https://doaj.org/article/fdf37731cfee4814a8acb729358cfe1c
Publikováno v:
Área Abierta, Vol 21, Iss 2 (2021)
About a quarter of each Wikipedia language edition is dedicated to representing “local content”, i.e. the corresponding cultural context (geographical places, historical events, political figures, among others). To investigate the relevance of su
Externí odkaz:
https://doaj.org/article/0db4fdd525d245968b0d7e27af9afe06
Publikováno v:
Journal of Internet Services and Applications, Vol 8, Iss 1, Pp 1-17 (2017)
Abstract Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. Th
Externí odkaz:
https://doaj.org/article/9153ff7198ad44c8a6832ec84fe8c4e0
Autor:
Mersedeh Kooshki, Marc van den Homberg, Kyriaki Kalimeri, Andreas Kaltenbrunner, Yelena Mejova, Leonardo Milano, Pauline Ndirangu, Daniela Paolotti, Aklilu Teklesadik, Monica Turner
Due to its geographical location, the Philippines is prone to tropical cyclones (TC) which produce strong winds, accompanied by heavy rains and flooding of large areas, resulting in heavy casualties to human life and destruction to livelihoods and pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c82766cb45af960b3d1dd8bc38ff610
https://doi.org/10.5194/egusphere-egu23-14435
https://doi.org/10.5194/egusphere-egu23-14435
Publikováno v:
Big Data & Society, Vol 3 (2016)
Geoengineering is typically defined as a techno-scientific response to climate change that differs from mitigation and adaptation, and that includes diverse individual technologies, which can be classified as either solar radiation management or carb
Externí odkaz:
https://doaj.org/article/255c599441b64b8aae981b773c26bb01
Autor:
Young-Ho Eom, Pablo Aragón, David Laniado, Andreas Kaltenbrunner, Sebastiano Vigna, Dima L Shepelyansky
Publikováno v:
PLoS ONE, Vol 10, Iss 3, p e0114825 (2015)
Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikiped
Externí odkaz:
https://doaj.org/article/294b9335eafc4172b7c6b48f711f1954
Publikováno v:
PLoS ONE, Vol 9, Iss 8, p e104880 (2014)
BACKGROUND: Despite the undisputed role of emotions in teamwork, not much is known about the make-up of emotions in online collaboration. Publicly available repositories of collaboration data, such as Wikipedia editor discussions, now enable the larg
Externí odkaz:
https://doaj.org/article/bb08ad2b93f246b48ebf760d79d8e0a6
Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fa1f95c0d36f2c3368b1ec845f90fa3