Arkaitz Zubiaga
Biographic Data
| ID | 1666279 |
|---|---|
| NAME | Arkaitz Zubiaga |
| GIVEN NAMES | Arkaitz |
| FAMILY NAME | Zubiaga |
| SIGNATURE | ZUBIAGA A |
| AFFILIATIONS | Queen Mary University of London |
| ORCID | 0000-0003-4583-3623 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 5 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Few-Shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance toward new targets for which the model has…
Early Detection and Prevention of Malicious User Behavior on Twitter Using Deep Learning Techniques
Organized misinformation campaigns on Twitter continue to proliferate, even as the platform acknowledges such activities through its transparency center. These deceptive initiatives significantly impact vital societal issues, including climate change, thus spurring research aimed at pinpointing and intercepting these malicious actors. Present-day algorithms for detecting bots harness an array of data drawn from user profiles, tweets, and network …
Session-based cyberbullying detection in social media
Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four …
Hidden behind the obvious
While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. This means that they can falsely (1a) miss abuse without such keywords or (1b) flag non-abu…
SWSR
Automated fact‐checking
As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐…
Tweet, but verify
Tweet, but verify
Automated fact‐checking
As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐…
Hidden behind the obvious
While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. This means that they can falsely (1a) miss abuse without such keywords or (1b) flag non-abu…
SWSR
Session-based cyberbullying detection in social media
Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modelling and understanding the two defining characteristics of cyberbullying: repetitive behaviour and power imbalance. In this survey paper, we define a framework that encapsulates four …
Few-Shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance toward new targets for which the model has…
Early Detection and Prevention of Malicious User Behavior on Twitter Using Deep Learning Techniques
Organized misinformation campaigns on Twitter continue to proliferate, even as the platform acknowledges such activities through its transparency center. These deceptive initiatives significantly impact vital societal issues, including climate change, thus spurring research aimed at pinpointing and intercepting these malicious actors. Present-day algorithms for detecting bots harness an array of data drawn from user profiles, tweets, and network …
Computer Science (7 works) · Artificial Intelligence (5 works) · Data science (5 works) · Social media (4 works) · World Wide Web (4 works) · Hate Speech and Cyberbullying Detection (3 works) · Internet privacy (3 works) · Misinformation and Its Impacts (3 works) · Psychology (3 works) · Information retrieval (2 works)