Sophie Truong
Biographic Data
| ID | 4448835 |
|---|---|
| NAME | Sophie Truong |
| GIVEN NAMES | Sophie |
| FAMILY NAME | Truong |
| SIGNATURE | TRUONG S |
| AFFILIATIONS | Aalto University |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 5 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
From prejudice to marginalization
This study investigates online hate speech in Finland, particularly Twitter messages targeting people of Muslim faith and the LGBTQ+ community, using a mixed-methods approach that combines quantitative text classification with a BERT model and qualitative thematic analysis via BERTopic and examination of highly interacted posts from 2018 to 2023. The study shows increasing instances of hate speech occurring online, primarily against Muslims, with…
Tracing the dynamics of misinformation and vaccine stance in Finland amid Covid-19
This study analyzed 1,683,700 vaccine-related tweets in Finnish using FinBERT language model, Botometer, and BERTopic, from December 2019 to October 2022. A strong correlation was identified between Negative Stance towards Vaccines (NSV) and misinformation, with an upward trend over time and a significant role of malicious bot accounts. Topic modeling revealed persistent themes of vaccine skepticism and diverse misinformation themes adapting to d…
Unveiling the Veiled Threat
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated be…
Setting the misinformation agenda
This research investigates the dynamics of COVID-19 misinformation spread on Twitter within the unique context of Finland. Employing cutting-edge methodologies including text classification, topic modeling, social network analysis, and correspondence analysis (CA), the study analyzes 1.6 million Finnish tweets from December 2019 to October 2022. Misinformation tweets are identified through text classification and grouped into topics using BERTopi…
Unveiling the Veiled Threat
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated be…
Setting the misinformation agenda
This research investigates the dynamics of COVID-19 misinformation spread on Twitter within the unique context of Finland. Employing cutting-edge methodologies including text classification, topic modeling, social network analysis, and correspondence analysis (CA), the study analyzes 1.6 million Finnish tweets from December 2019 to October 2022. Misinformation tweets are identified through text classification and grouped into topics using BERTopi…
From prejudice to marginalization
This study investigates online hate speech in Finland, particularly Twitter messages targeting people of Muslim faith and the LGBTQ+ community, using a mixed-methods approach that combines quantitative text classification with a BERT model and qualitative thematic analysis via BERTopic and examination of highly interacted posts from 2018 to 2023. The study shows increasing instances of hate speech occurring online, primarily against Muslims, with…
Unveiling the Veiled Threat
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated be…
Setting the misinformation agenda
This research investigates the dynamics of COVID-19 misinformation spread on Twitter within the unique context of Finland. Employing cutting-edge methodologies including text classification, topic modeling, social network analysis, and correspondence analysis (CA), the study analyzes 1.6 million Finnish tweets from December 2019 to October 2022. Misinformation tweets are identified through text classification and grouped into topics using BERTopi…
Tracing the dynamics of misinformation and vaccine stance in Finland amid Covid-19
This study analyzed 1,683,700 vaccine-related tweets in Finnish using FinBERT language model, Botometer, and BERTopic, from December 2019 to October 2022. A strong correlation was identified between Negative Stance towards Vaccines (NSV) and misinformation, with an upward trend over time and a significant role of malicious bot accounts. Topic modeling revealed persistent themes of vaccine skepticism and diverse misinformation themes adapting to d…
From prejudice to marginalization
This study investigates online hate speech in Finland, particularly Twitter messages targeting people of Muslim faith and the LGBTQ+ community, using a mixed-methods approach that combines quantitative text classification with a BERT model and qualitative thematic analysis via BERTopic and examination of highly interacted posts from 2018 to 2023. The study shows increasing instances of hate speech occurring online, primarily against Muslims, with…
2019-20 coronavirus outbreak (3 works) · Computer Science (3 works) · Hate Speech and Cyberbullying Detection (3 works) · Medicine (3 works) · Misinformation and Its Impacts (3 works) · Psychology (3 works) · Sociology (3 works) · Virology (3 works) · Coronavirus disease 2019 (COVID-19 (2 works) · Internet privacy (2 works)