Preslav Nakov
Biographic Data
| ID | 3445446 |
|---|---|
| NAME | Preslav Nakov |
| GIVEN NAMES | Preslav |
| FAMILY NAME | Nakov |
| SIGNATURE | NAKOV P |
| AFFILIATIONS | Mohamed bin Zayed University of Artificial Intelligence |
| ORCID | 0000-0002-3600-1510 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Challenging others when posting misinformation: A UK vs. Arab cross-cultural comparison on the perception of negative consequences and injunctive norms
This study investigates the factors influencing the willingness to challenge misinformation on social media across two cultural contexts, the United Kingdom (UK) and Arab countries. A total of 462 participants completed an online survey (250 UK, 212 Arabs). The analysis revealed that three types of negative consequences (relationship cost, negative impact on the person being challenged, futility) and also injunctive norms influence the willingnes…
On the need to develop nuanced measures assessing attitudes towards AI and AI literacy in representative large-scale samples
Since the launch of the generative AI, including tools such as ChatGPT, the impact of artificial intelligence (AI) on societies has been extensively discussed. How far reaching will the impact of AI be? On the positive side, AI might help find solutions for the world’s problems, such as tackling issues around climate change or the development of new psychopharmaceuticals to enhance global mental health. However, AI, as a technology that leads to …
Why do we not stand up to misinformation? Factors influencing the likelihood of challenging misinformation on social media and the role of demographics
This study investigates the barriers to challenging others who post misinformation on social media platforms. We conducted a survey amongst U.K. Facebook users (143 (57.2 %) women, 104 (41.6 %) men) to assess the extent to which the barriers to correcting others, as identified in literature across disciplines, apply to correcting misinformation on social media. We also group the barriers into factors and explore demographic differences amongst th…
Similar Languages, Varieties, and Dialects: A Computational Perspective
The dark side of news community forums: Opinion manipulation trolls
Purpose The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community foru…
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding corr…
Source Language Adaptation Approaches for Resource-Poor Machine Translation
Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language. Thus, we propose three novel, language-independent approaches to source language adaptation for resource-poor statistical machine translation. Specifically, we build improved statistical machine translation models from a resource-poor language POOR into a target language TGT by adapting and us…
Why do we not stand up to misinformation? Factors influencing the likelihood of challenging misinformation on social media and the role of demographics
This study investigates the barriers to challenging others who post misinformation on social media platforms. We conducted a survey amongst U.K. Facebook users (143 (57.2 %) women, 104 (41.6 %) men) to assess the extent to which the barriers to correcting others, as identified in literature across disciplines, apply to correcting misinformation on social media. We also group the barriers into factors and explore demographic differences amongst th…
The dark side of news community forums: Opinion manipulation trolls
Purpose The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community foru…
Source Language Adaptation Approaches for Resource-Poor Machine Translation
Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language. Thus, we propose three novel, language-independent approaches to source language adaptation for resource-poor statistical machine translation. Specifically, we build improved statistical machine translation models from a resource-poor language POOR into a target language TGT by adapting and us…
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding corr…
The dark side of news community forums: Opinion manipulation trolls
Purpose The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community foru…
Similar Languages, Varieties, and Dialects: A Computational Perspective
Why do we not stand up to misinformation? Factors influencing the likelihood of challenging misinformation on social media and the role of demographics
This study investigates the barriers to challenging others who post misinformation on social media platforms. We conducted a survey amongst U.K. Facebook users (143 (57.2 %) women, 104 (41.6 %) men) to assess the extent to which the barriers to correcting others, as identified in literature across disciplines, apply to correcting misinformation on social media. We also group the barriers into factors and explore demographic differences amongst th…
On the need to develop nuanced measures assessing attitudes towards AI and AI literacy in representative large-scale samples
Since the launch of the generative AI, including tools such as ChatGPT, the impact of artificial intelligence (AI) on societies has been extensively discussed. How far reaching will the impact of AI be? On the positive side, AI might help find solutions for the world’s problems, such as tackling issues around climate change or the development of new psychopharmaceuticals to enhance global mental health. However, AI, as a technology that leads to …
Challenging others when posting misinformation: A UK vs. Arab cross-cultural comparison on the perception of negative consequences and injunctive norms
This study investigates the factors influencing the willingness to challenge misinformation on social media across two cultural contexts, the United Kingdom (UK) and Arab countries. A total of 462 participants completed an online survey (250 UK, 212 Arabs). The analysis revealed that three types of negative consequences (relationship cost, negative impact on the person being challenged, futility) and also injunctive norms influence the willingnes…
Computer Science (6 works) · Psychology (4 works) · Artificial Intelligence (3 works) · Misinformation and Its Impacts (3 works) · Political science (3 works) · Topic Modeling (3 works) · Internet privacy (2 works) · Linguistics (2 works) · Machine translation (2 works) · Misinformation (2 works)