Twitter bot detection using deep learning
Social media platforms have revolutionized how people interact with each other and how people gain information. However, social media platforms such as Twitter and Facebook quickly became the platform for public manipulation and spreading or amplifying political or ideological misinformation. Althou...
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
2022
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| Sorozat: | Magyar Számítógépes Nyelvészeti Konferencia
18 |
| Kulcsszavak: | Nyelvészet - számítógép alkalmazása, Média - közösségi |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/75879 |
| LEADER | 02002naa a2200265 i 4500 | ||
|---|---|---|---|
| 001 | acta75879 | ||
| 005 | 20221108114906.0 | ||
| 008 | 220525s2022 hu o 1|| eng d | ||
| 020 | |a 978-963-306-848-9 | ||
| 040 | |a SZTE Egyetemi Kiadványok Repozitórium |b hun | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kenyeres Ádám | |
| 245 | 1 | 0 | |a Twitter bot detection using deep learning |h [elektronikus dokumentum] / |c Kenyeres Ádám |
| 260 | |c 2022 | ||
| 300 | |a 257-269 | ||
| 490 | 0 | |a Magyar Számítógépes Nyelvészeti Konferencia |v 18 | |
| 520 | 3 | |a Social media platforms have revolutionized how people interact with each other and how people gain information. However, social media platforms such as Twitter and Facebook quickly became the platform for public manipulation and spreading or amplifying political or ideological misinformation. Although malicious content can be shared by individuals, today millions of individual and coordinated automated accounts exist, also called bots which share hate, spread misinformation and manipulate public opinion without any human intervention. The work presented in this paper aims at designing and implementing deep learning approaches that successfully identify social media bots. Moreover we show that deep learning models can yield an accuracy of 0.9 on the PAN 2019 Bots and Gender Profiling dataset. In addition, the findings of this work also show that pre-trained models will be able to improve the accuracy of deep learning models and compete with Classical Machine Learning methods even on limited dataset. | |
| 650 | 4 | |a Természettudományok | |
| 650 | 4 | |a Számítás- és információtudomány | |
| 650 | 4 | |a Bölcsészettudományok | |
| 650 | 4 | |a Nyelvek és irodalom | |
| 695 | |a Nyelvészet - számítógép alkalmazása, Média - közösségi | ||
| 700 | 0 | 1 | |a Kovács György |e aut |
| 710 | |a Magyar számítógépes nyelvészeti konferencia (18.) (2022) (Szeged) | ||
| 856 | 4 | 0 | |u http://acta.bibl.u-szeged.hu/75879/1/msznykonf_018_257-269.pdf |z Dokumentum-elérés |