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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 Page: 1 2 3 4 5 Hits 1 – 20 of 87 1 Parler comme jamais. La langue, ce qu'on croit et ce qu'on en sait Véron, Laélia; Candea, Maria. - : HAL CCSD, 2021. : Le Robert / Binge audio, 2021 In: https://hal.archives-ouvertes.fr/hal-03377078 ; Le Robert / Binge audio, 2021, 9782321016687 (2021) BASE Show details 2 The Challenges of Digital Diplomacy in the Era of Globalization: The Case of the United Arab Emirates Antwi-Boateng, Osman; Al Mazrouei, Khadija Ali Mohammed In: International Journal of Communication; Vol 15 (2021); 19 ; 1932-8036 (2021) BASE Show details 3 Exploiting BERT and RoBERTa to Improve Performance for Aspect Based Sentiment Analysis Narayanaswamy, Gagan Reddy. - : Technological University Dublin, 2021 In: Dissertations (2021) BASE Show details 4 Distinguishing fake and real news of twitter data with the help of machine learning techniques Shah, Aanan. - : Laurentian University of Sudbury, 2021 BASE Show details 5 Using language models for holistic language variety comparisons ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 6 Orthographic variation of (lol) on Twitter ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 7 Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia Robertshaw, David; Babicova, Ivana In: First Monday; Volume 26, Number 6 - 7 June 2021 ; 1396-0466 (2021) BASE Show details 8 Narrativised simile and emotional responses to Brexit Barbara Dancygier In: Russian Journal of Linguistics, Vol 25, Iss 3, Pp 663-684 (2021) (2021) BASE Show details 9 “You’re too thick to change the station” – Impoliteness, insults and responses to insults on Twitter Bączkowska Anna In: Topics in Linguistics, Vol 22, Iss 2, Pp 62-84 (2021) (2021) BASE Show details 10 Implicit vs explicit evaluation: How English-speaking Twitter users discuss migration problems Elena V. Gabrielova; Olga I. Maksimenko In: Russian Journal of Linguistics, Vol 25, Iss 1, Pp 105-124 (2021) (2021) BASE Show details 11 eterna regularidad de los participios Miguel Silvestre Llamas In: Domínios de Lingu@gem, Vol 16, Iss 2, Pp 843-869 (2021) (2021) BASE Show details 12 Spanish politicians in Twitter: A linguistic analysis of their written discourse Ricardo Casañ Pitarch In: Ibérica, Vol 40, Iss 1, Pp 195-216 (2021) (2021) BASE Show details 13 Construcción de la imagen colectiva de grupos a favor del Acuerdo de paz de Colombia en Twitter / Construction of the collective image of groups in favor of the Colombian peace agreement on Twitter Laura Cristina Bonilla-Neira In: Revista de Estudos da Linguagem, Vol 29, Iss 4, Pp 2225-2257 (2021) (2021) BASE Show details 14 Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter Pitropakis, Nikolaos; Kokot, Kamil; Gkatzia, Dimitra. - : MDPI, 2020 BASE Show details 15 Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 ... Boukouvalas, Zois; Mallinson, Christine; Crothers, Evan; Japkowicz, Nathalie; Piplai, Aritran; Mittal, Sudip; Joshi, Anupam; Adalı, Tülay. - : Maryland Shared Open Access Repository, 2020 Abstract: Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria. ... Keyword: COVID-19; detection of misinformation; independent component analysis; knowledge discovery; twitter URL: http://mdsoar.org/handle/11603/19952 https://dx.doi.org/10.13016/m2n0kx-zgui BASE Hide details 16 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 17 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 18 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 19 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 20 A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration ... Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu. - : Zenodo, 2020 BASE Show details Page: 1 2 3 4 5

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