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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Cross-lingual few-shot hate speech and offensive language detection using meta learning Mozafari, Marzieh; Farahbakhsh, Reza; Crespi, Noel In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022) BASE Show details 2 Dataset of coronavirus content from Instagram with an exploratory analysis Zarei, Koosha; Farahbakhsh, Reza; Crespi, Noel; Tyson, Gareth In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559489 ; IEEE Access, IEEE, 2021, 9, pp.157192-157202. ⟨10.1109/ACCESS.2021.3126552⟩ (2021) Abstract: International audience ; The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professionals’ engagement in social media. Online Social Networks are playing an important role in news propagation as well as keeping people in contact. At the same time, social media is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this study, we publish a multilingual coronavirus (COVID-19) Instagram dataset that we have continuously collected during the first wave of the pandemic from 5 January 2020 to 30 May 2020. The dataset contains 25.7K posts, 829K comments, and 3.2M likes in various subjects from different publishers such as ‘public accounts’, ‘fake accounts (bots)’, ‘newsagencies’, ‘influencers’, ‘celebrities’, ‘business pages’, etc. In addition to the dataset, this paper provides an analysis of the behaviour of the publishers. We study the behavioural aspects of the users in terms of their engagement, use of hashtags, activities, reactions as well as a full analysis of the published content related to the COVID-19. We believe this contribution helps the research community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]; [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; Bot; Coronavirus; COVID-19; Dataset; Fake content; Instagram; Social network analysis URL: https://hal.archives-ouvertes.fr/hal-03559489 https://doi.org/10.1109/ACCESS.2021.3126552 BASE Hide details 3 Transfer Learning for Multi-lingual Tasks -- a Survey ... Jafari, Amir Reza; Heidary, Behnam; Farahbakhsh, Reza. - : arXiv, 2021 BASE Show details 4 A First Instagram Dataset on COVID-19 ... Zarei, Koosha; Farahbakhsh, Reza; Crespi, Noel. - : arXiv, 2020 BASE Show details

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