The bias effect of news media sources on social media users
Treball fi de màster de: Master in Intelligent Interactive Systems
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/48949 |
| Acceso en línea: | http://hdl.handle.net/10230/48949 |
| Access Level: | acceso abierto |
| Palabra clave: | Media bias Twitter analysis Deep learning Transfer learning Political bias News bias |
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The bias effect of news media sources on social media usersKavas, HamitMedia biasTwitter analysisDeep learningTransfer learningPolitical biasNews biasTreball fi de màster de: Master in Intelligent Interactive SystemsTutor: Leo WannerBias in media news is such an interesting topic that it increases its popularity day by day. Classification of news sources according to their political tendency is a very well studied subject. However, we have not been able to find any research based on how these polit- ical orientations are transferred to the users on social media. Thus, in this research we aim to detect changes in the opinion of social media users over time in comparison to the news articles. In light of this, we have created an hybrid model, by combining Convolu- tional neural networks (CNN) with Long short-term memory (LSTM), which is initiated by state-of-the-art BERT embeddings. Our choice of BERT embeddings was based on a long trial and error process with other word embeddings such as GloVe, word2vec and fastText, which led us to the conclusion that the transformer and attention mecha- nism properties of BERT embeddings make it superior to the others. Domain adaptation, which a transfer learning method is employed as the supplementary method in this pa- per, to overcome the language usage differences between news articles and user tweets. Thanks to transfer learning, we have observed a significant improvement in the models performance. The created model has been trained on 600.000 labeled news articles and 84.000 politically leaned tweets during the transfer learning. As a result of our testing on recently published and labeled news articles, as expected our model has been successful in proving that politically biased news articles provoke Twitter users to make more biased comments, whereas objective news articles do not lead people to express more political bias. This thesis also includes an Apache NiFi implementation of the idea of monitoring bias dynamically.202120212020info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/48949reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/489492026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
The bias effect of news media sources on social media users |
| title |
The bias effect of news media sources on social media users |
| spellingShingle |
The bias effect of news media sources on social media users Kavas, Hamit Media bias Twitter analysis Deep learning Transfer learning Political bias News bias |
| title_short |
The bias effect of news media sources on social media users |
| title_full |
The bias effect of news media sources on social media users |
| title_fullStr |
The bias effect of news media sources on social media users |
| title_full_unstemmed |
The bias effect of news media sources on social media users |
| title_sort |
The bias effect of news media sources on social media users |
| dc.creator.none.fl_str_mv |
Kavas, Hamit |
| author |
Kavas, Hamit |
| author_facet |
Kavas, Hamit |
| author_role |
author |
| dc.subject.none.fl_str_mv |
Media bias Twitter analysis Deep learning Transfer learning Political bias News bias |
| topic |
Media bias Twitter analysis Deep learning Transfer learning Political bias News bias |
| description |
Treball fi de màster de: Master in Intelligent Interactive Systems |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020 2021 2021 |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10230/48949 |
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http://hdl.handle.net/10230/48949 |
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Inglés |
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Inglés |
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Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
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openAccess |
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application/pdf application/pdf |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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15,811543 |